Behavioral and Brain Sciences, Volume 33, Issue 2-3, April June 2010

An International journal of current research and theory with open peer commentary Volume 33 | Issue 2/3 | April/June 2010 | ISSN: 0140-525X

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Editorial Board Atypical Neurodevelopment Simon Baron-Cohen/Cambridge U. Behavioral Neurogenetics Wim E. Crusio/CNRS UMR Cognition and Artificial Intelligence Zenon Pylyshyn/Rutgers U. Cognitive and Decision Sciences Nick Chater/University College London Cognitive Development Annette Karmiloff-Smith/Birbeck College Cognitive Neuroscience Moshe Bar/Harvard Medical School Computational Neuroscience Nestor A. Schmajuk/Duke U. Evolution of Brain and Cognition Dean Falk/Florida State U. Evolution of Cognition Celia Heyes/Oxford U. Experimental Analysis of Behavior A. Charles Catania/U. Maryland, Baltimore County Language and Language Disorders Max Coltheart/Macquarie U. Linguistics Robert A. Freidin/Princeton U. Perception Bruce Bridgeman/U. of California, Santa Cruz Philosophy of Science Massimo Piattelli-Palmarini/U. of Arizona Primate Cognition Laurie R. Santos/Yale U. Social Cognition Mahzarin R. Banaji/Harvard U. Social Cognitive Neuroscience Rebecca Saxe/MIT Vision, Language and Computation Shimon Edelman/Cornell U.

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Contents

Volume 33:2/3

April/June 2010

Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Open Peer Commentary Astuti, R. & Bloch, M. Why a theory of human nature cannot be based on the distinction between universality and variability: Lessons from anthropology Baumard, N. & Sperber, D. Weird people, yes, but also weird experiments Bennis, W. M. & Medin, D. L. Weirdness is in the eye of the beholder Boesch, C. Away from ethnocentrism and anthropocentrism: Towards a scientific understanding of “what makes us human” Ceci, S. J., Kahan, D. M. & Braman, D. The WEIRD are even weirder than you think: Diversifying contexts is as important as diversifying samples Chiao, J. Y. & Cheon, B. K. The weirdest brains in the world Danks, D. & Rose, D. Diversity in representations; uniformity in learning Fernald, A. Getting beyond the “convenience sample” in research on early cognitive development Fessler, D. M. T. Cultural congruence between investigators and participants masks the unknown unknowns: Shame research as an example Ga¨chter, S. (Dis)advantages of student subjects: What is your research question? Gaertner, L., Sedikides, C., Cai, H. & Brown, J. D. It’s not WEIRD, it’s WRONG: When Researchers Overlook uNderlying Genotypes, they will not detect universal processes Gosling, S. D., Sandy, C. J., John, O. P. & Potter, J. Wired but not WEIRD: The promise of the Internet in reaching more diverse samples Karasik, L. B., Adolph, K. E., Tamis-LeMonda, C. S. & Bornstein, M. H. WEIRD walking: Cross-cultural research on motor development

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Kesebir, S., Oishi, S. & Spellman, B. A. The socio-ecological approach turns variance among populations from a liability to an asset Khemlani, S. S., Lee, N. Y. L. & Bucciarelli, M. Determinants of cognitive variability Konecˇni, V. J. Responsible behavioral science generalizations and applications require much more than non-WEIRD samples Lancy, D. F. When nurture becomes nature: Ethnocentrism in studies of human development Leavens, D. A., Bard, K. A. & Hopkins, W. D. BIZARRE chimpanzees do not represent “the chimpanzee” Machery, E. Explaining why experimental behavior varies across cultures: A missing step in “The weirdest people in the world?” Majid, A. & Levinson, S. C. WEIRD languages have misled us, too Maryanski, A. WEIRD societies may be more compatible with human nature Meadon, M. & Spurrett, D. It’s not just the subjects – there are too many WEIRD researchers Panchanathan, K., Frankenhuis, W. E. & Barrett, H. C. Development: Evolutionary ecology’s midwife Rai, T. S. & Fiske, A. ODD (observation- and description-deprived) psychological research Rochat, P. What is really wrong with a priori claims of universality? Sampling, validity, process level, and the irresistible drive to reduce Rozin, P. The weirdest people in the world are a harbinger of the future of the world Shweder, R. A. Donald Campbell’s doubt: Cultural difference or failure of communication? Stich, S. Philosophy and WEIRD intuition

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Authors’ Response Henrich, J., Heine, S. J. & Norenzayan, A. Beyond WEIRD: Towards a broad-based behavioral science

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Cramer, A. O. J., Waldorp, L. J., van der Maas, H. L. J. & Borsboom, D. Comorbidity: A network perspective 137 Open Peer Commentary Belzung, C., Billette de Villemeur, E., Lemoine, M. & Camus, V. Latent variables and the network perspective

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Bornstein, R. F. The rocky road from Axis I to Axis II: Extending the network model of diagnostic comorbidity to personality pathology

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Cervone, D. Aligning psychological assessment with psychological science Danks, D., Fancsali, S., Glymour, C. & Scheines, R. Comorbid science? Davis, O. S. P. & Plomin, R. Visualizing genetic similarity at the symptom level: The example of learning disabilities Fleeson, W., Furr, R. M. & Arnold, E. M. An agenda for symptom-based research Haig, B. D. & Vertue, F. M. Extending the network perspective on comorbidity Haslam, N. Symptom networks and psychiatric categories Hood, S. B. & Lovett, B. J. Network models of psychopathology and comorbidity: Philosophical and pragmatic considerations Humphry, S. M. & McGrane, J. A. Is there a contradiction between the network and latent variable perspectives? Hyland, M. E. Network origins of anxiety and depression Johnson, W. & Penke, L. The network perspective will help, but is comorbidity the question? Krueger, R. F., DeYoung, C. G. & Markon, K. E. Toward scientifically useful quantitative models of psychopathology: The importance of a comparative approach Markus, K. A. Questions about networks, measurement, and causation McFarland, D. J. & Malta, L. S. Symptoms as latent variables Molenaar, P. C. M. Latent variable models are network models

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Ross, D. Some mental disorders are based on networks, others on latent variables Rothenberger, A., Banaschewski, T., Becker, A. & Roessner, V. Comorbidity: The case of developmental psychopathology Rubinsten, O. & Henik, A. Comorbidity: Cognition and biology count! Staniloiu, A. & Markowitsch, H. J. Looking at comorbidity through the glasses of neuroscientific memory research: A brain-network perspective Tzur-Bitan, D., Meiran, N. & Shahar, G. The importance of modeling comorbidity using an intra-individual, time-series approach van der Sluis, S., Kan, K.-J. & Dolan, C. V. Consequences of a network view for genetic association studies van Geert, P. L. C. & Steenbeek, H. W. Networks as complex dynamic systems: Applications to clinical and developmental psychology and psychopathology Wass, S. & Karmiloff-Smith, A. The missing developmental dimension in the network perspective Yordanova, J., Kolev, V., Kirov, R. & Rothenberger, A. Comorbidity in the context of neural network properties Zachar, P. The abandonment of latent variables: Philosophical considerations

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Authors’ Response Cramer, A. O. J., Waldorp, L. J., van der Maas, H. L. J. & Borsboom, D. Complex realities require complex theories: Refining and extending the network approach to mental disorders

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Machery, E. Pre´cis of Doing without Concepts Open Peer Commentary Blanchard, T. Default knowledge, time pressure, and the theory-theory of concepts Couchman, J. J., Boomer, J., Coutinho, M. V. C. & Smith, J. D. Carving nature at its joints using a knife called concepts Danks, D. Not different kinds, just special cases Dove, G. An additional heterogeneity hypothesis Edwards, K. Unity amidst heterogeneity in theories of concepts Gonnerman, C. & Weinberg, J. M. Two uneliminated uses for “concepts”: Hybrids and guides for inquiry Hampton, J. A. Concept talk cannot be avoided Harnard, S. Eliminating the “concept” concept Hayes, B. K. & Kearney, L. Defending the concept of “concepts” Jacobson, A. J. The faux, fake, forged, false, fabricated, and phony: Problems for the independence of similarity-based theories of concepts

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Keil, F. Hybrid vigor and conceptual structure Khemlani, S. S. & Goodwin, G. The function and representation of concepts Lalumera, E. Concepts are a functional kind Lombrozo, T. From conceptual representations to explanatory relations Margolis, E. & Laurence, S. Concepts and theoretical unification Markman, A. B. Where are nature’s joints? Finding the mechanisms underlying categorization Rey, G. Concepts versus conceptions (again) Samuels, R. & Ferreira, M. Why don’t concepts constitute a natural kind? Scarantino, A. Evidence of coordination as a cure for concept eliminativism Schneider, S. Conceptual atomism rethought Strohminger, N. & Moore, B. W. Banishing the thought Virtel, J. & Piccinini, G. Are prototypes and exemplars used in distinct cognitive processes?

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Vlach, H. A., Krogh, L., Thom, E. E. & Sandhofer, C. M. Doing with development: Moving toward a complete theory of concepts Weiskopf, D. A. The theoretical indispensability of concepts Yermolayeva, Y. & Rakison, D. H. Developing without concepts

Zaki, S. & Cruz, J. Parsimony and the triple-system model of concepts

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Author’s Response Machery, E. The heterogeneity of knowledge representation and the elimination of concept

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BEHAVIORAL AND BRAIN SCIENCES (2010) 33, 61 –135 doi:10.1017/S0140525X0999152X

The weirdest people in the world? Joseph Henrich Department of Psychology and Department of Economics, University of British Columbia, Vancouver V6T 1Z4, Canada [email protected] http://www.psych.ubc.ca/henrich/home.html

Steven J. Heine Department of Psychology, University of British Columbia, Vancouver V6T 1Z4, Canada [email protected]

Ara Norenzayan Department of Psychology, University of British Columbia, Vancouver V6T 1Z4, Canada [email protected]

Abstract: Behavioral scientists routinely publish broad claims about human psychology and behavior in the world’s top journals based on samples drawn entirely from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. Researchers – often implicitly – assume that either there is little variation across human populations, or that these “standard subjects” are as representative of the species as any other population. Are these assumptions justified? Here, our review of the comparative database from across the behavioral sciences suggests both that there is substantial variability in experimental results across populations and that WEIRD subjects are particularly unusual compared with the rest of the species – frequent outliers. The domains reviewed include visual perception, fairness, cooperation, spatial reasoning, categorization and inferential induction, moral reasoning, reasoning styles, self-concepts and related motivations, and the heritability of IQ. The findings suggest that members of WEIRD societies, including young children, are among the least representative populations one could find for generalizing about humans. Many of these findings involve domains that are associated with fundamental aspects of psychology, motivation, and behavior – hence, there are no obvious a priori grounds for claiming that a particular behavioral phenomenon is universal based on sampling from a single subpopulation. Overall, these empirical patterns suggests that we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity. We close by proposing ways to structurally re-organize the behavioral sciences to best tackle these challenges. Keywords: behavioral economics; cross-cultural research; cultural psychology; culture; evolutionary psychology; experiments; external validity; generalizability; human universals; population variability

1. Introduction In the tropical forests of New Guinea, the Etoro believe that for a boy to achieve manhood he must ingest the semen of his elders. This is accomplished through ritualized rites of passage that require young male initiates to fellate a senior member (Herdt 1984/1993; Kelley 1980). In contrast, the nearby Kaluli maintain that male initiation is only properly done by ritually delivering the semen through the initiate’s anus, not his mouth. The Etoro revile these Kaluli practices, finding them disgusting. To become a man in these societies, and eventually take a wife, every boy undergoes these initiations. Such boy-inseminating practices, which are enmeshed in rich systems of meaning and imbued with local cultural values, were not uncommon among the traditional societies of Melanesia and Aboriginal Australia (Herdt 1984/1993), as well as in Ancient Greece and Tokugawa Japan. Such in-depth studies of seemingly “exotic” societies, historically the province of anthropology, are crucial for understanding human behavioral and psychological variation. However, this target article is not about these peoples. It is about a truly unusual group: people from # Cambridge University Press 2010

0140-525X/10 $40.00

Western, Educated, Industrialized, Rich, and Democratic (WEIRD)1 societies. In particular, it is about the Western, and more specifically American, undergraduates who form the bulk of the database in the experimental branches of psychology, cognitive science, and economics, as well as allied fields (hereafter collectively labeled the “behavioral sciences”). Given that scientific knowledge about human psychology is largely based on findings from this subpopulation, we ask just how representative are these typical subjects in light of the available comparative database. How justified are researchers in assuming a species-level generality for their findings? Here, we review the evidence regarding how WEIRD people compare with other populations. We pursued this question by constructing an empirical review of studies involving large-scale comparative experimentation on important psychological or behavioral variables. Although such larger-scale studies are highly informative, they are rather rare, especially when compared to the frequency of species-generalizing claims. When such comparative projects were absent, we relied on large assemblies of studies comparing two or three populations, and, when available, on meta-analyses. 61

Henrich et al.: The weirdest people in the world? Of course, researchers do not implicitly assume psychological or motivational universality with everything they study. The present review does not address those phenomena assessed by individual difference measures for which the guiding assumption is variability among populations. Phenomena such as personal values, emotional expressiveness, and personality traits are expected a priori to vary across individuals, and by extension, societies. Indeed, the goal of much research on these topics is to identify the ways that people and societies differ from one another. For example, a number of large projects have sought to map out the world on dimensions such as values (Hofstede 2001; Inglehart et al. 1998; Schwartz & Bilsky 1990), personality traits (e.g., McCrae et al. 2005; Schmitt et al. 2007), and levels of happiness, (e.g., Diener et al. 1995). Similarly, we avoid the vast psychopathology literature, which finds much evidence for both variability and universality in psychological pathologies (Kleinman 1988; Tseng 2001), because this work focuses on individual-level (and unusual) variations in psychological functioning. Instead, we restrict our exploration to

JOSEPH HENRICH holds the Canada Research Chair in Culture, Cognition, and Evolution at the University of British Columbia, where he is appointed Professor in both Economics and Psychology. His theoretical work focuses on how natural selection has shaped human learning and how this in turn influences cultural evolution, and culture-gene coevolution. Methodologically, his research synthesizes experimental and analytical tools drawn from behavioural economics and psychology with in-depth quantitative ethnography, and he has performed long-term fieldwork in the Peruvian Amazon, rural Chile, and in Fiji. Trained in anthropology, Dr. Henrich’s work has been published in the top journals in biology, anthropology, and economics. In 2004 he was awarded the Presidential Early Career Award, the highest award bestowed by the United States upon scientists early in their careers. In 2007 he co-authored Why Humans Cooperate. In 2009 the Human Behavior and Evolution Society awarded him their Early Career Award for Distinguished Scientific Contributions. ARA NORENZAYAN is an Associate Professor of Psychology at the University of British Columbia, Vancouver. He received his Ph.D. from the University of Michigan in 1999, was a postdoctoral fellow at the Ecole Polytechnique, Paris, and served on the faculty of the University of Illinois, Urbana-Champaign before his appointment at UBC. His most recent work addresses the evolution of religious beliefs and behaviors. STEVEN J. HEINE is Professor of Psychology and Distinguished University Scholar at the University of British Columbia. Much of his work has focused on how culture shapes people’s self-concepts, particularly their motivations for self-esteem. Dr. Heine has received the Early Career Award from the International Society of Self and Identity and the Distinguished Scientist Early Career Award for Social Psychology from the American Psychological Association. He is the author of a textbook entitled Cultural Psychology, published in 2008.

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those domains which have largely been assumed, at least until recently, to be de facto psychological universals. Finally, we also do not address societal-level behavioral universals, or claims thereof, related to phenomena such as dancing, fire making, cooking, kinship systems, body adornment, play, trade, and grammar, for two reasons. First, at this surface level alone, such phenomena do not make specific claims about universal underlying psychological or motivational processes. Second, systematic, quantitative, comparative data based on individual-level measures are typically lacking for these domains. Our examination of the representativeness of WEIRD subjects is necessarily restricted to the rather limited database currently available. We have organized our presentation into a series of telescoping contrasts showing, at each level of contrast, how WEIRD people measure up relative to the available reference populations. Our first contrast compares people from modern industrialized societies with those from small-scale societies. Our second telescoping stage contrasts people from Western societies with those from non-Western industrialized societies. Next, we contrast Americans with people from other Western societies. Finally, we contrast universityeducated Americans with non – university-educated Americans, or university students with non-student adults, depending on the available data. At each level we discuss behavioral and psychological phenomena for which there are available comparative data, and we assess how WEIRD people compare with other samples. We emphasize that our presentation of telescoping contrasts is only a rhetorical approach guided by the nature of the available data. It should not be taken as capturing any unidimensional continuum, or suggesting any single theoretical explanation for the variation. Throughout this article we take no position regarding the substantive origins of the observed differences between populations. While many of the differences are probably cultural in nature in that they were socially transmitted (Boyd & Richerson 1985; Nisbett et al. 2001), other differences are likely environmental and represent some form of non-cultural phenotypic plasticity, which may be developmental or facultative, as well as either adaptive or maladaptive (Gangestad et al. 2006; Tooby & Cosmides 1992). Other population differences could arise from genetic variation, as observed for lactose processing (Beja-Pereira et al. 2003). Regardless of the reasons underlying these population differences, our concern is whether researchers can reasonably generalize from WEIRD samples to humanity at large. Many radical versions of interpretivism and cultural relativity deny any shared commonalities in human psychologies across populations (e.g., Gergen 1973; see critique and discussion in Slingerland 2008, Ch. 2). To the contrary, we expect humans from all societies to share, and probably share substantially, basic aspects of cognition, motivation, and behavior. As researchers who see great value in applying evolutionary thinking to psychology and behavior, we have little doubt that if a full accounting were taken across all domains among peoples past and present, the number of similarities would indeed be large, as much ethnographic work suggests (e.g., Brown 1991) – ultimately, of course, this is an empirical question. Thus, our thesis is not that humans share few basic psychological properties or processes; rather, we question our current ability to distinguish these reliably developing

Henrich et al.: The weirdest people in the world? aspects of human psychology from more developmentally, culturally, or environmentally contingent aspects of our psychology given the disproportionate reliance on WEIRD subjects. Our aim here, then, is to inspire efforts to place knowledge of such universal features of psychology on a firmer footing by empirically addressing, rather than a priori dismissing or ignoring, questions of population variability.

2. Background Before commencing with our telescoping contrasts, we first discuss two observations regarding the existing literature: (1) The database in the behavioral sciences is drawn from an extremely narrow slice of human diversity; and (2) behavioral scientists routinely assume, at least implicitly, that their findings from this narrow slice generalize to the species.

2.1. The behavioral sciences database is narrow

Who are the people studied in behavioral science research? A recent analysis of the top journals in six subdisciplines of psychology from 2003 to 2007 revealed that 68% of subjects came from the United States, and a full 96% of subjects were from Western industrialized countries, specifically those in North America and Europe, as well as Australia and Israel (Arnett 2008). The make-up of these samples appears to largely reflect the country of residence of the authors, as 73% of first authors were at American universities, and 99% were at universities in Western countries. This means that 96% of psychological samples come from countries with only 12% of the world’s population. Even within the West, however, the typical sampling method for experimental studies is far from representative. In the Journal of Personality and Social Psychology, the premier journal in social psychology – the subdiscipline of psychology that should (arguably) be the most attentive to questions about the subjects’ backgrounds – 67% of the American samples (and 80% of the samples from other countries) were composed solely of undergraduates in psychology courses (Arnett 2008). In other words, a randomly selected American undergraduate is more than 4,000 times more likely to be a research participant than is a randomly selected person from outside of the West. Furthermore, this tendency to rely on undergraduate samples has not decreased over time (Peterson 2001; Wintre et al. 2001). Such studies are therefore sampling from a rather limited subpopulation within each country (see Rozin 2001). It is possible that the dominance of American authors in psychology publications just reflects that American universities have the resources to attract the best international researchers, and that similar tendencies exist in other fields. However, psychology is a distinct outlier here: 70% of all psychology citations come from the United States – a larger percentage than any of the other 19 sciences that were compared in one extensive international survey (see May 1997). In chemistry, by contrast, the percentage of citations that come from the United States is only 37%. It seems problematic that the discipline

in which there are the strongest theoretical reasons to anticipate population-level variation is precisely the discipline in which the American bias for research is most extreme. Beyond psychology and cognitive science, the subject pools of experimental economics and decision science are not much more diverse – still largely dominated by Westerners, and specifically Western undergraduates. However, to give credit where it is due, the nascent field of experimental economics has begun taking steps to address the problem of narrow samples.2 In sum, the available database does not reflect the full breadth of human diversity. Rather, we have largely been studying the nature of WEIRD people, a certainly narrow and potentially peculiar subpopulation.

2.2. Researchers often assume their findings are universal

Sampling from a thin slice of humanity would be less problematic if researchers confined their interpretations to the populations from which they sampled. However, despite their narrow samples, behavioral scientists often are interested in drawing inferences about the human mind and human behavior. This inferential step is rarely challenged or defended – with important exceptions (e.g., Medin & Atran 2004; Rozin 2001; Triandis 1994; Witkin & Berry 1975) – despite the lack of any general effort to assess how well results from WEIRD samples generalize to the species. This lack of epistemic vigilance underscores the prevalent, though implicit, assumption that the findings one derives from a particular sample will generalize broadly; one adult human sample is pretty much the same as the next. Leading scientific journals and university textbooks routinely publish research findings claiming to generalize to “humans” or “people” based on research done entirely with WEIRD undergraduates. In top journals such as Nature and Science, researchers frequently extend their findings from undergraduates to the species – often declaring this generalization in their titles. These contributions typically lack even a cautionary footnote about these inferential extensions. In psychology, much of this generalization is implicit. A typical article does not claim to be discussing “humans” but will rather simply describe a decision bias, psychological process, set of correlations, and so on, without addressing issues of generalizability, although findings are often linked to “people.” Commonly, there is no demographic information about the participants, aside from their age and gender. In recent years there is a trend to qualify some findings with disclaimers such as “at least within Western culture,” though there remains a robust tendency to generalize to the species. Arnett (2008) notes that psychologists would surely bristle if journals were renamed to more accurately reflect the nature of their samples (e.g., Journal of Personality and Social Psychology of American Undergraduate Psychology Students). They would bristle, presumably, because they believe that their findings generalize much beyond this sample. Of course, there are important exceptions to this general tendency, as some researchers have assembled a broad database to provide evidence for universality (Buss BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? 1989; Daly & Wilson 1988; Ekman 1999b; Elfenbein & Ambady 2002; Kenrick & Keefe 1992a; Tracy & Matsumoto 2008). When is it safe to generalize from a narrow sample to the species? First, if one had good empirical reasons to believe that little variability existed across diverse populations in a particular domain, it would be reasonable to tentatively infer universal processes from a single subpopulation. Second, one could make an argument that as long as one’s samples were drawn from near the center of the human distribution, then it would not be overly problematic to generalize across the distribution more broadly – at least the inferred pattern would be in the vicinity of the central tendency of our species. In the following, with these assumptions in mind, we review the evidence for the representativeness of findings from WEIRD people. 3. Contrast 1: Industrialized societies versus small-scale societies Our theoretical perspective, which is informed by evolutionary thinking, leads us to suspect that many aspects of people’s psychological repertoire are universal. However, the current empirical foundations for our suspicions are rather weak because the database of comparative studies that include small-scale societies is scant, despite the obvious importance of such societies in understanding both the evolutionary history of our species and the potential impact of diverse environments on our psychology. Here we first discuss the evidence for differences between populations drawn from industrialized and small-scale societies in some seemingly basic psychological domains, and follow this with research indicating universal patterns across this divide. 3.1. Visual perception

Many readers may suspect that tasks involving “low-level” or “basic” cognitive processes such as vision will not vary much across the human spectrum (Fodor 1983). However, in the 1960s an interdisciplinary team of anthropologists and psychologists systematically gathered data on the susceptibility of both children and adults from a wide range of human societies to five “standard illusions” (Segall et al. 1966). Here we highlight the comparative findings on the famed Mu¨ller-Lyer illusion, because of this illusion’s importance in textbooks, and its prominent role as Fodor’s indisputable example of “cognitive impenetrability” in debates about the modularity of cognition (McCauley & Henrich 2006). Note, however, that population-level variability in illusion susceptibility is not limited to the Mu¨ller-Lyer illusion; it was also found for the Sander-Parallelogram and both Horizontal-Vertical illusions. Segall et al. (1966) manipulated the length of the two lines in the Mu¨ller-Lyer illusion (Fig. 1) and estimated the magnitude of the illusion by determining the approximate point at which the two lines were perceived as being of the same length. Figure 2 shows the results from 16 societies, including 14 small-scale societies. The vertical axis gives the “point of subjective equality” (PSE), which measures the extent to which segment “a” must be 64

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Figure 1. The Mu¨ller-Lyer illusion. The lines labeled “a” and “b” are the same length. Many subjects perceive line “b” as longer than line “a”.

longer than segment “b” before the two segments are judged equal in length. PSE measures the strength of the illusion. The results show substantial differences among populations, with American undergraduates anchoring the extreme end of the distribution, followed by the South African-European sample from Johannesburg. On average, the undergraduates required that line “a” be about a fifth longer than line “b” before the two segments were perceived as equal. At the other end, the San foragers of the Kalahari were unaffected by the so-called illusion (it is not an illusion for them). While the San’s PSE value cannot be distinguished from zero, the American undergraduates’ PSE value is significantly different from all the other societies studied. As discussed by Segall et al., these findings suggest that visual exposure during ontogeny to factors such as the “carpentered corners” of modern environments may favor certain optical calibrations and visual habits that create and perpetuate this illusion. That is, the visual system ontogenetically adapts to the presence of recurrent features in the local visual environment. Because elements such as carpentered corners are products of particular cultural evolutionary trajectories, and were not part of most environments for most of human history, the Mu¨llerLyer illusion is a kind of culturally evolved by-product (Henrich 2008). These findings highlight three important considerations. First, this work suggests that even a process as apparently basic as visual perception can show substantial variation across populations. If visual perception can vary, what kind of psychological processes can we be

Figure 2. Mu¨ller-Lyer results for Segall et al.’s (1966) crosscultural project. PSE (point of subjective equality) is the percentage that segment a must be longer than b before subjects perceived the segments as equal in length. Children were sampled in the 5-to-11 age range.

Henrich et al.: The weirdest people in the world? sure will not vary? It is not merely that the strength of the illusory effect varies across populations – the effect cannot be detected in two populations. Second, both American undergraduates and children are at the extreme end of the distribution, showing significant differences from all other populations studied; whereas, many of the other populations cannot be distinguished from one another. Since children already show large population-level differences, it is not obvious that developmental work can substitute for research across diverse human populations. Children likely have different developmental trajectories in different societies. Finally, this provides an example of how population-level variation can be useful for illuminating the nature of a psychological process, which would not be as evident in the absence of comparative work. 3.2. Fairness and cooperation in economic decision-making

By the mid-1990s, researchers were arguing that a set of robust experimental findings from behavioral economics were evidence for a set of evolved universal motivations (Fehr & Ga¨chter 1998; Hoffman et al. 1998). Foremost among these experiments, the Ultimatum Game provides a pair of anonymous subjects with a sum of real money for a one-shot interaction. One of the pair – the proposer – can offer a portion of this sum to the second subject, the responder. Responders must decide whether to accept or reject the offer. If a responder accepts, she gets the amount of the offer and the proposer takes the remainder; if she rejects, both players get zero. If subjects are motivated purely by self-interest, responders should always accept any positive offer; knowing this, a selfinterested proposer should offer the smallest non-zero amount. Among subjects from industrialized populations – mostly undergraduates from the United States, Europe, and Asia – proposers typically offer an amount between 40% and 50% of the total, with a modal offer of 50% (Camerer 2003). Offers below about 30% are often rejected. With this seemingly robust empirical finding in their sights, Nowak et al. (2000) constructed an evolutionary analysis of the Ultimatum Game. When they modeled the Ultimatum Game exactly as played, they did not get results matching the undergraduate findings. However, if they added reputational information, such that players could know what their partners did with others on previous rounds of play, the analysis predicted offers and rejections in the range of typical undergraduate responses. They concluded that the Ultimatum Game reveals humans’ species-specific evolved capacity for fair and punishing behavior in situations with substantial reputational influence. But, since the Ultimatum Game is typically played one-shot without reputational information, Nowak et al. argued that people make fair offers and reject unfair offers because their motivations evolved in a world where such interactions were not fitness relevant – thus, we are not evolved to fully incorporate the possibility of non-reputational action in our decision-making, at least in such artificial experimental contexts. Recent comparative work has dramatically altered this initial picture. Two unified projects (which we call Phase

1 and Phase 2) have deployed the Ultimatum Game and other related experimental tools across thousands of subjects randomly sampled from 23 small-scale human societies, including foragers, horticulturalists, pastoralists, and subsistence farmers, drawn from Africa, Amazonia, Oceania, Siberia, and New Guinea (Henrich et al. 2005; 2006; 2010). Three different experimental measures show that people in industrialized societies consistently occupy the extreme end of the human distribution. Notably, people in some of the smallest-scale societies, where real life is principally face-to-face, behaved in a manner reminiscent of Nowak et al.’s analysis before they added the reputational information. That is, these populations made low offers and did not reject. To concisely present these diverse empirical findings, we show results only from the Ultimatum and Dictator Games in Phase II. The Dictator Game is the same as the Ultimatum Game except that the second player cannot reject the offer. If subjects are motivated purely by self-interest, they would offer zero in the Dictator Game. Thus, Dictator Game offers yield a measure of “fairness” (equal divisions) among two anonymous people. By contrast, Ultimatum Game offers yield a measure of fairness combined with an assessment of the likelihood of rejection (punishment). Rejections of offers in the Ultimatum Game provide a measure of people’s willingness to punish unfairness. Using aggregate measures, Figure 3 shows that the behavior of the U.S. adult (non-student) sample occupies the extreme end of the distribution in each case. For Dictator Game offers, Figure 3A shows that the U.S. sample has the highest mean offer, followed by the Sanquianga from Colombia, who are renowned for their prosociality (Kraul 2008). The U.S. offers are nearly double that of the Hadza, foragers from Tanzania, and the Tsimane, forager-horticulturalists from the Bolivian Amazon. Figure 3B shows that for Ultimatum Game offers, the United States has the second highest mean offer, behind the Sursurunga from Papua New Guinea. On the punishment side in the Ultimatum Game, Figure 3C shows the income-maximizing offers (IMO) for each population, which is a measure of the population’s willingness to punish inequitable offers. IMO is the offer that an income-maximizing proposer would make if he knew the probability of rejection for each of the possible offer amounts. The U.S. sample is tied with the Sursurunga. These two groups have an IMO five times higher than 70% of the other societies. While none of these measures indicates that people from industrialized societies are entirely unique vis-a`-vis other populations, they do show that people from industrialized societies consistently occupy the extreme end of the human distribution. Analyses of these data show that a population’s degree of market integration and its participation in a world religion both independently predict higher offers, and account for much of the variation between populations. Community size positively predicts greater punishment (Henrich et al. 2010). The authors suggest that norms and institutions for exchange in ephemeral interactions culturally coevolved with markets and expanding largerscale sedentary populations. In some cases, at least in their most efficient forms, neither markets nor large populations were feasible before such norms and institutions BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Figure 3. Behavioral measures of fairness and punishment from the Dictator and Ultimatum Games for 15 societies (Phase II). Figures 3A and 3B show mean offers for each society in the Dictator and Ultimatum Games, respectively. Figure 3C gives the income-maximizing offer (IMO) for each society.

emerged. That is, it may be that what behavioral economists have been measuring among undergraduates in such games is a specific set of social norms, culturally evolved for dealing with money and strangers, that have emerged 66

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since the origins of agriculture and the rise of complex societies. In addition to differences in populations’ willingness to reject offers that are too low, the evidence also indicates a

Henrich et al.: The weirdest people in the world? willingness to reject offers that are too high in about half the societies studied. This tendency to reject so-called hyper-fair offers rises as offers increase from 60% to 100% of the stake (Henrich et al. 2006). This phenomenon, which is not observed in typical undergraduate subjects (who essentially never reject offers greater than half), has now emerged among populations in Russia (Bahry & Wilson 2006) and China (Hennig-Schmidt et al. 2008), as well as (to a lesser degree) among non-student adults in Sweden (Wallace et al. 2007), Germany (Guth et al. 2003), and the Netherlands (Bellemare et al. 2008). Attempts to explain away this phenomenon as a consequence of confusion or misunderstanding, have not found support despite substantial efforts. Suppose that Nowak and his coauthors were Tsimane, and that the numerous empirical findings they had on hand were all from Tsimane villages. If this were the case, presumably these researchers would have simulated the Ultimatum Game and found that there was no need to add reputation to their model. This unadorned evolutionary solution would have worked fine until they realized that the Tsimane are not representative of humanity. According to the above data, the Tsimane are about as representative of the species as are Americans, but at the opposite end of the spectrum. If the database of the behavioral sciences consisted entirely of Tsimane subjects, researchers would likely be quite concerned about generalizability. 3.3. Folkbiological reasoning

Recent work in small-scale societies suggests that some of the central conclusions regarding the development and operation of human folkbiological categorization, reasoning, and induction are limited to urban subpopulations of non-experts in industrialized societies. Although much more work needs to be done, it appears that typical subjects (children of WEIRD parents) develop their folkbiological reasoning in a culturally and experientially impoverished environment, by contrast to those of smallscale societies (and of our evolutionary past), distorting both the species-typical pattern of cognitive development and the patterns of reasoning in WEIRD adults. Cognitive scientists using (as subjects) children drawn from U.S. urban centers – often those surrounding universities – have constructed an influential, though actively debated, developmental theory in which folkbiological reasoning emerges from folkpsychological reasoning. Before age 7, urban children reason about biological phenomena by analogy to, and by extension from, humans. Between ages 7 and 10, urban children undergo a conceptual shift to the adult pattern of viewing humans as one animal among many. These conclusions are underpinned by three robust findings from urban children: (1) Inferential projections of properties from humans are stronger than projections from other living kinds; (2) inferences from humans to mammals emerge as stronger than inferences from mammals to humans; and (3) children’s inferences violate their own similarity judgments by, for example, providing stronger inference from humans to bugs than from bugs to bees (Carey 1985; 1995). However, when the folkbiological reasoning of children in rural Native American communities in Wisconsin and

Yukatek Maya communities in Mexico was investigated (Atran et al. 2001; Ross et al. 2003; Waxman & Medin 2007) none of these three empirical patterns emerged. Among the American urban children, the human category appears to be incorporated into folkbiological induction relatively late compared to these other populations. The results indicate that some background knowledge of the relevant species is crucial for the application and induction across a hierarchical taxonomy (Atran et al. 2001). In rural environments, both exposure to and interest in the natural world is commonplace, unavoidable, and an inevitable part of the enculturation process. This suggests that the anthropocentric patterns seen in U.S. urban children result from insufficient cultural input and a lack of exposure to the natural world. The only real animal that most urban children know much about is Homo sapiens, so it is not surprising that this species dominates their inferential patterns. Since such urban environments are highly “unnatural” from the perspective of human evolutionary history, any conclusions drawn from subjects reared in such informationally impoverished environments must remain rather tentative. Indeed, studying the cognitive development of folkbiology in urban children would seem the equivalent of studying “normal” physical growth in malnourished children. This deficiency of input likely underpins the fact that the basic-level folkbiological categories for WEIRD adults are life-form categories (e.g., bird, fish, and mammal), and these are also the first categories learned by WEIRD children – for example, if one says “What’s that?” (pointing at a maple tree), their common answer is “tree.” However, in all small-scale societies studied, the generic species (e.g., maple, crow, trout, and fox) is the basic-level category and the first learned by children (Atran 1993; Berlin 1992). Impoverished interactions with the natural world may also distort assessments of the typicality of natural kinds in categorization. The standard conclusion from American undergraduate samples has been that goodness of example, or typicality, is driven by similarity relations. A robin is a typical bird because this species shares many of the perceptual features that are commonly found in the category BIRD. In the absence of close familiarity with natural kinds, this is the default strategy of American undergraduates, and psychology has assumed it is the universal pattern. However, in samples which interact with the natural world regularly, such as Itza Maya villagers, typicality is based not on similarity but on knowledge of cultural ideals, reflecting the symbolic or material significance of the species in that culture. For the Itza, the wild turkey is a typical bird because of its rich cultural significance, even though it is in no way most similar to other birds. The same pattern holds for similarity effects in inductive reasoning – WEIRD people make strong inferences from computations of similarity, whereas populations with greater familiarity with the natural world, despite their capacity for similarity-based inductions, prefer to make strong inferences from folkbiological knowledge that takes into account ecological context and relationships among species (Atran et al. 2005). In general, research suggests that what people think about can affect how they think (Bang et al. 2007). To the extent that there is population-level variability in the content of folkbiological BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? beliefs, such variability affects cognitive processing in this domain as well. So far we have emphasized differences in folkbiological cognition uncovered by comparative research. This same work has also uncovered reliably developing aspects of human folkbiological cognition that do not vary, such as categorizing plants and animals in a hierarchical taxonomy, or that the generic species level has the strongest inductive potential, despite the fact that this level is not always the basic level across populations, as discussed above. Our goal in emphasizing the differences here is to show (1) how peculiar industrialized (urban, in this case) samples are, given the unprecedented environment they grow up in; and (2) how difficult it is to conclude a priori what aspects will be reliably developing and robust across diverse slices of humanity if research is largely conducted with WEIRD samples. 3.4. Spatial cognition

Human societies vary in their linguistic tools for, and cultural practices associated with, representing and communicating (1) directions in physical space, (2) the color spectrum, and (3) integer amounts. There is some evidence that each of these differences in cultural content may influence some aspects of nonlinguistic cognitive processes (D’Andrade 1995; Gordon 2004; Kay 2005; Levinson 2003; Roberson et al. 2000). Here we focus on spatial cognition, for which the evidence is most provocative. As above, it appears that industrialized societies are at the extreme end of the continuum in spatial cognition. Human populations show differences in how they think about spatial orientation and deal with directions, and these differences may be influenced by linguistically based spatial reference systems. Speakers of English and other Indo-European languages favor the use of an egocentric (relative) system to represent the location of objects – that is, relative to the self (e.g., “the man is on the right side of the flagpole”). In contrast, many if not most languages favor an allocentric frame, which comes in two flavors. Some allocentric languages such as Guugu Yimithirr (an Australian language) and Tzeltal (a Mayan language) favor a geocentric system in which absolute reference is based on cardinal directions (“the man is west of the house”). The other allocentric frame is an object-centered (intrinsic) approach that locates objects in space, relative to some coordinate system anchored to the object (“the man is behind the house”). When languages possess systems for encoding all of these spatial reference frames, they often privilege one at the expense of the others. However, the fact that some languages lack one or more of the reference systems suggests that the accretion of all three systems into most contemporary languages may be a product of longterm cumulative cultural evolution. In data on spatial reference systems from 20 languages drawn from diverse societies – including foragers, horticulturalists, agriculturalists, and industrialized populations – only three languages relied on egocentric frames as their single preferred system of reference. All three were from industrialized populations: Japanese, English, and Dutch (Majid et al. 2004). The presence of, or emphasis on, different reference systems may influence nonlinguistic spatial reasoning 68

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(Levinson 2003). In one study, Dutch and Tzeltal speakers were seated at a table and shown an arrow pointing either to the right (north) or the left (south). They were then rotated 180 degrees to a second table where they saw two arrows: one pointing to the left (north) and the other one pointing to the right (south). Participants were asked which arrow on the second table was like the one they saw before. Consistent with the spatial-marking system of their languages, Dutch speakers chose the relative solution, whereas the Tzeltal speakers chose the absolute solution. Several other comparative experiments testing spatial memory and reasoning are consistent with this pattern, although lively debates about interpretation persist (Levinson et al. 2002; Li & Gleitman 2002). Extending the above exploration, Haun and colleagues (Haun et al. 2006a; 2006b) examined performance on a spatial reasoning task similar to the one described above, using children and adults from different societies and great apes. In the first step, Dutch-speaking adults and 8-year-olds (speakers of an egocentric language) showed the typical egocentric bias, whereas Hai//om-speaking adults and 8-year-olds (a Namibian foraging population who speak an allocentric language) showed a typical allocentric bias. In the second step, 4-year-old German-speaking children, gorillas, orangutans, chimpanzees, and bonobos were tested on a simplified version of the same task. All showed a marked preference for allocentric reasoning. These results suggest that children share with other great apes an innate preference for allocentric spatial reasoning, but that this bias can be overridden by input from language and cultural routines. If one were to work on spatial cognition exclusively with WEIRD subjects (say, using subjects from the United States and Europe), one might conclude that children start off with an allocentric bias but naturally shift to an egocentric bias with maturation. The problem with this conclusion is that it would not apply to many human populations, and it may be the consequence of studying subjects from peculiar cultural environments. The next telescoping contrast highlights some additional evidence suggesting that WEIRD people may even be unusual in their egocentric bias vis-a`-vis most other industrialized populations. 3.5. Other potential differences

We have discussed several lines of data suggesting not only population-level variation, but that industrialized populations are consistently unusual compared to small-scale societies. There are also numerous studies that have found differences between much smaller numbers of samples (usually two samples). In these studies it is impossible to discern who is unusual, the small-scale society or the WEIRD population. For example, one study found that both samples from two different industrialized populations were risk-averse decision makers when facing monetary gambles involving gains (Henrich & McElreath 2002), whereas both samples from small-scale societies were risk-prone. Risk-aversion for monetary gains may be a recent, local phenomenon. Similarly, extensive inter-temporal choice experiments using a panel method of data collection indicates that the Tsimane, an Amazonian population of forager-horticulturalists, discount the future 10 times more steeply than do

Henrich et al.: The weirdest people in the world? WEIRD people (Godoy et al. 2004). In Uganda, a study of individual decision-making among small-scale farmers showed qualitatively different deviations from expected utility maximization than is typically found among undergraduates. For example, rather than the inverse S-shape for probabilities in Prospect Theory, a regular S-shape was found.3 3.6. Similarities between industrialized and small-scale societies

Some larger-scale comparative projects show universal patterns in human psychology. Here we list some noteworthy examples: 1. Some perceptual illusions: We discussed the Mu¨llerLyer illusion above. However, there are illusions, such as the Perspective Drawing Illusion, for which the industrialized populations are not extreme outliers, and for which perception varies little in the populations studied (Segall et al. 1966). 2. Perceiving color: While the number of basic color terms systematically varies across human languages (Regier et al. 2005), the ability to perceive different colors does emerge in small-scale societies (Rivers 1901a),4 although terms and categories do influence color perception at the margins (Kay & Regier 2006). 3. Emotional expression: In studying facial displays of emotions, Ekman and colleagues have shown much evidence for universality in recognition of the “basic” facial expressions of emotions, although this work has included only a small – yet convincing – sampling of small-scale societies (Ekman 1999a; 1999b). There is also evidence for the universality of pride displays (Tracy & Matsumoto 2008; Tracy & Robins 2008). This main effect for emotional recognition across population (58% of variance) is qualified by a smaller effect for cultural specificity of emotional expressions (9% of variance: Elfenbein & Ambady 2002). 4. False belief tasks: Comparative work in China, the United States, Canada, Peru, India, Samoa, and Thailand suggests that the ability to explicitly pass the false belief task emerges in all populations studied (Callaghan et al. 2005; Liu et al. 2008), although the age at which subjects can pass the explicit version of the false belief task varies from 4 to at least 9 (Boesch 2007; Callaghan et al. 2005; Liu et al. 2008), with industrialized populations at the extreme low end. 5. Analog numeracy: There is growing consensus in the literature on numerical thinking that quantity estimation relies on a primitive “analog” number sense that is sensitive to quantity but limited in accuracy. This cognitive ability appears to be independent of counting practices and was shown to operate in similar ways among two Amazonian societies with very limited counting systems (Gordon 2004; Pica et al. 2004), as well as in infants and primates (e.g., Dehaene 1997). 6. Social relationships: Research on the cognitive processes underlying social relationships reveals similar patterns across distinct populations. Fiske (1993) studied people’s tendency to confuse one person with another (e.g., intending to phone your son Bob but accidentally calling your son Fred). Chinese, Korean, Bengali, and Vai (Liberia and Sierra Leone) immigrants tended to confuse people in the same category of social relationship.

Interestingly, the social categories in which the most confusion occurred varied across populations. 7. Psychological essentialism: Research from a variety of societies, including Vezo children in Madagascar (Astuti et al. 2004), children from impoverished neighborhoods in Brazil (Sousa et al. 2002), Menominee in Wisconsin (Waxman et al. 2007), and middle-class children and adults in the United States (Gelman 2003), shows evidence of perceiving living organisms as having an underlying and non-trivial nature that makes them what they are. Psychological essentialism also extends to the understanding of social groups, which may be found in Americans (Gelman 2003), rural Ukranians (Kanovsky 2007), Vezo in Madagascar (Astuti 2001), Mapuche farmers in Chile (Henrich & Henrich, unpublished manuscript), Iraqi Chaldeans and Hmong immigrants in Detroit (Henrich & Henrich 2007), and Mongolian herdsmen (Gil-White 2001). Notably, this evidence is not well suited to examining differences in the degree of psychological essentialism across populations, though it suggests that inter-population variation may be substantial. There are also numerous studies involving dyadic comparisons between a single small-scale society and a Western population (or a pattern of Western results) in which cross-population similarities have been found. Examples are numerous but include the development of an understanding of death (Barrett & Behne 2005), shame (Fessler 2004),5 and cheater detection (Sugiyama et al. 2002). Finding evidence for similarities across two such disparate populations is an important step towards providing evidence for universality (Norenzayan & Heine 2005); however, the case would be considerably stronger if it was found across a larger number of diverse populations.6 3.7. Summary for Contrast 1

Although there are several domains in which the data from small-scale societies appear similar to that from industrialized societies, comparative projects involving visual illusions, social motivations (fairness), folkbiological cognition, and spatial cognition all show industrialized populations as outliers. Given all this, it seems problematic to generalize from industrialized populations to humans more broadly, in the absence of supportive empirical evidence. 4. Contrast 2: Western7 versus non-Western societies For our second contrast, we review evidence comparing Western with non-Western populations. Here we examine four of the most studied domains: social decision making (fairness, cooperation, and punishment), independent versus interdependent self-concepts (and associated motivations), analytic versus holistic reasoning, and moral reasoning. We also briefly return to spatial cognition. 4.1. Anti-social punishment and cooperation

In the previous contrast, we reviewed social decisionmaking experiments showing that industrialized populations occupy the extreme end of the behavioral BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? distribution vis-a`-vis a broad swath of smaller-scale societies. Here we show that even among industrialized populations, Westerners are again clumped at the extreme end of the behavioral distribution. Notably, the behaviors measured in the experiments discussed below are strongly correlated with the strength of formal institutions, norms of civic cooperation, and Gross Domestic Product (GDP) per capita. In 2002, Fehr and Ga¨chter published their classic paper, “Altruistic Punishment in Humans,” in Nature, based on Public Goods Games with and without punishment, conducted with undergraduates at the University of Zurich. The paper demonstrated that adding the possibility of punishment to a cooperative dilemma dramatically altered the outcome, from a gradual slide towards little cooperation (and rampant free-riding), to a steady increase towards stable cooperation. Enough subjects were willing to punish non-cooperators at a cost to themselves to shift the balance from free-riding to cooperation. In stable groups this cooperation-punishment combination dramatically increases long-run gains (Ga¨chter et al. 2008). To examine the generalizability of these results, which many took to be a feature of our species, Herrmann, Thoni, and Ga¨chter conducted systematic comparable experiments among undergraduates from a diverse swath of industrialized populations (Herrmann et al. 2008). In these Public Goods Games, subjects played with the same four partners for 10 rounds and could contribute during each round to a group project. All contributions to the group project were multiplied by 1.6 and distributed equally among all partners. Players could also pay to punish other players by taking money away from them. In addition to finding population-level differences in the subjects’ initial willingness to cooperate, Ga¨chter’s team unearthed in about half of these samples a phenomenon that is not observed beyond a trivial degree among typical undergraduate subjects (see our Fig. 4): Many subjects engaged in anti-social punishment; that is, they paid to reduce the earnings of “overly” cooperative individuals (those who contributed more than the punisher did). The effect of this behavior on levels of cooperation was dramatic, completely compensating for the cooperationinducing effects of punishment in the Zurich experiment. Possibilities for altruistic punishment do not generate high levels of cooperation in these populations. Meanwhile, participants from a number of Western countries, such as the United States, the United Kingdom, and Australia, behaved like the original Zurich students. Thus, it appears that the Zurich sample works well for generalizing to the patterns of other Western samples (as well as the Chinese sample), but such findings cannot be readily extended beyond this. 4.2. Independent and interdependent self-concepts

Much psychological research has explored the nature of people’s self-concepts. Self-concepts are important, as they organize the information that people have about themselves, direct attention to information that is perceived to be relevant, shape motivations, influence how people appraise situations that influence their emotional experiences, and guide their choices of relationship partners. Markus and Kitayama (1991) posited that self70

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Figure 4. Mean punishment expenditures from each sample for a given deviation from the punisher’s contribution to the public good. The deviations of the punished subject’s contribution from the punisher’s contribution are grouped into five intervals, where [-20,-11] indicates that the punished subjects contributed between 11 and 20 less than the punishing subject; [0] indicates that the punished subject contributed exactly the same amount as the punishing subject; and [1,10] ([11,20]) indicates that the punished subject contributed between 1 and 10 (11 and 20) more than the punishing subject. Adapted from Herrmann et al. (2008).

concepts can take on a continuum of forms stretching between two poles, termed independent and interdependent self-views, which relate to the individualism-collectivism construct (Triandis 1989; 1994). Do people conceive of themselves primarily as self-contained individuals, understanding themselves as autonomous agents who consist largely of component parts, such as attitudes, personality traits, and abilities? Or do they conceive of themselves as interpersonal beings intertwined with one another in social webs, with incumbent role-based obligations towards others within those networks? The extent to which people perceive themselves in ways similar to these independent or interdependent poles has significant consequences for a variety of emotions, cognitions, and motivations. Much research has underscored how Westerners have more independent views of self than non-Westerners. For example, research using the Twenty Statements Test (Kuhn & McPartland 1954) reveals that people from Western populations (e.g., Australians, Americans, Canadians, Swedes) are far more likely to understand their selves in terms of internal psychological characteristics, such as their personality traits and attitudes, and are less likely to understand them in terms of roles and relationships, than are people from non-Western populations, such as Native Americans, Cook Islanders, Maasai and Samburu (both African pastoralists), Malaysians, and East Asians (for a review, see Heine 2008). Studies using other measures (Hofstede 1980; Morling & Lamoreaux

Henrich et al.: The weirdest people in the world? 2008; Oyserman et al. 2002; Triandis et al. 1990) provide convergent evidence that Westerners tend to have more independent, and less interdependent, self-concepts than those of other populations. These data converge with much ethnographic observation, in particular Geertz’s (1975, p. 48) claim that the Western self is “a rather peculiar idea within the context of the world’s cultures.” There are numerous psychological patterns associated with self-concepts. For example, people with independent self-concepts are more likely to demonstrate (1) positively biased views of themselves; (2) a heightened valuation of personal choice; and (3) an increased motivation to “stand out” rather than to “fit in.” Each of these represents a significant research enterprise, and we discuss them in turn. 4.2.1. Positive self-views. The most widely endorsed

assumption regarding the self is that people are motivated to view themselves positively. Roger Brown (1986) famously declared this motivation to maintain high selfesteem an “urge so deeply human, we can hardly imagine its absence” (p. 534). The strength of this motivation has been perhaps most clearly documented by assessing the ways that people go about exaggerating their self-views by engaging in self-serving biases, in which people view themselves more positively than objective benchmarks would justify. For example, in one study, 94% of American professors rated themselves as better than the average American professor (Cross 1977). However, meta-analyses reveal that these self-serving biases tend to be more pronounced in Western populations than in non-Western ones (Heine & Hamamura 2007; Mezulis et al. 2004) – for example, Mexicans (Tropp & Wright 2003), Native Americans (Fryberg & Markus 2003), Chileans (Heine & Raineri 2009), and Fijians (Rennie & Dunne 1994) score much lower on various measures of positive self-views than do Westerners (although there are some exceptions to this general pattern; see Harrington & Liu 2002). Indeed, in some cultural contexts, most notably East Asian ones, evidence for self-serving biases tends to be null, or in some cases, shows significant reversals, with East Asians demonstrating selfeffacing biases (Heine & Hamamura 2007). At best, the sharp self-enhancing biases of Westerners are less pronounced in much of the rest of the world, although selfenhancement has long been discussed as if it were a fundamental aspect of human psychology (e.g., Rogers 1951; Tesser 1988).

children were equally motivated by the task if a trusted other made the same choices for them (Iyengar & Lepper 1999). Another two sets of studies found that Indians were slower at making choices, were less likely to make choices consistent with their personal preferences, and were less likely to view their actions as expressions of choice, than were Americans (Savani et al. 2008; in press). Likewise, the extent to which people feel that they have much choice in their lives varies across populations. Surveys conducted at bank branches in Argentina, Brazil, Mexico, the Philippines, Singapore, Taiwan, and the United States found that Americans were more likely to perceive having more choice at their jobs than were subjects from the other countries (Iyengar & DeVoe 2003). Another survey administered in more than 40 countries found, in general, that feelings of free choice in one’s life were considerably higher in Western nations (e.g., Finland, the United States, and Northern Ireland) than in various non-Western nations (e.g., Turkey, Japan, and Belarus: Inglehart et al. 1998). This research reveals that perceptions of choice are experienced less often, and are a lesser concern, among those from non-Western populations. 4.2.3. Motivations to conform. Many studies have

explored whether motivations to conform are similar across populations by employing a standard experimental procedure (Asch 1951; 1952). In these studies, which were initially conducted with Americans, participants first hear a number of confederates making a perceptual judgment that is obviously incorrect, and then participants are given the opportunity to state their own judgment. A majority of American participants were found to go along with the majority’s incorrect judgment at least once. This research sparked much interest, apparently because Westerners typically feel that they are acting on their own independent resolve and are not conforming. A meta-analysis of studies performed in 17 societies (Bond & Smith 1996), including subjects from Oceania, the Middle East, South America, Africa, South America, East Asia, Europe, and the United States, found that motivations for conformity are weaker in Western societies than elsewhere. Other research converges with this conclusion. For example, Kim and Markus (1999) found that Koreans preferred objects that were more common, whereas Americans showed a greater preference for objects that were more unusual. 4.3. Analytic versus holistic reasoning

4.2.2. Personal choice. Psychology has long been fasci-

nated with how people assert agency by making choices (Bandura 1982; Kahneman & Tversky 2000; Schwartz 2004), and has explored the efforts that people go through to ensure that their actions feel freely chosen and that their choices are sensible. However, there is considerable variation across populations in the extent to which people value choice and in the range of behaviors over which they feel that they are making choices. For example, one study found that European-American children preferred working on a task, worked on it longer, and performed better on it, if they had made some superficial choices regarding the task than if others made the same choices for them. In contrast, Asian-American

Variation in favored modes of reasoning has been compared across several populations. Most of the research has contrasted Western (American, Canadian, Western European) with East Asian (Chinese, Japanese, Korean) populations with regard to their relative reliance on what is known as “holistic” versus “analytic” reasoning (Nisbett 2003; Peng & Nisbett 1999). However, growing evidence from other non-Western populations points to a divide between Western nations and most everyone else, including groups as diverse as Arabs, Malaysians, and Russians (see Norenzayan et al. [2007] for a review), as well as subsistence farmers in Africa and South America and sedentary foragers (Norenzayan et al., n.d.; Witkin & Berry 1975), rather than an East-West divide. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? Holistic thought involves an orientation to the context or field as a whole, including attention to relationships between a focal object and the field, and a preference for explaining and predicting events on the basis of such relationships. Analytic thought involves a detachment of objects from contexts, a tendency to focus on objects’ attributes, and a preference for using categorical rules to explain and predict behavior. This distinction between habits of thought rests on a theoretical partition between two reasoning systems. One system is associative, and its computations reflect similarity and contiguity (i.e., whether two stimuli share perceptual resemblances and co-occur in time); the other system relies on abstract, symbolic representational systems, and its computations reflect a rule-based structure (e.g., Neisser 1963; Sloman 1996). Although both cognitive systems are available in all normal adults, different environments, experiences, and cultural routines may encourage reliance on one system at the expense of the other, giving rise to populationlevel differences in the use of these different cognitive strategies to solve identical problems. There is growing evidence that a key factor influencing the prominence of analytic versus holistic cognition is the different self-construals prevalent across populations. First, independent self-construal primes facilitate analytic processing, whereas interdependent primes facilitate holistic processing (Oyserman & Lee 2008). Second, geographic regions with greater prevalence of interdependent selfconstruals show more holistic processing, as can be seen in comparisons of Northern and Southern Italians, Hokkaido and mainland Japanese, and Western and Eastern Europeans (Varnum et al. 2008). Furthermore, the analytic approach is culturally more valued in Western contexts, whereas the holistic approach is more valued in East Asian contexts, leading to normative judgments about cognitive strategies that differ across the respective populations (Buchtel & Norenzayan 2008). Below we highlight some findings from this research showing that, compared to diverse populations of nonWesterners, Westerners (1) attend more to objects than fields; (2) explain behavior in more decontextualized terms; and (3) rely more on rules over similarity relations to classify objects (for further discussion of the cross-cultural evidence, see Nisbett 2003; Norenzayan et al. 2007). 1. Using evidence derived from the Rod & Frame Test and Embedded Figures Test, Witkin and Berry (1975) summarize a wide range of evidence from migratory and sedentary foraging populations (Arctic, Australia, and Africa), sedentary agriculturalists, and industrialized Westerners. Only Westerners and migratory foragers consistently emerged at the field-independent end of the spectrum. Recent work among East Asians (Ji et al. 2004) in industrialized societies using the Rod & Frame Test, the Framed Line Test (Kitayama et al. 2003), and the Embedded Figures Test again shows Westerners at the field-independent end of the spectrum, compared to field-dependent East Asians, Malays, and Russians (Kuhnen et al. 2001). Similarly, Norenzayan et al. (2007) found that Canadians showed less field-dependent processing than did Chinese, who in turn were less field-dependent than were Arabs (also see Zebian & Denny 2001). 2. East Asians’ recall for objects is worse than Americans’ if the background has been switched (Masuda & 72

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Nisbett 2001), indicating that East Asians are attending more to the field. This difference in attention has also been found in saccadic eye-movements as measured with eye-trackers. Americans gaze at focal objects longer than East Asians, who in turn gaze at the background more than Americans (Chua et al. 2005). Furthermore, when performing identical cognitive tasks, East Asians and Westerners show differential brain activation, corresponding to the predicted cultural differences in cognitive processing (Gutchess et al. 2006; Hedden et al. 2008). 3. Several classic studies, initially conducted with Western participants, found that “people” tend to make strong attributions about a person’s disposition, even when there are compelling situational constraints (Jones & Harris 1967; Ross et al. 1977). This tendency to ignore situational information in favor of dispositional information is so commonly observed – among typical subjects – that it was dubbed the “fundamental attribution error” (Ross et al. 1977). However, consistent with much ethnography in non-Western cultures (e.g., Geertz 1975), comparative experimental work demonstrates differences that, while Americans attend to dispositions at the expense of situations (Gilbert & Malone 1995), East Asians are more likely than Americans to infer that behaviors are strongly controlled by the situation (Miyamoto & Kitayama 2002; Morris & Peng 1994; Norenzayan et al. 2002a; Van Boven et al. 1999), particularly when situational information is made salient (Choi & Nisbett 1998).8 Grossmann and Varnum (2010) provides parallel findings with Russians. Likewise, in an investigation of people’s lay beliefs about personality across eight populations, Church et al. (2006) found that people from Western populations (i.e., American and Euro-Australian) strongly endorsed the notion that traits remain stable over time and predict behavior over many situations, whereas people from non-Western populations (i.e., Asian-Australian, Chinese-Malaysian, Filipino, Japanese, Mexican, and Malay) more strongly endorsed contextual beliefs about personality, such as ideas suggesting that traits do not describe a person as well as roles or duties do, and that trait-related behavior changes from situation to situation. These patterns are consistent with earlier work on attributions comparing Euro-Americans with Hindu Indians (see Miller 1984; Shweder & Bourne 1982). Hence, although dispositional inferences can be found outside the West, the fundamental attribution error seems less fundamental elsewhere (Choi et al. 1999). 4. Westerners are also more likely to rely on rules over similarity relations in reasoning and categorization. Chinese subjects were found to be more likely to group together objects which shared a functional (e.g., pencilnotebook) or contextual (e.g., sky-sunshine) relationship, whereas Americans were more likely to group objects together if they belonged to a category defined by a simple rule (e.g., notebook-magazine; Ji et al. 2004). Similarly, work with Russian students (Grossmann, 2010) and Russian small-scale farmers (Luria 1976) showed strong tendencies for participants to group objects according to their practical functions. This appears widespread, as Norenzayan et al. (n.d.) examined classification among the Mapuche and Sangu subsistence farmers in Chile and Tanzania, respectively, and found that their classification resembled the Chinese pattern, although it was exaggerated towards holistic reasoning.

Henrich et al.: The weirdest people in the world?

Figure 5. Relative dominance of rule-based versus family resemblance – based judgments of categories for the same cognitive task. European-American, Asian-American, and East Asian university students were tested by Norenzayan et al. (2002b); the herders, fishermen, and farmers of Turkey’s Black Sea coast were tested by Uskul et al. (2008). Positive scores indicate a relative bias towards rule-based judgments, whereas negative scores indicate a relative bias towards family resemblance – based judgments. It can be seen that EuropeanAmerican students show the most pronounced bias toward rule-based judgments, and they are outliers in terms of absolute deviation from zero. Adapted from Norenzayan et al. (2002b) and Uskul et al. (2008).

5. In a similar vein, research with East Asians found they were more likely to group objects if the objects shared a strong family resemblance, whereas Americans were more likely to group the same objects if they could be assigned to that group on the basis of a deterministic rule (Norenzayan et al. 2002b). When those results are compared with Uskul et al.’s (2008) findings from herding, fishing, and tea-farming communities on the Black Sea coast in Turkey – the two studies used the same stimuli – it is evident that European-Americans are again at the extreme (see our Figure 5). In summary, although analytic and holistic cognitive systems are available to all normal adults, a large body of evidence shows that the habitual use of what are considered “basic” cognitive processes, including those involved in attention, perception, categorization, deductive reasoning, and social inference, varies systematically across populations in predictable ways, highlighting the difference between the West and the rest. Several biases and patterns are not merely differences in strength or tendency, but show reversals of Western patterns. We emphasize, however, that Westerners are not unique in their cognitive styles (Uskul et al. 2008; Witkin & Berry 1975), but they do occupy the extreme end of the distribution. 4.4. Moral reasoning

A central concern in the developmental literature has been the way people acquire the cognitive foundations of moral reasoning. The most influential approach to the development of moral reasoning has been Kohlberg’s (1971; 1976; 1981), in which people’s abilities to reason morally are seen to hinge on cognitive abilities that develop over maturation. Kohlberg proposed that people progressed through the same three levels: (1) Children start out at a pre-conventional level, viewing right and wrong as based on internal standards regarding the physical or hedonistic consequences of actions; (2) then they progress to a

conventional level, where morality is based on external standards, such as that which maintains the social order of their group; and finally (3) some progress further to a post-conventional level, where they no longer rely on external standards for evaluating right and wrong, but instead do so on the basis of abstract ethical principles regarding justice and individual rights – the moral code inherent in most Western constitutions. While all of Kohlberg’s levels are commonly found in WEIRD populations, much subsequent research has revealed scant evidence for post-conventional moral reasoning in other populations. One meta-analysis carried out with data from 27 countries found consistent evidence for post-conventional moral reasoning in all the Western urbanized samples, yet found no evidence for this type of reasoning in small-scale societies (Snarey 1985). Furthermore, it is not just that formal education is necessary to achieve Kohlberg’s post-conventional level. Some highly educated non-Western populations do not show this post-conventional reasoning. At Kuwait University, for example, faculty members scored lower on Kohlberg’s schemes than the typical norms for Western adults, and the elder faculty there scored no higher than the younger ones, contrary to Western patterns (AlShehab 2002; Miller et al. 1990). Research in moral psychology indicates that typical Western subjects rely principally on justice- and harm/ care-based principles in judging morality. However, recent work indicates that non-Western adults and Western religious conservatives rely on a wider range of moral principles than these two dimensions of morality (Baek 2002; Haidt & Graham 2007; Haidt et al. 1993; e.g., Miller & Bersoff 1992). Shweder et al. (1997) proposed that in addition to a dominant justice-based morality, which they termed an “ethic of autonomy,” there are two other ethics that are commonly found outside the West: an ethic of community, in which morality derives from the fulfillment of interpersonal obligations that are tied to an individual’s role within the social order, and an ethic of divinity, in which people are perceived to be bearers of something holy or god-like, and have moral obligations to not act in ways that are degrading to or incommensurate with that holiness. The ethic of divinity requires that people treat their bodies as temples, not as playgrounds, and so personal choices that seem to harm nobody else (e.g., about food, sex, and hygiene) are sometimes moralized (for a further elaboration of moral foundations, see Haidt & Graham 2007). In sum, the high-socioeconomic status (SES), secular Western populations that have been the primary target of study thus far, appear unusual in a global context, based on their peculiarly narrow reliance, relative to the rest of humanity, on a single foundation for moral reasoning (based on justice, individual rights, and the avoidance of harm to others; cf. Haidt & Graham 2007). 4.5. Other potential differences

There are many other psychological phenomena in which Western samples differ from non-Western ones; however, at present there are insufficient data in these domains derived from diverse populations to assess where Westerners reside in the human spectrum. For example, compared with Westerners, some non-Westerners (1) have BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? less dynamic social networks, in which people work to avoid negative interactions among their existing networks rather than seeking new relations (Adams 2005); (2) prefer lower to higher arousal-positive affective states (Tsai 2007); (3) are less egocentric when they try to take the perspective of others (Cohen et al. 2007; Wu & Keysar 2007); (4) have weaker motivations for consistency (Kanagawa et al. 2001; Suh 2002); (5) are less prone to “social-loafing” (i.e., reducing efforts on group tasks when individual contributions are not being monitored) (Earley 1993); (6) associate fewer benefits with a person’s physical attractiveness (Anderson et al. 2008); and (7) have more pronounced motivations to avoid negative outcomes relative to their motivations to approach positive outcomes (Elliot et al. 2001; Lee et al. 2000). With reference to the spatial reasoning patterns discussed earlier, emerging evidence suggests that a geocentric bias (i.e., a landscape- or earth-fixed spatial coordinate system) may be much more widespread than previously thought – indeed, it may be the common pattern outside of the West, even among non-Western speakers of languages which make regular use of egocentric linguistic markers. Comparative research contrasting children and adults in Geneva with samples in Indonesia, Nepal, and rural and urban India have found the typical geocentric reasoning pattern in all of these populations, except for the Geneva samples (Dasen et al. 2006). Although many of these population-level differences are pronounced, more research is needed before we can assess whether the geocentric pattern is common across a broader swath of humanity. 4.6. Similarities between Western and non-Western societies

We expect that as more large-scale comparative studies of Western and non-Western populations are conducted, they will reveal substantial similarities in psychological processes. However, given the relative ease of conducting such studies (as compared to working in small-scale societies), there have been few comparative programs that have put universality claims to the test. Here we highlight three examples of larger-scale comparative projects that show broad and important similarities across populations. 1. Mate preferences: First, Buss (1989) compared people from 37 (largely industrialized) populations around the world and found some striking similarities in their mate preferences. In all 37 of the populations, males ranked the physical attractiveness of their mates to be more important than did females; and in 34 of the 37 populations, females ranked the ambition and industriousness of their mates as more important than did males (but for other interpretations, see Eagly & Wood 1999).9 Likewise, Kenrick and Keefe (1992a; 1992b) provide evidence of robust differences in age preferences of mates across populations. Finally, comparative research examining men’s preferred waist-to-hip ratios in potential mates finds that men in both industrialized and developing large-scale populations prefer a waist-to-hip ratio of around 0.7 (Singh 2006; Singh & Luis 1994; Streeter & McBurney 2003; Swami et al. 2007).10 2. Personality structure: Recent efforts have taken personality instruments to university students in 51 different countries (McCrae et al. 2005). In most of these populations, the same five-factor structure emerges that has 74

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previously been found with American samples,11 indicating the universal structure of the Five Factor Model of personality (also see Allik & Mccrae 2004; Yik et al. 2002).12 3. Punishment of free-riding: While in Hermann et al.’s (2008) study (Fig. 4) both initial cooperation and antisocial punishment varied dramatically, the willingness of players to punish low contributors (free-riders) was not different among populations, once age, sex, and other socio-demographic controls are included. 4.7. Summary of Contrast 2

Although robust patterns have emerged among people from industrialized societies, Westerners emerge as unusual – frequent global outliers – on several key dimensions. The experiments reviewed are numerous, arise from different disciplines, use diverse methods, and are often part of systematically comparable data sets created by unified projects. Many of these differences are not merely differences in the magnitude of effects but often show qualitative differences, involving effect reversals or novel phenomena such as allocentric spatial reasoning and antisocial punishment. 5. Contrast 3: Contemporary Americans versus the rest of the West Above we compared WEIRD populations to non-Western populations. However, given the dominance of American research within psychology (see May 1997) and the behavioral sciences, it is important to assess the similarity of American data with that from Westerners more generally. Is it reasonable to generalize from Americans to the rest of the West? Americans are, of course, people too, so they will share many psychological characteristics with other Homo sapiens. At present, we could find no systematic research program to compare Americans with other Westerners, so the evidence presented is assembled from many sources. 5.1. Individualism and related psychological phenomena

Americans stand out relative to other Westerners on phenomena that are associated with independent selfconcepts and individualism. A number of analyses, using a diverse range of methods, reveal that Americans are, on average, the most individualistic people in the world (e.g., Hofstede 1980; Lipset 1996; Morling & Lamoreaux 2008; Oyserman et al. 2002). The observation that the United States is especially individualistic is not new and dates at least as far back as de Toqueville (1835). The unusually individualistic nature of Americans may be caused by, or reflect, an ideology that particularly stresses the importance of freedom and self-sufficiency, as well as various practices in education and childrearing that may help to inculcate this sense of autonomy. American parents, for example, were the only ones in a survey of 100 societies who created a separate room for their baby to sleep (Burton & Whiting 1961; also see Lewis 1995), reflecting that from the time they are born, Americans are raised in an environment that emphasizes their independence (on the unusual nature of American childrearing, see Lancy 2008; Rogoff 2003).13

Henrich et al.: The weirdest people in the world? The extreme individualism of Americans is evident on many demographic and political measures. In American Exceptionalism, sociologist Seymour Martin Lipset (1996) documents a long list of the ways that Americans are unique in the Western world. At the time of Lipset’s surveys, compared with other Western industrialized societies, Americans were found to be the most patriotic, litigious, philanthropic, and populist (they have the most positions for elections and the most frequent elections, although they have among the lowest voter turnout rates). They were also among the most optimistic, and the least class-conscious. They were the most churchgoing in Protestantism, and the most fundamentalist in Christendom, and were more likely than others from Western industrialized countries to see the world in absolute moral terms. In contrast to other large Western industrialized societies, the United States had the highest crime rate, the longest working hours, the highest divorce rate, the highest rate of volunteerism, the highest percentage of citizens with a post-secondary education, the highest productivity rate, the highest GDP, the highest poverty rate, and the highest income-inequality rate; and Americans were the least supportive of various governmental interventions. The United States is the only industrialized society that never had a viable socialist movement; it was the last country to get a national pension plan, unemployment insurance, and accident insurance; and, at the time of writing, remain the only industrialized nation that does not have a general allowance for families or a national health insurance plan. In sum, there is some reason to suspect that Americans might be different from other Westerners, as de Tocqueville noted. Given the centrality of self-concept to so many psychological processes, it follows that the unusual emphasis in America on individualism and independence would be reflected in a wide spectrum of self-related phenomena. For example, self-concepts are implicated when people make choices (e.g., Vohs et al. 2008). While Westerners in general tend to value choices more than non-Westerners do (e.g., Iyengar & DeVoe 2003), Americans value choices more still, and prefer more opportunities, than do Westerners from elsewhere (Savani et al. 2008). For example, in a survey of people from six Western countries, only Americans preferred a choice from 50 different ice cream flavors compared with 10 flavors. Likewise, Americans (and Britons) prefer to have more choices on menus in upscale restaurants than do people from other European countries (Rozin et al. 2006). The array of choices available, and people’s motivation to make such choices, is even more extreme in the United States compared to the rest of the West. Likewise, because cultural differences in analytic and holistic reasoning styles appear to be influenced by whether one views the social world as a collection of discrete individuals or as a set of interconnected relationships (Nisbett 2003), it follows that exceptionally individualistic Americans should be exceptionally analytic as well. One recent study suggests that this might indeed be the case: Americans showed significantly more focused attention in the Framed Line Task than did people from other European countries (Britain and Germany) as well as from Japan (Kitayama et al. 2009). Although more research is needed, Americans may see the world in more analytic terms than the rest of the West.

Terror management theory maintains that because humans possess the conscious awareness that they will someday die, they cope with the associated existential anxiety by making efforts to align themselves with their cultural worldviews (Greenberg et al. 1997). The theory is explicit that the existential problem of death is a human universal, and indeed posits that an awareness of death preceded the evolution of cultural meaning systems in humans (Becker 1973). In support of this argument of universality, the tendency to defend one’s cultural worldview following thoughts about death has been found in every one of the more than a dozen diverse populations studied thus far. However, there is also significant crosspopulation diversity in the magnitude of these effects. A recent meta-analysis of all terror management studies reveals that the effect sizes for cultural worldview defense in the face of thoughts of death are significantly more pronounced among American samples (r ¼ 0.37) than among other Western (r ¼ 0.30) or non-Western samples (r ¼ 0.26: Burke et al. 2010). Curiously, Americans respond more defensively to death thoughts than do those from other countries. In the previous section, we discussed Herrmann et al.’s (2008) work showing substantial qualitative differences in punishment between Western and non-Western societies. While Western countries all clump at one end of Figure 4, the Americans anchor the extreme end of the West’s distribution. Perhaps it is this extreme tendency for Americans to punish free-riders, while not punishing cooperators, that contributes to Americans having the world’s highest worker productivity. American society is also anomalous, even relative to other Western societies, in its low relational focus in work settings, which is reflected in practices such as the encouragement of an impersonal work style, direct (rather than indirect) communication, the clear separation of the work domain from the non-work, and discouragement of friendships at work (Sanchez-Burks 2005). 5.2. Similarities between Americans and other Westerners

We are unable to locate any research program (other than the ones reviewed in the first two telescoping contrasts) that has demonstrated that American psychological and behavioral patterns are similar to the patterns of other Westerners. We reason that there should be many similarities between the United States and the rest of the West, and we assume that many researchers share our impression. Perhaps this is why we are not able to find studies that have been conducted to explicitly establish these similarities – many researchers likely would not see such studies as worth the effort. In the absence of comparative evidence for a given phenomenon, it might not be unreasonable to assume that the Americans would look similar to the rest of the West. However, the above findings provide a hint that, at least along some key dimensions, Americans are extreme. 5.3. Summary of Contrast 3

There are few research programs that have explicitly sought to contrast Americans with other Westerners on psychological or behavioral measures. However, those BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? phenomena for which sufficient data are available to make cross-population comparisons reveal that American participants are exceptional even within the unusual population of Westerners – outliers among outliers. 6. Contrast 4: Typical contemporary American subjects versus other Americans The previous contrasts have revealed that WEIRD populations frequently occupy the tail-ends of distributions of psychological and behavioral phenomena. However, it is important to recognize, as a number of researchers have (e.g., Arnett 2008; Medin & Atran 2004; Sears 1986), that the majority of behavioral research on non-clinical populations within North America is conducted with undergraduates (Peterson 2001; Wintre et al. 2001). Further, within psychology, the subjects are usually psychology majors, or at least taking introductory psychology courses. In the case of child participants, they are often the progeny of high-SES people. Thus, there are numerous social, economic, and demographic dimensions that tentatively suggest that these subjects might be unusual. But, are they? 6.1. Comparisons among contemporary adult Americans

Highly educated Americans differ from other Americans in many important respects. In the following subsections, we first highlight findings from social psychology and then from behavioral economics. 6.1.1. Findings from social psychology. For a number of the phenomena reviewed above in which Americans were identified as global outliers, highly educated Americans occupy an even more extreme position than less-educated Americans. Here we itemize eight examples. 1. Although college-educated Americans have been found to rationalize their choices in dozens of postchoice dissonance studies, Snibbe and Markus (2005) found that non-college-educated American adults do not (cf. Sheth 1970). 2. Although Americans are the most individualistic people in the world, American undergraduates score higher on some measures of individualism than do their non-college-educated counterparts, particularly for those aspects associated with self-actualization, uniqueness, and locus of control (Kusserow 1999; Snibbe & Markus 2005). 3. Conformity motivations were found to be weaker among college-educated Americans than among noncollege-educated Americans (Stephens et al. 2007), who acted in ways more similar to that observed in East Asian samples (cf. Kim & Markus 1999). 4. Non-college-educated adults are embedded in more tightly structured social networks than are college students (Lamont 2000), which raises the question of whether research on relationship formation, dissolution, and interdependence conducted among students will generalize to the population at large (cf. Adams 2005; Falk et al. 2009). 5. A large study that sampled participants from the general population in southeastern Michigan found that working-class people were more interdependent and more holistic than middle-class people (Na et al., in press)

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6. The moral reasoning of college-educated Americans occurs almost exclusively within the ethic of autonomy, whereas non-college-educated Americans use the ethics of community and divinity (Haidt et al. 1993; Jensen 1997). Parallel differences exist in moral reasoning between American liberals and conservatives (Haidt & Graham 2007). 7. American college students respond more favorably toward other groups in society, are more supportive of racial diversity, and are more motivated to mask or explain away negative intergroup attitudes, than are American (non-student) adults (Henry 2009). This difference is more problematic because the percentage of psychological studies of prejudice that exclusively rely on student samples has increased over the last two decades (from 82.7% to 91.6%), and this percentage is accentuated in the higher-impact social psychology journals (Henry 2009). 8. A meta-analysis reveals that college students (the vast majority of whom were American) respond with more cultural worldview defense to death thoughts (r ¼ 0.36) than do non-college students (r ¼ 0.25: Burke et al. 2010). More broadly, a second-order meta-analysis (N . 650,000, Number of studies . 7,000) of studies that included either college student samples or non-student adult samples revealed that the two groups differed either directionally or in magnitude for approximately half of the phenomena studied (e.g., attitudes, gender perceptions, social desirability: Peterson 2001). However, no clear pattern regarding the factors that accounted for the differences emerged. Other research has found that American undergraduates have higher degrees of self-monitoring (Reifman et al. 1989), are more susceptible to attitude change (Krosnick & Alwin 1989), and are more susceptible to social influence (Pasupathi 1999) compared to nonstudent adults. 6.1.2. Findings from behavioral economics. Consistent

and non-trivial differences between undergraduates and fully-fledged adults are emerging in behavioral economics as well. When compared with diverse and sometimes representative adult samples, undergraduate subjects consistently set the lower bound for prosociality in experimental measures of trust, fairness, cooperation, and punishment of unfairness or free-riding. For example, in both the Ultimatum and Dictator Games, non-student Americans (both rural and urban participants) make significantly higher offers than do undergraduate subjects (Henrich & Henrich 2007). The difference is most pronounced in Dictator Games in which samples of non-student American adults from Missouri (urban and rural Missouri did not differ) offered a mean 47% of the total stake while undergraduate freshmen gave 32%, well within the typical range for undergraduates in this game (Camerer 2003; Ensminger & Cook, under review; Henrich & Henrich, under review). These seemingly high offers among non-students in the Dictator Game are similar to those found in other non-student samples in the United States (Carpenter et al. 2005; Henrich & Henrich 2007). It is the student results that are anomalous. Similarly, more recent research comparing students with both representative and selectively diverse samples of adults using the Trust Game, Ultimatum Game, and Public Goods Game shows that undergraduates ride the lower bound on prosociality measures (Bellemare &

Henrich et al.: The weirdest people in the world? Kro¨ger 2007; Bellemare et al. 2008; Carpenter et al. 2008; Fehr & List 2004). In fact, “being an undergraduate” (or being young and educated) is one of the few demographic variables that seems to matter in explaining within-country variability. Behavioral economics research also indicates that developmental or acculturative changes to some motivations and preferences are still occurring within the age range of undergraduates (Henrich 2008). For example, Ultimatum Game offers continue to change over the university years, with freshmen making lower offers than seniors (Carter & Irons 1991). Other work shows that offers do not hit their adult plateau in behavioral games until around age 24 (Carpenter et al. 2005), after which time offers do not change with age until people reach old age. In the Trust Game, measures of trust and trustworthiness increase with age, until they reach a plateau close to age 30 (Sutter & Kocher 2007a). Such research may explain why treatment effects also depend on the subject pool used, with students being the most sensitive. For example, Dictator Game treatments involving double-blind setups, such that the experimenter cannot know how much a subject contributes, have dramatically smaller effects on offers among nonstudent adults, and sometimes no effect at all in adult populations outside the United States (Lesorogol & Ensminger, under review). Similarly, unconscious religious primes increased Dictator Game offers in a Canadian student sample of religious and nonreligious participants alike, but when non-student adults were sampled, no significant effect emerged for the nonreligious adults (Shariff & Norenzayan 2007). For several of these economics measures, such as public good contributions (Egas & Riedl 2008), undergraduate behavior is qualitatively similar to fully-fledged adult behaviors, just less prosocial. However, in at least one area (so far), it appears that a particularly interesting phenomenon is qualitatively absent in undergraduates by comparison with fully-fledged adults from the same populations: As discussed earlier for small-scale societies, researchers using the Ultimatum Game have found systematic, nontrivial tendencies in many populations to reject offers greater than 50% of the stake, a phenomenon neither previously observed in students nor intuited by researchers. Recent work using representative adult samples has revealed this tendency for “hyper-fair rejections” among non-student adults in Western populations, though it is substantially weaker than in many of the non-Western populations discussed above (Bellemare et al. 2008; Guth et al. 2003; Wallace et al. 2007). 6.2. Comparisons among subpopulations of American children

Although studying young children is one important strategy for discerning universals, it does not completely avoid these challenges, as developmental studies are frequently biased toward middle- and upper-class American children. Recent evidence indicates that something as seemingly basic as the differences in spatial reasoning between males and females (Hyde 1981; Mann et al. 1990; Voyer et al. 1995) does not generalize well to poor American children. On two different spatial tasks, repeated four times over two years with 547 second- and third-graders, low-

SES children did not show the sex differences observed in middle- and high-SES children from Chicago (Levine et al. 2005). Such findings, when combined with other research indicating no sex differences on spatial tasks among migratory foragers (Berry 1966), suggest that a proper theory of the origins of sex differences in spatial abilities needs to explain why both poor Chicago children and foragers do not show any sex differences. Research on IQ using analytical tools from behavioral genetics has long shown that IQ is highly heritable, and not strongly influenced by shared family environment (Bouchard 2004). However, research using 7-year-old twins drawn from a wide range of socioeconomic statuses, shows that contributions of genetic variation and shared environment vary dramatically from low- to high-SES children (Turkheimer et al. 2003). For high-SES children, where environmental variability is negligible, genetic differences account for 70–80% of the variation, with shared environment contributing less than 10%. For low-SES children, where there is far more variability in environmental contributions to intelligence, genetic differences account for 0–10% of the variance, with shared environment contributing about 60%. This raises the specter that much of what we think we have learned from behavioral genetics may be misleading, as the data are disproportionately influenced by WEIRD people and their children (Nisbett 2009). A similar problem of generalizing from narrow samples exists for genetics research more broadly. Genetic findings obtained with one sample frequently do not replicate in a second sample, to the point that Nature Genetics now requires all empirical papers to include data from two independent samples. There are at least two ways in which geographically limited samples may give rise to spurious genotype-phenotype associations. First, the proportions of various polymorphisms vary across different regions of the world due to different migratory patterns and histories of selection (e.g., Cavalli-Sforza et al. 1994). A genetic association identified in a sample obtained from one region may not replicate in a sample from another region because it involves interactions with other genetic variants that are not equally distributed across regions. Second, the same gene may be expressed differently across populations. For example, Kim et al. (in press) found that a particular serotonin receptor polymorphism (5-HTR1A) was associated with increased attention to focal objects among Americans, but that the same allele was associated with decreased attention to focal objects among Koreans. Researchers would draw different conclusions regarding the function of this polymorphism depending upon the location of their sample. A more complete investigation of heritability and genetic associations demands a comparison of measures across diverse environments and populations. 6.3. Contemporary Americans compared with previous generations

Contemporary Americans may also be psychologically unusual compared to their forebears 50 or 100 years ago. Some documented changes among Americans over the past few decades include increasing individualism, as indicated by increasingly solitary lifestyles dominated by individual-centered activities and a decrease in group participation (Putnam 2000), increasingly positive self-esteem BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? (Twenge & Campbell 2001), and a lower need for social approval (Twenge & Im 2007). These findings suggest that the unusual nature of Americans in these domains, as we reviewed earlier, may be a relatively recent phenomenon. For example, Rozin (2003) found that attitudes towards tradition are more similar between Indian college students and American grandparents than they are between Indian and American college students. Although more research is needed to reach firm conclusions, these initial findings raise doubts as to whether research on contemporary American students (and WEIRD people more generally) is even extendable to American students of previous decades. The evidence of temporal change is probably best for IQ. Research by Flynn (1987; 2007) shows that IQ scores increased over the last half century by an average of 18 points across all industrialized nations for which there were adequate data. Moreover, this rise was driven primarily by increasing scores on the analytic subtests. This is a striking finding considering recent work showing how unusual Westerners are in their analytic reasoning styles. Given such findings, it seems plausible that Americans of only 50 or 100 years ago were reasoning in ways much more similar to the rest of the non-Western world than Americans of today. 6.4. Similarities between typical experimental subjects and other Americans

We expect that typical American subjects are very similar to other Americans in myriad ways. The problem with this expectation, however, is that it is not immediately apparent in which domains they should be similar. We think that there are enough differences between these two groups to raise concerns about speaking incautiously on the thoughts and behaviors of Americans, in general. There have been rather few studies that have explicitly contrasted whether undergraduates or college-educated Americans differ in various psychological measures from those who are not currently students, or who were never college-educated. There are numerous meta-analyses that include data from both college student and nonstudent samples that speak partially to this issue. Although the meta-analyses do not specify the national origin of the participants, we assume that most of the subjects were American. Some of these analyses indicate considerable similarity between student and non-student samples. For example, the aforementioned second-order meta-analysis (Peterson 2001) revealed similarities between students and non-student samples for about half of the phenomena. Similarly, the relation between attribution styles and depression (Sweeney et al. 1986), and the relations among intentions, attitudes, and norms (Farley et al. 1981) do not show any appreciable differences between student and non-student samples. In these instances, there do not appear to be any problems in generalizing from student to non-student samples, which may suggest that college education, and SES more generally, is not related to these phenomena. 6.5. Summary of Contrast 4

Numerous findings from multiple disciplines indicate that, in addition to many similarities, there are differences 78

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among typical subjects and the rest of the American population in unexpected domains. In some of these domains (e.g., individualism, moral reasoning, worldview defense in response to death thoughts, and perceptions of choice), the data from American undergraduates represent even more dramatic departures from the patterns identified in non-Western samples. Further, contemporary American college students appear further removed along some of these dimensions than did their predecessors a few decades earlier. Typical subjects may be outliers within an outlier population. 7. General discussion As the four contrasts summarized above reveal, WEIRD subjects are unusual in the context of the world in some key ways. In this section, we first discuss the main conclusions and implications of our empirical review. We then address two common challenges to our claim that WEIRD subjects are frequent outliers. Finally, we offer some recommendations for how the behavioral sciences may address these challenges. 7.1. Summary of our conclusions and implications 7.1.1. Pronounced population variation is commonplace in the behavioral sciences. There are now enough sources

of experimental evidence, using widely differing methods from diverse disciplines, to indicate that there is substantial psychological and behavioral variation among human populations. As we have seen, some of this variability involves differences in the magnitude of effects, motivations, or biases. There is also considerable variability in both whether certain effects or biases exist in some populations (as with antisocial punishment and the Mu¨ller-Lyer illusion) and in which direction they go (as with preferences for analytic versus holistic reasoning). The causal origins of such population-level variation may be manifold, including behavioral plasticity in response to different environments, epigenetic effects, divergent trajectories of cultural evolution, and even the differential distribution of genes across groups in response to divergent evolutionary histories. With all these causal possibilities on the table, we think the existence of this population-level variation alone should suffice to energize course corrections in our research directions. We have also identified many domains in which there are striking similarities across populations. These similarities could indicate reliably developing adaptations (e.g., theory of mind), by-products of innate adaptations (such as some aspects of religious cognition), or independent inventions or diffusions of learned responses that have universal utility (such as counting systems, dance, cooking practices, or techniques for making fire). We have no doubt that there are many more pan-human similarities than we have mentioned (e.g., movement perception, taste for sugar, chunking, habituation, and depth computation); however, thus far there are few databases with individual-level measures sufficient to evaluate the similarities or differences across populations. Many of the processes identified above that vary dramatically across populations would seem to be “basic” psychological processes. The reviewed findings identified

Henrich et al.: The weirdest people in the world? variation in aspects of visual perception, memory, attention, fairness motivations, categorization, induction, spatial cognition, self-enhancement, moral reasoning, defensive responses to thoughts about death, and heritability estimates of IQ. These domains are not unique to the social world – they span social as well as nonsocial aspects of the environment, and do not appear to be any less “fundamental” than those domains for which much similarity has been identified. At this point, we know of no strong grounds to make a priori claims to the “fundamentalness” or the likely universality of a given psychological process. The application of evolutionary theory does not provide grounds for such a priori claims of “fundamental” or “basic” processes, at least in general. Evolutionary theory is a powerful tool for generating and eliminating hypotheses. However, despite its power (or perhaps because of it), it is often overly fecund, as it generates multiple competing hypotheses, with predictions sometimes dependent on unknown or at least debatable aspects of ancestral environments. Hence, adjudicating among alternative evolutionary hypotheses often requires comparative work. Moreover, theoretical work is increasingly recognizing that natural selection has favored ontogenetic adaptations that allow humans, and other species, to adapt non-genetically to local environments (Henrich 2008). Although we do not yet know of a principled way to predict whether a given psychological process or behavioral pattern will be similar across populations in the absence of comparative empirical research, it would surely be of much value to the field if there were a set of criteria that could be used to anticipate universality (Norenzayan 2006; Norenzayan & Heine 2005). Here we discuss some possible criteria that might be considered. First, perhaps there are some domains in which researchers could expect phenomena to be more universal than they are in other domains. We believe that the degree of universality does likely vary across domains, although this has yet to be demonstrated. Many researchers (including us) have the intuition that there are cognitive domains related to attention, memory, and perception in which inter-population variability is likely to be low. Our review of the data, however, does not bolster this intuition. Second, it might be reasonable to assume that some phenomena are more fundamental to the extent that they are measured at a physiological or genetic level, such as genotype-phenotype relations or neural activity. However, recall that the same genes can be expressed differently across populations (e.g., Kim et al., in press), and the same cognitive task may be associated with different neural activations across populations (e.g., Hedden et al. 2008). Third, there may be criteria by which one could confidently make generalizations from one well-studied universal phenomenon to another similar phenomenon; for example, because pride displays are highly similar across populations (e.g., Tracy & Matsumoto 2008), it might follow that the conceptually related shame display should also be similar across populations as well (Fessler 1999). Fourth, it would seem that demonstrating a process or effect in other species, such as rats or pigeons, would indicate human universality (and more). Although this may generally be true, several researchers have argued that culture-gene coevolution has dramatically shaped human

evolution in a manner uncharacteristic of other species (Richerson & Boyd 2005). Part of this process may involve the off-loading of previously genetically encoded preferences and abilities into culture (e.g., tastes for spices). Fifth, phenomena which are evident among infants might be reasonably assumed to be more universal than phenomena identified in older children or adults. We suspect this is the case, but it is possible that early biases can be reversed by later ontogeny. Showing parallel findings or effects in both adults and infants from the same population is powerful, and it raises the likelihood of universality; but quite different environments might still shape adult psychologies away from infant patterns (consider the spatial cognition finding with apes, children, and adults). Finally, perhaps particular brain regions are less responsive to experience, such that if a given phenomenon was localized to those regions one could anticipate more universality. Whatever the relevant principles, it is an important goal to develop theories that predict which elements of our psychological processes are reliably developing across normal human environments and which are locally variable (focusing on the how and why of that variability: Barrett 2006). We note that behavioral scientists have typically been overly confident regarding the universality of what they study, and as this review reveals, our intuitions for what is universal do not have a particularly good track record. We also think this article explains why those intuitions are so poor: Most scientists are WEIRD, or were trained in WEIRD subcultures. Hence, any set of criteria by which universality can be successfully predicted must be grounded in substantial empirical data. We look forward to seeing data that can help to identify criteria to anticipate universality in future research. 7.1.2. WEIRD subjects may often be the worst population from which to make generalizations. The empirical

foundation of the behavioral sciences comes principally from experiments with American undergraduates. The patterns we have identified in the available (albeit limited) data indicate that this sub-subpopulation is highly unusual along many important psychological and behavioral dimensions. It is not merely that researchers frequently make generalizations from a narrow subpopulation. The concern is that this particular subpopulation is highly unrepresentative of the species. The fact that WEIRD people are the outliers in so many key domains of the behavioral sciences may render them one of the worst subpopulations one could study for generalizing about Homo sapiens. To many anthropologically savvy researchers it is not surprising that Americans, and people from modern industrialized societies more generally, appear unusual vis-a`-vis the rest of the species. For the vast majority of its evolutionary history, humans have lived in small-scale societies without formal schools, governments, hospitals, police, complex divisions of labor, markets, militaries, formal laws, or mechanized transportation. Every household provisioned much or all of its own food; made its own clothes, tools, and shelters; and – aside from sexual divisions of labor – most everyone had to master the same skills and domains of knowledge. Children typically did not grow up in small, monogamous nuclear families with few kin BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? around, nor were they away from their families at school for much of the day. Rather, through the course of this history, and in some contemporary societies still, children have typically grown up in mixed-age playgroups, where they received little active instruction or exposure to books or TV (Fiske 1998; Lancy 1996; 2008); they learned largely by observation and imitation; received more directives, more physical punishment, and less praise; and were less likely to be engaged in conversation by adults (and there’s no “why” phase). By age 10, children in some foraging societies obtain sufficient calories to feed themselves, and routinely kill and butcher animals. Adolescent females in particular take on most of the work-related responsibilities of adult women. People in small-scale societies tend to have less reliable nutrition, greater exposure to hunger, pain, chronic diseases, and lethal dangers, and more frequently experience the death of family members. WEIRD people, from this perspective, grow up in, and adapt to, a rather atypical environment vis-a`-vis that of most of human history. It should not be surprising that their psychological world is unusual as well. 7.1.3. Research topics have been limited by the heavy reliance on WEIRD populations. Relying on WEIRD

populations may cause researchers to miss important dimensions of variation, and devote undue attention to behavioral tendencies that are unusual in a global context. There are good arguments for choosing topics that are of primary interest to the readers of the literature (i.e., largely WEIRD people); however, if the goal of the research program is to shed light on the human condition, then this narrow, unrepresentative sample may lead to an uneven and incomplete understanding. We suspect that some topics such as self-enhancement, cognitive dissonance, fairness, and analytic reasoning might not have been sufficiently interesting to justify in-depth investigation for most humans at most times throughout history. Alternatively, the behavioral sciences have shown a rather limited interest in such topics as kinship, food, ethnicity (not race), religion, sacred values, polygamy, animal behavior, and rituals (for further critiques on this point, see Rozin 2001; Rozin et al. 2006). Had the behavioral sciences developed elsewhere, important theoretical foci and central lines of research might likely look very different (Medin & Bang 2008). Moreover, it may be unnecessarily difficult to study psychological phenomena in populations where the phenomena are unusually weak, as is the case for conformity or shame among Americans (see Fessler 2004). 7.1.4. Studying children and primates is crucial, but not a replacement for comparative work. Working with chil-

dren and nonhuman primates is essential for understanding human psychology. However, it is important to note that despite its great utility and intuitive appeal, such research does not fully obviate these challenges. In the case of primate research, discovering parallel results in great apes and in one human population is an important step, but it doesn’t tell us how reliably a particular aspect of psychology develops. As the spatial cognition work indicates, because language and cultural practices can – but need not – influence the cognition humans acquired from their phylogenetic history as apes, 80

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establishing the same patterns of cognition in apes and Westerners is insufficient to make any strong claims about universality. Suppose most psychologists were Hai\\om speakers (instead of Indo-European speakers); they might have studied only Hai\\om-speaking children and adults, as well as nonhuman apes, and concluded (incorrectly) that allocentric spatial reasoning was universal. Similarly, imagine if Tsimane economists compared Ultimatum Game results for Tsimane adults to those for chimpanzees (Gurven 2004; Henrich & Smith 2001; Jensen et al. 2007). These researchers would have found the same results for both species, and concluded that standard game theoretic models (assuming pure self-interest) and evolutionary analyses (Nowak et al. 2000) were fairly accurate predictors in Ultimatum Game behavior for both chimpanzees and humans – a very tidy finding. In both of these cases, the conclusions would be opposite to those drawn from studies with WEIRD populations.14 Studying children is crucial for developing universal theories. However, evidence suggests that psychological differences among populations can emerge relatively early in children (as with folkbiological reasoning), and sometimes differences are even larger in children than in adults, as with the Mu¨ller-Lyer illusion. Moreover, developmental patterns may be different in different populations, as with sex differences in spatial cognition between low-income versus middle- and high-income subpopulations in the United States, or with performance in the false belief task. This suggests a need for converging lines of research. The most compelling conclusions regarding universality would derive from comparative work among diverse human populations done with both adults and children, including infants if possible. Human work can then be properly compared with work among nonhuman species (including but not limited to primates), based on a combination of field and laboratory work. 7.1.5. Understanding human diversity is crucial for constructing evolutionary theories of human behavior. Evolution has equipped humans with ontogen-

etic programs, including cultural learning, that help us adapt our bodies and brains to the local physical and social environment. Over the course of human history, convergent forms of cultural evolution have effectively altered (1) our physical environments with tools, technology, and knowledge; (2) our cognitive environments with counting systems, color terms, written symbols, novel grammatical structures, categories, and heuristics; and (3) our social environments with norms, institutions, laws, and punishments. Broad patterns of psychology may be – in part – a product of our genetic program’s common response to culturally constructed environments that have emerged and converged over thousands of years. This means that the odd results from small-scale societies, instead of being dismissed as unusual exceptions, ought to be considered as crucial data points that help us understand the ontogenetic processes that build our psychologies in locally adaptive and context-specific ways. Based on this and the previous point, it seems clear that comparative developmental studies involving diverse human societies combined with parallel studies of nonhuman primates (and other relevant species) provide an approach to understanding human psychology and behavior that can allow us to go well beyond merely establishing

Henrich et al.: The weirdest people in the world? universality or variability. Such a systematic, multipronged approach can allow us to test a richer array of hypotheses about the processes by which both the reliable universal patterns and the diversity of psychological and behavioral variation emerge. 7.1.6. Exclusive use of WEIRD samples is justified when seeking existential proofs15. Our argument should not be

construed to suggest that the exclusive use of WEIRD samples should always be avoided. There are cases where the exclusive use of these samples would be legitimate to the extent that generalizability is not a relevant goal of the research, at least initially (Mook 1983). Research programs that are seeking existential proofs for psychological or behavioral phenomena, such as in the case of altruistic punishment discussed earlier (e.g., Fehr & Ga¨chter 2002), could certainly start with WEIRD samples. That is, if the question is whether a certain phenomenon can be found in humans at all, reliance on any slice of humanity would be a legitimate sampling strategy. For another example, Tversky, Kahneman, and their colleagues sought to demonstrate the existence of systematic biases in decision-making that violate the basic principles of rationality (Gilovich et al. 2002). Most of their work was done with WEIRD samples. Counterexamples to standard rationality predictions could come from any sample in the world.16 Furthermore, existential proof for a psychological phenomenon in WEIRD samples can be especially compelling when such a finding is theoretically unexpected. For example, Rozin and Nemeroff (1990) found (surprisingly, to many) that even elite U.S. university students show some magical thinking. Nevertheless, even in such cases, learning about the extent to which population variability affects such phenomena is a necessary subsequent phase of the enterprise, since any theory of human behavior ultimately has to account for such variability (if it exists). 7.2. Concerns with our argument

We have encountered two quite different sets of concerns about our argument. Those with the first set of concerns, elaborated below, worry that our findings are exaggerated because (a) we may have cherry-picked only the most extreme cases that fit our argument, and have thus exaggerated the degree to which WEIRD people are outliers, and/or (b) the observed variation across populations may be due to various methodological artifacts that arise from translating experiments across contexts. The second set of concerns is quite the opposite: Some researchers dismissively claim that we are making an obvious point which everyone already recognizes. Perhaps the most productive thing we offer is for these two groups of readers to confront each other. We preface our response to the first set of concerns with an admonition: Of course, many patterns and processes of human behavior and psychology will be generally shared across the species. We recognize that human thought and behavior is importantly tethered to our common biology and our common experiences. Given this, the real challenge is to design a research program that can explain the manifest patterns of similarity and variation by clarifying the underlying evolutionary and developmental processes.

We offer three general responses to the concern that our review presents a biased picture. To begin, we constructed our empirical review by targeting studies involving important psychological or behavioral concepts which were, or still are, considered to be universal, and which have been tested across diverse populations. We also listed and discussed major comparative studies that have identified important cross-population similarities. Since we have surely overlooked relevant material, we invite commentators to add to our efforts in identifying phenomena which have been widely tested across diverse subpopulations. Second, we acknowledge that because proper comparative data are lacking for most studied phenomena, we cannot accurately evaluate the full extent of how unusual WEIRD people are. This is, however, precisely the point. We hope research teams will be inspired to span the globe and prove our claims of non-representativeness wrong. The problem is that we simply do not know how well many key phenomena generalize beyond the extant database of WEIRD people. The evidence we present aims only to challenge (provoke?) those who assume that undergraduates are sufficient to make claims about human psychology and behavior. Third, to address the concern that the observed population-level differences originate from the methodological challenges of working across diverse contexts, we emphasize that the evidence in our article derives from diverse disciplines, theoretical approaches, and methodological techniques. They include experiments involving (1) incentivized economic decisions; (2) perceptual judgments; (3) deceptive experimental practices that prevented subjects from knowing what was being measured; and (4) children, who are less likely than adults to have motivations to shape their responses in ways that they perceive as desirable (or undesirable) to the experimenter. The findings, often published in the best journals of their respective fields, hinged on the researchers making a compelling case that their methodology was comparably meaningful across the populations being studied. Furthermore, the same methods that have yielded population differences in one domain have demonstrated similarities in other domains (Atran 2005; Haun et al. 2006b; Henrich et al. 2006; Herrmann et al. 2008; Medin & Atran 2004; Segall et al. 1966). If one wants to highlight the demonstrated similarities, one cannot then ignore the demonstrated differences which relied on the same or similar methodologies. Note also that few of the findings that we reviewed involve comparing means across subjective self-report measures, for which there are well-known challenges in making cross-population comparisons (Chen et al. 1995; Hamamura et al. 2008; Heine et al. 2002; Norenzayan et al. 2002b; Peng et al. 1997). Therefore, while methodological challenges may certainly be an issue in some specific cases, we think it strains credulity to suggest that such issues invalidate the thrust of our argument, and thus eliminate concerns about the non-representativeness of typical subjects. 7.3. Our recommendations

Our experience is that many researchers who work exclusively with WEIRD subjects would like to establish the BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Henrich et al.: The weirdest people in the world? broad generalizability of their findings. Even if they strongly suspect that their findings will generalize across the species, most agree that it would be better to have comparative data across diverse populations. The problem, then, is not exclusively a scientific or epistemological disagreement, but one of institutionalized incentives as well. Hence, addressing this issue will require adjusting the existing incentive structures for researchers. The central focus of these adjustments should be that in presenting our research designs to granting agencies, or our empirical findings in journals, we must explicitly address questions of generalizability and representativeness. With this in mind, we offer the following recommendations. Journal editors and reviewers should press authors to both explicitly discuss and defend the generalizability of their findings. Claims and confidence regarding generalizability must scale with the strength of the empirical defense. If a result is novel, being explicitly uncertain about generalizability should be fine, but one should not imply universality without an empirically grounded argument. This does not imply that all experimentalists need to shift to performing comparative work across diverse subject pools! As comparative evidence accumulates in different domains, researchers will be able to assess the growing body of comparative research and thus be able to calibrate their confidence in the generalizability of their findings. The widespread practice of subtly implying universality by using statements such as “people’s reasoning is biased. . .” should be avoided. “Which people?” should be a primary question asked by reviewers. We think this practice alone will energize more comparative work (Rozin 2009). The experience of evolutionarily-oriented researchers attests to the power of such incentives. More than other researchers in the social sciences, evolutionary researchers have led the way in performing systematic comparative work, drawing data from diverse societies. This is not because they are interested in variation per se (though some are), but because they are compelled, through some combination of their scientific drive and the enthusiasm of their critics, to test their hypotheses in diverse populations (e.g., Billing & Sherman 1998; Buss 1989; Daly & Wilson 1988; Fessler et al. 2005; Gangestad et al. 2006; Henrich et al. 2005; Kenrick & Keefe 1992a; 1992b; Low 2000; Medin & Atran 2004; Schaller & Murray 2008; Schmitt 2005; Sugiyama et al. 2002; Tracy & Robins 2008). Meta-analyses are often compromised because many studies provide little background information about the subjects. Journal editors should require explicit and detailed information on subject-pool composition (see Rozin 2001). Some granting agencies already require this. Comparative efforts would also be greatly facilitated if researchers would make their data readily available to any who asked; or, better yet, data files should be made available online. Sadly, a recent investigation found that only 27% of authors in psychology journals shared their data when an explicit request was made to them to do so in accordance with APA guidelines (Wicherts et al. 2006). Tests of generalizability require broad access to published data. Given the general state of ignorance with regard to the generalizability of so many findings, we think granting 82

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agencies, reviewers, and editors would be wise to give researchers credit for tapping and comparing diverse subject pools. Work with undergraduates and the children who live around universities is much easier than going out into the world to find subjects. As things stand, researchers suffer a competitive disadvantage when seeking a more diverse sampling of subjects. Because many of the best journals routinely require that papers include several studies to address concerns about internal validity (Carver 2004), the current incentives greatly favor targeting the easiest subject pool to access. There is an often unrecognized tradeoff between the experimental rigor of using multiple studies and the concomitant lack of generalizability that easy-to-run subject pools entail (Rozin 2009). If the incentive structure came to favor nonstudent subject pools, we anticipate that researchers could also be more persuasive in encouraging their universities and departments to invest in building non-student subject pools – for example, by setting up permanent psychological and behavioral testing facilities in bus terminals, Fijian villages, rail stations, airports, and anywhere diverse subjects might find themselves with extra time. Beyond this, departments and universities should build research links to diverse subject pools. There are literally untapped billions of people around the world who would be willing to participate in research projects, as both paid subjects and research assistants. The amounts of money necessary to pay people who might normally make less than $12 per day are trivial vis-a`-vis the average research grant. Development economists, anthropologists, and public health researchers already do extensive research among diverse populations, and therefore already possess the contacts and collaborations. Experimentalists merely need to work on building the networks. Funding agencies, departments, and universities can encourage and facilitate both professors and graduate students to work on expanding sample diversity. Research partnerships with non-WEIRD institutions can be established to further the goal of expanding and diversifying the empirical base of the behavioral sciences. By supplying research leaves, adjusted expectations of student progress, special funding sources, and institutionalized relationships to populations outside the university as well as to nonWEIRD universities, these organizations can make an important contribution to building a more complete understanding of human nature. 8. Closing words Although we are certainly not the first to worry about the representativeness of prevalent undergraduate samples in the behavioral sciences (Gergen 1973; Medin & Atran 2004; Norenzayan & Heine 2005; Rozin 2001; 2009; Sears 1986; Sue 1999), our efforts to compile an empirical case have revealed an even more alarming situation than previously recognized. The sample of contemporary Western undergraduates that so overwhelms our database is not just an extraordinarily restricted sample of humanity; it is frequently a distinct outlier vis-a`-vis other global samples. It may represent the worst population on which to base our understanding of Homo sapiens. Behavioral

Commentary/Henrich et al.: The weirdest people in the world? scientists now face a choice – they can either acknowledge that their findings in many domains cannot be generalized beyond this unusual subpopulation (and leave it at that), or they can begin to take the difficult steps to building a broader, richer, and better-grounded understanding of our species. ACKNOWLEDGMENTS We thank several anonymous reviewers and the following colleagues for their very helpful comments on earlier versions of this manuscript: Nicholas Epley, Alan Fiske, Simon Ga¨chter, Jonathan Haidt, Shinobu Kitayama, Shaun Nichols, Richard Nisbett, Paul Rozin, Mark Schaller, Natalie Henrich, Daniel Fessler, Michael Gurven, Clark Barrett, Ted Slingerland, Rick Shweder, Mark Collard, Paul Bloom, Scott Atran, Doug Medin, Tage Rai, Ayse Uskul, Colin Camerer, Karen Wynn, Tim Wilson, and Stephen Stich.

NOTES 1. We also use the term “WEIRD” throughout this paper to refer to the exceptional nature of this sample, and do not intend any negative connotations or moral judgments by the acronym. 2. Key steps include: (1) establishing nationally representative experimental samples in Europe (Fehr et al. 2002; Guth et al. 2003); (2) applying experimental methods in developing countries (Cardenas & Carpenter 2008; Tanaka et al., forthcoming); (3) creating university-wide subject recruiting rather than discipline-specific subject pools (most economic experiments); and (4) targeting specific samples of non-student subjects (Bellemare et al. 2008; Bellemare & Kro¨ger 2007; Harrison et al. 2002; List 2004). 3. Comparative studies of individual decision-making processes using samples from small-scale and WEIRD populations, including explorations of risk aversion, prospect theory, and inter-temporal choice, yield mixed results. Sometimes similarities, both qualitative and quantitative, are found. Other times differences emerge (Cardenas & Carpenter 2008; Henrich & McElreath 2002; Hsu et al. 2009; Humphrey & Verschoor 2004a; 2004b; Kirby et al. 2002; Tanaka et al., forthcoming). So far, we do not see how to figure out which features will vary and which will not. 4. Rivers, for instance, found that cultures with a single color term for blue and green could still tell the difference between a blue and a green thread. (See Rivers 1901a). 5. Fessler also emphasizes important differences in shame and guilt between Americans and Indonesians. 6. To illustrate the limits of inferring universality from twopopulation comparisons, we note the finding that field independence on the Rod & Frame test is shown for both migratory foragers and Americans (Witkin & Berry 1975), yet East Asians and sedentary foragers show evidence for field dependence (Ji et al. 2000). 7. We are using “Western” to refer to those countries clustered in the northwest of Europe (the United Kingdom, France, Germany, Switzerland, the Netherlands, etc.), and British-descent societies such as the United States, Canada, New Zealand, and Australia. In particular, we are concerned about those populations from which most subjects in behavioral and psychological experiments are drawn. We recognize that there are important limitations and problems with this label, but we use it for convenience. 8. See also Knowles et al. (2001); but, for contrary findings, see Lieberman et al. (2005). 9. Interestingly, evidence indicates a somewhat different pattern in small-scale societies; see Marlowe (2004), Moore et al. (2006), and Pillsworth (2008). 10. Efforts to replicate these findings in various small-scale societies have all failed (Marlowe & Wetsman 2001; Sugiyama

2004; Yu & Shepard 1998). These failures suggest a more complicated and context-specific set of evolutionary hypotheses (Marlowe et al. 2005; Swami & Tove´e 2007). 11. The factor structure was less evident in a number of developing populations (e.g., Botswana, Ethiopia, Lebanon, Malaysia, Puerto Rico, Uganda), where independent assessments revealed that the data quality was poor. Future efforts to obtain betterquality data from these countries are important for demonstrating the universality of the Five Factor Model. 12. The robustness of the Five Factor Model is considerably weaker when it is derived from indigenous personality traits from other languages, although some of the five traits do still emerge (Benet-Martinez & Waller 1995; Cheung et al. 1996; Saucier et al. 2005). 13. As American and Canadian researchers at a Canadian university, we note that Canada is also a highly unusual population along the same lines as the United States, although perhaps not quite as pronounced as the United States, at least in terms of individualism (Hofstede 1980). 14. These examples illustrate a parallel problem for those interested in the differences between human and nonhuman cognition. Since most ape-human comparisons involve WEIRD people (or their children) as subjects, some seeming apehuman differences may not represent real species-level contrasts, but may instead reflect the psychological peculiarities of WEIRD people (Boesch 2007). 15. Thanks to Shaun Nichols for pointing this out. 16. We note that the heuristics and biases derived from this empirical work were, however, readily extended to “people” without hesitation (Kahneman et al. 1982).

Open Peer Commentary Why a theory of human nature cannot be based on the distinction between universality and variability: Lessons from anthropology doi:10.1017/S0140525X10000026 Rita Astuti and Maurice Bloch Department of Anthropology, London School of Economics and Political Science, London WC2A 2AE, United Kingdom. [email protected] http://www2.lse.ac.uk/anthropology/people/astuti.aspx [email protected] http://www2.lse.ac.uk/anthropology/people/bloch.aspx

Abstract: We welcome the critical appraisal of the database used by the behavioral sciences, but we suggest that the authors’ differentiation between variable and universal features is ill conceived and that their categorization of non-WEIRD populations is misleading. We propose a different approach to comparative research, which takes population variability seriously and recognizes the methodological difficulties it engenders.

The authors of the target article, Henrich et al., call for an ambitious reorganization of the behavioral sciences, motivated by two key observations: (1) that the populations on which behavioral scientists typically base their findings are outliers from the rest of humankind; and (2) that there is significant population variability, which complicates the identification of those behavioral and psychological features that are universal. We start by appraising BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? each of these observations (in reverse order), and we conclude by proposing a different approach to comparative research. 1. Population variability. The existence of significant population variability is convincingly documented by the authors, who find it as soon as they look beyond the ridiculously narrow samples on which claims of universality have typically been based. Although in agreement with the finding, we have a problem with the authors’ underlying assumption. This is the idea that it is possible to neatly sort variable features of human behavior and psychology from universal ones. As anthropologists, we have no problem in accepting that cultural, historical, and environmental contexts affect all the features discussed in the article, but this observation has no bearing on the question of whether such features are “universal” or “variable.” It is the variable/universal dichotomy itself (and the questions it generates) that is misleading. This is because human beings are affected simultaneously by processes of a different nature, among them phylogeny, history in its social and cultural instantiations, and ontogeny. But none of these processes is ever active in isolation, making it impossible to track its universal or variable effects. Searching in any human phenomenon for the clear signature of one of these processes in isolation is a wild goose chase. 2. WEIRD as outliers. The authors use three broad population contrasts in order to zoom in on the weirdness of the subject population used to generalize about human nature. The point is well taken, as is the call for more research among non-WEIRD populations. In their eagerness to condemn the reliance on WEIRD subjects, however, the authors end up presenting and conceptualizing population variability in terms of extremely dubious categories. Curiously, while they feel the need to clarify what they mean by the term “Western” and to acknowledge its limitations, they offer no apology for using “small-scale societies” as if the term referred to a unified, meaningful whole (a similar point could be made for “non-Westerner” or “East Asian”). This uncritical lumping together of a variety of disparate societies is particularly odd in a paper that denounces unsound generalizations. As clearly demonstrated by the results of the economic games, some “small-scale societies” can vary just as much among themselves as they do from the WEIRD population – a fact that should not be surprising given that “small-scale societies” are as caught up in the flow of human history as any other. One could argue that the extreme weirdness of the WEIRD population is partly the result of having lumped together other populations under too simplistic and under-theorized labels. 3. Our proposal. As anthropologists committed to the study of human nature (see Bloch 2005), we welcome Henrich et al.’s critical appraisal of the behavioral sciences’ comparative database. We feel, nonetheless, that the authors have not sufficiently taken to heart the fundamental implications of their analysis. One obvious conclusion they might have drawn is that behavioral scientists should pay more attention to the work of cultural/ social anthropologists, since these are the scientists who have made human variability their main focus. It is striking, however, how little reference Henrich et al. make to anthropological research. This, of course, is no accident. It has to do with the kind of data that anthropologists have produced, which in turn has to do with the history of their discipline. At the start, anthropologists went to the field with ready-made questions that were generated by a simplistic, yet highly influential, evolutionary theory, which is still the basis of popular understandings of the difference between “civilized” and “primitive” societies (the latter sometimes euphemistically called “smallscale”). But such outmoded theory had to be abandoned because, it was soon realized, human history does not proceed along a progressive and unilineal path. Because of the human capacity for culture, each human society is the unique product of a unique, albeit not isolated, history. Ever since the recognition of this fact, anthropologists have faced a methodological difficulty: Questions formulated from within one historical context produce misleading answers when

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transposed elsewhere, as they appear weird, uninterpretable, or mean something else (arguably, this is what generates the weirdness of the WEIRD population, since what distinguishes it from all the others is that it is the one that generates the questions). The way anthropologists have tried to overcome this challenge has been to abandon, initially at least, all questions formulated outside the context under their investigation. Rather, through participant observation, they have allowed themselves to discover, from the inside, the terms and values of the people they study. This strategy is not without difficulties, as it generates a kind of data that appears impressionistic and anecdotal and which, crucially, precludes comparison and generalization – which is why such data is so often ignored by other behavioral scientists, such as the authors of the target article. We recognize that this is a very serious limitation, but we insist that behavioral scientists must acknowledge and never underestimate the equally serious and unavoidable problem that led anthropologists down this methodological route in the first place. Therefore, the solution cannot be, as suggested by Henrich et al., to administer studies upon studies to the billions of (poor) people around the world who remain untapped by the behavioral sciences. The solution is far more complicated and costly. It requires an often uncomfortable compromise between internal validity and generality, and a lot more detailed ethnographic work than many seem to be willing to accept (see, e.g., Astuti & Harris 2008; Astuti et al. 2004). Only in this way will data from non-WEIRD populations become a meaningful and indispensable ingredient of any general theory about our species.

Weird people, yes, but also weird experiments doi:10.1017/S0140525X10000038 Nicolas Baumarda and Dan Sperberb a Institute of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford OX2 6PN, United Kingdom; bInstitut Jean Nicod, Ecole Normale Supe´rieure, 75005 Paris, France. [email protected] https://sites.google.com/site/nicolasbaumard/Home [email protected] http://www.dan.sperber.fr/

Abstract: While we agree that the cultural imbalance in the recruitment of participants in psychology experiments is highly detrimental, we emphasize the need to complement this criticism with a warning about the “weirdness” of some cross-cultural studies showing seemingly deep cultural differences. We take the example of economic games and suggest that the variety of results observed in these games may not be due to deep psychological differences per se, but rather due to different interpretations of the situation.

Henrich et al.’s article fleshes out in a very useful and timely manner comments often heard but rarely published about the extraordinary cultural imbalance in the recruitment of participants in psychology experiments and the doubt this casts on generalization of findings from these “weird” samples to humans in general. The authors mention that one of the concerns they have met in defending their views has been of a methodological nature: “the observed variation across populations may be due to various methodological artifacts that arise from translating experiments across contexts” (sect. 7.2, para. 1). Here we want to express a less sweeping methodological concern. While accepting the general conclusions and recommendations of the article, we believe they should be complemented with a warning about the “weirdness” of some experimental designs that have been used across cultures and seem to show deep cultural differences. In fact, they may just show quite different interpretations of the experimental situation by the participants. This is not to deny, of course, that these differences in interpretations are themselves

Commentary/Henrich et al.: The weirdest people in the world? both psychological and cultural and are worth studying in their own right. In fact, unless one pays attention to them, it unclear what the experimental evidence is really about. Let us illustrate our point with the case of economic games (discussed in sections 3.2, 4.1, and 6.1 of the target article). In these experiments, people are given a sum of money for free (which never happens in the real life) and have to share it with someone about whom they have no information (which also never happens in real life). Many researchers, including one of the article’s authors (see Henrich et al. 2005), have pointed out that cultural variations in economic games may have more to do with methodological problems than with actual cultural differences (Ensminger 2002; Heintz 2005; Lesorogol 2007). In particular, participants in these games have no information about the rights of each player over the stake and are asked to make a “blind” decision. But who owns the money? Is the money a gift? Is the money a payment in exchange for my participation? Who is the other participant? Is he or she someone I know? Does he or she have rights over the money? And so on. This leaves open the possibility that behavioural differences observed in economic games are not due to deep psychological differences per se, but rather due to different interpretations of the situation (for a similar point, see Hagen & Hammerstein 2006; Heintz 2005). For example, Henrich et al.’s (2005) study in 15 small-scale societies reveals a striking difference between the Lamalera, who make very generous offers in the Ultimatum Game, and the Tsimane and the Machigenga, who make very low offers in the very same game. But the game is likely to be construed very differently within these societies. The Lamalera, being collective hunters, may indeed see the money as jointly owned by the proposer and the recipient. By contrast, the Tsimane and the Machigenga, who are solitary horticulturalists, may see the money as their own property and therefore feel entitled to keep it. In the same way, Westerners may appear as outliers not because they have a different moral psychology, but rather because, living in very large, democratic and capitalist societies, they make different assumptions in economic games (e.g., that, not knowing the other participant – a situation of anonymity that is common in large-scale urban societies – they have no particular duty to share the stake with her). In line with this idea, economic games framed within a more detailed context tend to show that people’s decisions are based on property rights (Oxoby & Spraggon 2008), past contributions to collective actions (Cappelen et al. 2007; Frohlich et al. 2004), or a personal link of solidarity (Cronk 2007). One possible interpretation is that participants try to be fair with others when they distribute the money: If the other player has produced the money, she has more right over it; if she has been more productive or has invested more money, she deserves a bigger part of it; if both players are friends, they have special duties toward each other; and so forth. Such a “sense of fairness” combined with contextual differences might well explain the variety of results observed around the world. When confronted with cultural differences in experimental result, we should therefore ask: Are they the product of deep differences in the psychological dispositions and processes these experiments are intended to illuminate, or do they reflect differences in the interpretation of the experimental situation? One way to help answer this question would be, for instance, to present the Lamalera and the Machigenga with, as much as possible, the same rich context (e.g., clarifying the source of the money and the relationships between the participants) and assess whether they use the parameters at stake (i.e., rights, past contributions, social links) in the same way. The importance of the way participants interpret a task – which may differ from the way the experimenter intended them to interpret it – has been often stressed in experimental psychology (e.g., Sperber et al. 1995). The more the experiment is artificial and devoid of “ecological validity” – in other terms, the weirder it is – the greater the risk of misinterpreting the differences between societies. When it comes to cross-cultural comparisons, ignoring

this pragmatic dimension of participants’ performance may cause one to exaggerate or to miss genuine psychological differences.

Weirdness is in the eye of the beholder doi:10.1017/S0140525X1000004X Will M. Bennis and Douglas L. Medin Department of Psychology, Northwestern University, Evanston, IL 60208. [email protected] [email protected] http://www.wcas.northwestern.edu/psych/people/faculty/ faculty_individual_pages/Medin.htm

Abstract: Henrich et al.’s critical review demonstrating that psychology research is over-reliant on WEIRD samples is an important contribution to the field. Their stronger claim that “WEIRD subjects are particularly unusual” is less convincing, however. We argue that WEIRD people’s apparent distinct weirdness is a methodological side-effect of psychology’s over-reliance on WEIRD populations for developing its methods and theoretical constructs.

In their important article, Henrich et al. offer both weak and strong versions of an argument against the widespread use of research with WEIRD (Western, Educated, Industrialized, Rich, and Democratic) people as a means to learn about general human psychology. The weak version critiques the over-reliance on such samples and reviews an extensive body of literature across domains to establish that widespread cross-cultural differences exist for many of the psychological findings researchers have assumed were species universal. We are one hundred percent convinced of the weak argument and strongly endorse its attendant plea for moving beyond WEIRD samples. Their review is a major contribution to the literature, and we thank the authors for it. The strong version of the argument makes the additional point that WEIRD people are literally weird, atypical of humankind at large. On this account, it is the field’s ironic misfortune that of all samples to study, psychology should have picked this one. This strong argument is intriguing, and Henrich et al. present extensive evidence suggesting that this narrow slice of humanity indeed is a cultural outlier. For reasons that form the basis of our commentary, however, we remain skeptical with respect to this strong argument. The evidence for the distinctness of WEIRD samples comes from studies that generally take the following form: Findings originally conducted on the WEIRD population are assessed with a different population, and a different pattern of results emerges. When a broader range of groups is considered, the WEIRD population tends to be at the extreme in its responses. For example, Henrich et al. cite Segall and colleagues’ replication of the well-known Mu¨ller-Lyer illusion (Segall et al. 1996). Segall et al. find not only a wide distribution of the magnitude of the illusion across cultures, but also that the U.S. sample is the most extreme in magnitude (see their Fig. 1). Other phenomena they review demonstrate a similar trend. Base rates provide one clue that there might be something amiss with the argument that the group with which we are most intimately familiar is also the most distinctive. If there are a thousand potential samples, then the probability that the first selected is the most deviant is one out of a thousand. We think the apparent extremity of WEIRD populations can best be explained by two factors contributing to what we have called “the home-field disadvantage” – that is, the tendency for research developed in one’s “home-culture” and subsequently co-opted for cross-cultural comparison to result in one-sided conclusions about the nature of cross-cultural differences (Medin et al., under review). The first factor is the similarity between researcher and researched. Variations across cultures may reflect both adaptations to particular environments (e.g., Nisbett & Cohen 1996) and “niche BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? construction” (environmental adaptations that favor and reinforce cultural characteristics; Laland et al. 2000). For example, it may not matter whether people drive on the left or right side of roads; but once there is a consensus in a given culture, it is adaptive to conform to it. Researchers may have privileged insight in their own culture into what is important or what experimental manipulations are likely to achieve interesting and reliable results, and they may find it natural to study these sorts of things. But the very fact that the results are important, interesting, or reliable in one’s home culture makes it more likely that one’s culture represents an extreme with respect to those results (see Medin & Bang 2008). The other main factor reinforcing apparent extremes among WEIRD samples is their status as the originating research population. Research methods and theoretical constructs are calibrated to the populations they have been selected and designed for: in psychology’s case, WEIRD people. A side-effect is that these same tools are less well fit or even ill fit to other populations, in much the same way that any adaptation evolved for a particular niche will not function as well in other niches. For example, imagine a literature on sense of humor evolved from studies with undergraduates at major U.S. universities. Jokes that proved to be effective would tend to appear in later studies and ones that fell flat would tend to go by the wayside. If one then got the bright idea of doing a cross-cultural comparison, it may seem natural to use the same jokes favored by U.S. college students, with the more or less inevitable consequence that other populations wouldn’t find these jokes quite so funny, and the U.S. college sample would appear to be an extreme. Consider the Mu¨ller-Lyer illusion mentioned above and discussed in the target article. That particular illusion is a classic of Western psychology, taught in any introductory class discussing perceptual illusions. And it is taught because it is so readily demonstrated, a fact that reveals both general properties of the perceptual system and a response to the perceptual environment in which Westerners live. Small wonder that the effect is weaker in populations exposed to a different perceptual environment. Similarly, some novel perceptual illusion discovered in some other population is likely to be smaller in magnitude when tested with our WEIRD sample. But that is just our point – overwhelmingly, psychological research originates with the WEIRD sample and then is applied elsewhere – the converse pattern is rare. We believe that this habit of using research methods and theoretical constructs (stimuli, procedures, models, etc.) for cross-cultural comparisons that originated with WEIRD samples, coupled with insider information about what those WEIRD samples find important and which experimental manipulations are likely to achieve interesting and reliable results, may well account for the apparent extremity of the WEIRD population. Had psychology started with Chinese rice farmers studying members of their own community and then later their research protocols and theoretical constructs were exported for cross-cultural comparison and tested for universal validity, then, on our account, Chinese rice farmers would be the cultural outliers and WEIRD people would look more like everyone else.

Away from ethnocentrism and anthropocentrism: Towards a scientific understanding of “what makes us human” doi:10.1017/S0140525X10000051 Christophe Boesch Department of Primatology, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany. [email protected] http://www.eva.mpg.de/primat/staff/boesch/index.html

Abstract: The quest to understand “what makes us human” has been heading towards an impasse, when comparative psychology compares

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primarily individuals that are not representative of their species. Captives experience such divergent socioecological niches that they cannot stand for their wild counterparts. Only after removing ethnocentrism and anthropocentrism will we be able to progress in our understanding of “what makes us human.”

Henrich et al.’s review of cognitive differences among human cultures is very timely in reminding us that different living conditions have consequences for cognitive development. Not all humans are Westerners, and this is true also for their cognition. Here, I want to address how this affects our understanding of cognitive differences between humans and chimpanzees, and requires reconsideration of many claims about “what makes us human” (Boesch 2007; 2008). To become a science, comparative psychology will have to include population differences in its theoretical thinking and empirical approaches. Comparative psychology suffers from the same weaknesses as noted by Henrich et al. for psychology. Bold claims about “human uniqueness” are made based on the assumption that WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies’ humans (I referred to them as WMC, or Western middle class, humans; Boesch 2007) and captive chimpanzee populations are representative of each of the two species (Boesch 2007; de Waal 2001). Comparative psychology predominantly compared captive chimpanzees with free Western humans (see black arrow A in my Figure 1). The overwhelming conclusion of these studies was that humans clearly outperform chimpanzees in such different cognitive domains as folk physics, altruism, cooperation, theory of mind, and gaze following (e.g., Hermann et al. 2007; Povinelli 2000; Povinelli & Vonk 2003; Silk et al. 2005; Tomasello et al. 2005). However, three essential points that invalidate their conclusions have been too often forgotten in the interpretation of such studies. First, the characteristics of the animal populations included in these studies are typically not representative of their species. The Louisiana captive chimpanzee group that has been used extensively in a variety of widely cited cognitive studies (e.g., Povinelli 2000; Povinelli & Vonk 2003; Silk et al. 2005; Vonk et al. 2008) nicely illustrates this point (see Fig. 1). This group was created by putting together seven 2- to 3-year-old chimpanzees that were kept in isolation as a same-aged peers group all their lives, in a small, stable, and restricted man-made environment (see Povinelli 2000). Such a history cannot be more different from the one of young chimpanzees in the wild. Wild individuals live in large, flexible, fission-fusion groups, with 30 to 100 individuals of different

Figure 1 (Boesch). Schematic representation of the cognitive landscape in humans and chimpanzees as a function of the different socioecological niches that each species encounters. For each species, the possible range of cognitive performance is illustrated by an ellipse including all the individual population performances. Two types of cross-species comparisons are illustrated: The first one, the classical comparative psychology approach, compares two outlier populations for their species (black arrow A compares captive chimpanzees with WEIRD humans); and the second one compares populations of two species facing similar socioecological niches (white arrow B).

Commentary/Henrich et al.: The weirdest people in the world? ages, in kilometer-wide ranges where food has to be located and extracted, and where life-threatening neighbors and predators loom (e.g., Mitani et al. 2002). True, captive conditions range from highly ecologically deprived environments, typical for the early 1950s, to much more enriched conditions, as seen in modern settings. Moreover, social conditions vary from complete isolation to more social groups. However, all captive conditions differ from wild ones in that captives are forced to live in much smaller, stable social groups, in very passive environments where food is provided and no competition with others exists. The fields of developmental and social psychology have shown that such differences have important effects on cognitive development in humans (Carpendale & Lewis 2004; Nelson et al. 2007). No surprise that some captive chimpanzees have difficulties in understanding unseen relationships (Povinelli 2000), whereas wild chimpanzees transport stone hammers to distant, out-ofsight nut-producing trees (Boesch & Boesch 1984) and use tools to extract unseen underground resources (Boesch et al. 2009; Sanz et al. 2004). Similarly, some captives are unable to share food (Silk et al. 2005; Warneken & Tomasello 2006) or to work as a team with shared goals (Tomasello et al. 2005), whereas wild individuals share vast quantities of food with unrelated group members for extended periods of time and work as a close team when hunting prey, chasing leopards, or during risky intergroup encounters (e.g., Boesch & Boesch 1989; Boesch et al. 2008; 2010; Goodall 1986; Mitani & Watts 2005). Second, comparative psychology has favored experimental studies using anthropocentric designs and assumptions. These might allow testing human abilities in other species, but are unlikely to uncover cognitive abilities of nonhuman animals. For example, to understand the altruistic abilities of chimpanzees, experiments have been designed on the ethnocentric assumption that sharing should be preferred over nonsharing when there is no cost to oneself (Silk et al. 2005). However, sharing implies a contractual obligation in some human populations (Henrich et al. 2006; and present study in the target article); and, therefore, this assumption does not even hold in all human populations. Similarly, numerous experiments with captive chimpanzees used a WEIRD notion of causality. In gaze-following experiments, tested animals needed to understand that a human gaze indicated an honest positive interest (Hermann et al. 2007; Tomasello et al. 2005). In helping experiments, tested individuals had to understand that experimenters pretending to not master a task needed to be helped (Warneken & Tomasello 2006). Less ethnocentric and anthropocentric experiments would bring us a long way to understand other species. Third, by favoring experiments in captive settings, comparative psychology has opted for low ecological validity. For example, altruism in wild chimpanzees is expressed mainly in situations where a highly sought after food, meat, is shared with individuals that are socially important to the giver, either because they are hunting partners or social allies (Boesch 2009; Mitani & Watts 2001). Such a social dimension has rarely been considered in comparative experiments. Similarly, chimpanzees primarily cooperate during life-threatening situations, such as during intergroup fights or when predators are near, or to get meat (Boesch 2009; Goodall 1986). The difficulties with mimicking such situations in experiments have not prevented comparative psychologists from making strong claims about chimpanzees’ limitations (Herrmann et al. 2007; Povinelli 2000; Tomasello et al. 2005). Comparative psychologists’ inability to mimic natural cooperative conditions is not proof that cooperative ability is absent in other animal species. The quest to understand “what makes us human” has been heading towards an impasse. It will progress again once the socioecological diversity of humans and other species are considered. I am asking for greater care before making sweeping claims based on only a few captive individuals. Knowing that cognitive diversity is natural in species living in different socioecological conditions, we need to compare what is comparable (following

white arrow B in Figure 1). Only when this condition is met will our quest to understand “what makes us human” progress.

The WEIRD are even weirder than you think: Diversifying contexts is as important as diversifying samples doi:10.1017/S0140525X10000063 Stephen J. Ceci,a Dan M. Kahan,b and Donald Bramanc a

Department of Human Development, Cornell University, Ithaca, NY 14853; Yale Law School, Yale University, New Haven, CT 06520-8215; cGeorge Washington University Law School, Washington, DC 20052. [email protected] http://www.human.cornell.edu/che/bio.cfm?netid ¼ sjc9 [email protected] http://www.culturalcognition.net/kahan/ [email protected] http://www.culturalcognition.net/braman/ b

Abstract: We argue that Henrich et al. do not go far enough in their critique: Sample diversification, while important, will not lead to the detection of generalizable principles. For that it will be necessary to broaden the range of contexts in which data are gathered. We demonstrate the power of contexts to alter results even in the presence of sample diversification.

We commend Henrich et al. for pointing out the (over)reliance on participants from WEIRD societies and the tenuousness of universal laws whose support is based on a single subpopulation of a single society. Notwithstanding the merit of studying participants from diverse races, social classes, and national cultures, this will not in itself lead to generalizability of findings because it leaves unaddressed other threats to generalization, including the restricted physical, ideological, and attitudinal parameters of most research, and the omission of social meanings that participants attach to their choices. Even when researchers include non-WEIRD participants, they rarely include contextual variation. Few of our principles are based on data from diverse settings and conditions. On those occasions when researchers do insert contextual diversity into their designs, it becomes apparent that theorizing is paradigm-bound – confined to the specific physical, motivational, and psychological conditions under which the data were gathered. Below we argue for the power of manipulating the context and social meanings, independent of sample diversity. Motivational context. When researchers contrast paradigms across settings, stimuli, and/or conditions, the results sometimes fail to replicate. For example, Ceci and Bronfenbrenner (1991, cited in Ceci 1996) asked children to predict where on a monitor geometric shapes would migrate after children pressed the space bar. A curvilinear algorithm determined where each shape would migrate1: :8 sin (x) þ :6 sin (y) þ :4 sin (z) þ 5%error Even after 750 trials, children were still unable to predict the shapes’ migration. The implication is that multiplicative reasoning is beyond their capability. As shown in our Figure 1, however, when the identical algorithm controlled a video game in which the object was to predict the destination at which vehicles would meet a roadblock, children reached ceiling by 450 trials (Ceci 1996). Thus, behavior in ecologically challenging contexts led to findings at odds with those from socially sanitized settings. Semantic context. Much research on memory, reasoning, and moral development is based on stimuli expunged of meaningful associations (e.g., nonsense syllables) in the belief this will reveal underlying principles. For example, Wason’s deduction BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Figure 1 (Ceci et al.). out of Laboratory.

Curvilinear Distance Estimation in and

task has been used to illuminate qualities of thinking. However, even when participants were from other cultures, it was clear that deduction depended on the content. Consider: The logic underlying Wason’s task is that any inference that has the form: p or q not– p [q

is valid independent of the propositional content. However, it has been repeatedly shown this is not true and that content matters. Suppose four cards are laid out EK47

with the instructions that each card has a number on one side and a letter on the other. Participants are told: If a card has a vowel on one side, then it has an even number on the other side. Their job is to determine which cards must be turned over to validate the rule. Even smart undergraduates have success rates of only 12%. However, if the content is changed to a mode of transportation on one side of each card and a destination on the other Manchester Sheffield Train Car

and the rule is: Every time I go to Manchester, I go by train. Nothing has changed logically, but performance skyrockets to 60% (Johnson-Laird 1983). Similarly, we have demonstrated that differences in the way meaning is represented mediate children’s metacognition (Ceci et al. 2010). Cultural-cognitive context. Two of us (Kahan, Braman) have

demonstrated that Americans who vary in their attitudes toward hierarchy and equality vary in their perceptions of legally consequential facts (Kahan, in press; Kahan et al. 2009). These competing attitudes cohere with opposing sets of norms, and related scripts of acceptable behavior, which can trump the demographic variables emphasized by the WEIRD critique (Kahan et al. 2007). Social meaning context. Actions have meanings as well as consequences. They embody attitudes, the expression of which shapes actors’ perceptions of what they are doing and hence the value of doing it. For example, under what circumstances will an actor prefer the certainty of one sum to the probability of another? “When the expected value of the former exceeds the latter,” the classical microeconomist says, “subject to one’s degree of risk aversion,” which the behavioral economist notes will vary depending on whether one understands the exchange as realizing a “gain” as opposed to avoiding a “loss.” But

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experimental work shows the answer also depends on what such an exchange means. Most members of WEIRD societies will shun trades involving tax evasion that they might well have accepted in the form of casino wagers, because the former, even if equivalent to the latter along the dimensions specified by economists, manifests attitudes and values antithetical to the self-conception of (most) WEIRD people (Baldry 1986). Likewise, economists’ considerations – dominant payoff strategies, signaling, reciprocity – don’t tell us all we must know to predict whether individuals will contribute to public goods (Liberman et al. 2004). Even the bedrock axiom of economics – “demand curves slope downward” – founders on the shoals of meaning. Offering a wage can cause persons who previously did community service for free to refrain from doing any: payment destroys the meaning, and thus the associated value, of public-spirited behavior (Gneezy & Rustichini 2000). In a nutshell, ignoring meaning – as behavioral economics and related approaches do – generates unreliable predictions for any collection of persons who experience a common life. Heinrich et al. rightly question whether the behavioral dynamics in the work they examine generalize to non-WEIRD samples. But it is important not to assume this critique identifies some distinctive problem hovering at the “margins” of that work. Indeed, what does generalize about the new behavioral science is the question about its external validity: its neglect of meaning and other elements of context gives us reason to be cautious about accepting extrapolations from its stylized experiments. In sum, our argument is that although we agree with Henrich et al. on the need to diversify samples, this alone will not unearth generalizable principles because contexts, attitudes, and meanings contribute systematic variance that must be included to reveal lawful ecological contrasts. NOTE 1. The mapping function (over a quarter sine phase) was x1 y1 z1 ¼ random number (0–9), where x, y, z ¼ maxdistance þ 1.8 (0.8 sin x1 x .10 þ 0.60 sin y1 x .10 þ 0.4 sin z1 x .10).

The weirdest brains in the world doi:10.1017/S0140525X10000282 Joan Y. Chiao and Bobby K. Cheon Department of Psychology, Northwestern University, Evanston, IL 60208. [email protected] [email protected] http://culturalneuro.psych.northwestern.edu/Lab_Website/ Welcome.html

Abstract: Henrich et al. provide a compelling argument about a bias in the behavioral sciences to study human behavior primarily in WEIRD populations. Here we argue that brain scientists are susceptible to similar biases, sampling primarily from WEIRD populations; and we discuss recent evidence from cultural neuroscience demonstrating the importance and viability of investigating culture across multiple levels of analysis.

Henrich et al. provide a compelling argument regarding the error of assuming minimal variability across human populations in the behavioral sciences and the notion that people from WEIRD populations are actually unusual, even outliers, relative to the rest of the species. Here we argue that these problems of prematurely assuming universalism and experimentally sampling primarily from WEIRD populations extend beyond the behavioral sciences, into the brain sciences, and that researchers in both the behavioral and brain sciences may simultaneously benefit from reorganizing research infrastructures to promote the study of diverse cultural comparisons across multiple levels of analysis.

Commentary/Henrich et al.: The weirdest people in the world? Brain scientists generalize from a narrow sample to the species. Akin to the behavioral sciences, brain sciences typically

sample from a thin slice of the species. Within the field of psychology, 95% of psychological samples come from countries with only 12% of the world’s population (Arnett 2008). Similarly, within the field of human neuroimaging, 90% of peer-reviewed neuroimaging studies come from Western countries (Chiao 2009). Several factors have contributed to the current WEIRD sampling bias in the brain sciences. First, human neuroscience research programs typically build on either empirical questions inspired by animal models, or case studies of brain damaged patients, or theories from evolutionary psychology. Each of these three starting points for neuroscience research carries implicit assumptions of minimal variability across human populations. Second, researchers have lacked the technology to study culture at the neural level in humans, as human neuroimaging methods have become available only within the past three decades and are still not available in many non-Western regions of the world. The use of neuroimaging is often prohibitively expensive, making it easier for richer, politically stable countries, such as Western industrialized nations, to create the powerful societal infrastructures necessary for novel and timely neuroscientific discovery. The infrastructural advantages afforded to neuroscientists from WEIRD nations create researcher biases in the field that are cyclical and that compound over time as researchers create new experiments and seek empirical evidence consistent with theory and predictions based on prior findings. Hence, our current state of knowledge of mind-brain mappings is largely restricted to scientific observations made from people living within WEIRD nations, leaving a large empirical gap in our understanding of how diverse cultures affect mind, brain, and behavior. It is not safe for brain scientists to generalize from a narrow sample to the species. In the target article, the authors identify

key cultural comparisons that reveal behavioral differences across cultures, including industrialized versus small-scale societies, Western versus non-Western cultures, contemporary American versus non-American Westerners, and contemporary Americans versus other Americans. To the extent that behavior arises from neurobiological processes, it is plausible that this

set of cultural comparisons would similarly reveal cultural differences in neural functioning. Indeed, early efforts by cultural neuroscientists to address the question of how culture influences brain function have proven fruitful, particularly for understanding differences in neurobiological processing between Westerners and East Asians. Westerners engage brain regions associated with object processing to a greater extent relative to East Asians, who are less likely to focus exclusively on objects within a complex visual scene (Gutchess et al. 2006). Westerners show differences in medial prefrontal activity when thinking about themselves relative to close others, but East Asians do not (Zhu et al. 2007). Activations in frontal and parietal regions associated with attentional control show greater response when Westerners and East Asians are engaged in culturally preferred judgments (Hedden et al. 2008). Even evolutionarily ancient limbic regions, such as the human amygdala, respond preferentially to fearful faces of one’s own cultural group (Chiao et al. 2008, Fig. 1). Taken together, these findings show cultural differences in brain functioning across a wide variety of psychological domains and demonstrate the importance of comparing, rather than generalizing, between Westerners and East Asians at a neural level. What about cultural differences in brain function beyond comparisons of Westerners and East Asians? Critically, brain scientists have yet to explore the neurobiological ramifications of cultural contrasts beyond Westerners and East Asians. No research to date has explored comparisons in neural functioning of individuals living in small-scale versus industrialized societies. Additionally, although neuroimaging data do exist for comparing the neural functioning of contemporary Americans with that of other Westerners, such as Europeans, no study to our knowledge has yet explored in a hypothesis-driven manner the possibility that brain – behavior relations may vary as a function of the kind of Western subculture. Social neuroscientists have recently begun to address how childhood socioeconomic status affects brain functioning (Hackman & Farah 2009); however, the effect of education level (e.g., college vs. no college education) on neural functioning remains unknown. Hence, the current state of knowledge in cultural neuroscience remains woefully behind that of cultural psychology. Yet, the knowledge to date supports the notion that it is not always safe for brain scientists

Figure 1 (Chiao & Cheon). Greater bilateral amygdala response to own-culture relative to other-culture fear faces (from Chiao et al. 2008). BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? to generalize from a narrow sample to the species and that restricting neuroscientific study to WEIRD populations will not be sufficient for building a comprehensive understanding of human nature. Behavioral scientists and brain scientists alike will mutually benefit from studying non-WEIRD populations. The journey

towards understanding how culture affects human nature is one that behavioral and brain scientists can readily embark on together, and likely with mutual benefits. For instance, where do these cultural differences in brain and behavior come from? One possibility is that psychological and neural diversity comes from differential experience specific to a given culture. Another possibility is that diversity in brain and behavior results from culture-gene coevolution, whereby culture coevolves with genes that regulate endophenotypes, such as brain and behavior. Testing both explanations requires empirical work that examines multiple levels of analysis (e.g., gene, brain, mind, culture) across multiple time scales (e.g., situation, ontogeny, phylogeny). It is humbling to think of the sheer volume of work that lies ahead to address these timely questions, but by working together, behavioral and brain scientists can offer each other a more complete cultural science than either can construct alone.

Diversity in representations; uniformity in learning doi:10.1017/S0140525X10000075 David Danks and David Rose Department of Philosophy, Carnegie Mellon University and Institute for Human and Machine Cognition, Pittsburgh, PA 15213. [email protected] [email protected] http://www.hss.cmu.edu/philosophy/faculty-danks.php

Abstract: Henrich et al.’s conclusion that psychologists ought not assume uniformity of psychological phenomena depends on their descriptive claim that there is no pattern to the great diversity in psychological phenomena. We argue that there is a pattern: uniformity of learning processes (broadly construed), and diversity of (some) mental contents (broadly construed).

Henrich et al. argue – correctly, in our view – that there is great diversity both across and within domains of human cognition. They then argue for the stronger claim that there is no underlying rhyme or reason for this diversity. This latter claim grounds their central methodological point: For all (interesting and not obviously culturally induced) cognitive phenomena, psychologists ought not automatically conclude, from samples of Western undergraduates, that any particular cognitive phenomenon is uniform across people. This methodological conclusion obviously depends crucially on the stronger descriptive claim. If a pattern of uniformity emerged with respect to a particular type of cognitive phenomenon, then psychologists could justifiably generalize (in only those particular domains, of course). We argue here that such a pattern does emerge, if one carves up the space of cognitive phenomena appropriately. More precisely, we suggest that there is diversity in cognitive “contents” (broadly construed), but uniformity in learning “processes” (broadly construed). Cognitive “phenomena” do not form a unitary type, as there is a natural, defensible distinction between the cognitive “objects” of the mind, and dynamic mental “processes.” Cognitive objects include representations, knowledge structures, and so on. Mental processes influence both those cognitive objects, and also other processes. As one example of this distinction, there is a natural difference between a concept and the processes by which one learns that concept. Concepts are particular representations that can be deployed in a range of circumstances; concept learning processes are the means by which those representations emerge. Importantly, the word process in our argument should explicitly

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not be read as a synonym for “specific causal mechanism”; the distinction between a process and its “target” requires only a thin notion of “process” (i.e., something like “if-then” rules, not a specific neural mechanism). Furthermore, our argument does not depend on the particular ways in which learning processes are instantiated in different individuals, as long as those processes have the same computational/psychological profile. We have no doubt that Henrich et al. are correct that there is great diversity in representations, including people’s folk-biological concepts, notions of “fairness” and “agency,” and moral concepts. It is unsurprising that people who develop in different environments – physical, mental, or social – develop different representations of those environments. But diversity in representation does not imply diversity in learning processes. For example, diversity in our learned concepts is arguably due to differences in inputs, not differences in concept learning processes. After all, the whole point of learning is that the same process can produce very different outputs depending on the environment, and thereby enable the organism to adapt to the demands of an unpredictable or ever-changing environment. There are thus prima facie reasons – genetic and evolutionary – to think that there is likely to be uniformity in learning processes. Of course, part of the point of Henrich et al.’s article is that we ought not rest with the assertion that “there really ought to be uniformity in learning processes”; instead, it is an empirical matter as to whether there actually is such uniformity. Determining whether learning processes are uniform across both WEIRD and non-WEIRD individuals requires discovering the learning processes in each individual, which is inevitably a tricky matter. We can rarely (if ever) directly observe a learning process; instead, we must find its characteristics by determining how the relevant cognitive objects change in response to different inputs (either within- or between-participants). Despite these difficulties, it is nonetheless often possible to learn about different individuals’ learning processes. Unfortunately, as Henrich et al.’s descriptive survey shows, there have been relatively few cross-cultural studies of learning processes, as opposed to representations or (relatively) automatic processing. By our reading, essentially all of the phenomena that Henrich et al. discuss are representations/contents, rather than learning processes. There are many psychological studies of learning processes, but almost entirely restricted to WEIRD participants. We must therefore look to more indirect evidence. It is suggestive that many of the cases of cognitive uniformity that Henrich et al. identify are also cases in which the environment is plausibly (relatively) uniform. On the “uniformity in learning processes” view we advocate, one would predict this sort of representational uniformity just when there is relative uniformity in the relevant environment. For example, it is (in our view) unsurprising that the ability to pass the False Belief Task emerges across cultures, since (presumably) all developing individuals have social environments that require interaction with (and prediction of the behavior of) individuals with false beliefs. Moreover, Henrich et al. themselves seem to assume uniformity of learning processes in some of their explanations for diversity. For example, they seem to endorse an explanation of diversity in the Mu¨ller-Lyer illusion in terms of variation in the visual environments of developing individuals. That is, Henrich et al. seem to argue that differences in adult visual representations are due to differences in infant/child visual environments. But implicit in this argument is an assumption that there is an “infant environment ! adult visual representation” mapping that is shared between both WEIRD and non-WEIRD individuals. More generally, all explanations of diversity in terms of environmental variation presuppose that there is relative uniformity in learning processes (in our thin sense). Explanations based in environmental variation require some shared process by which that variation can lead to behavioral or cognitive differences. If there is no shared learning process, then an appeal to environmental differences is simply a non sequitur.

Commentary/Henrich et al.: The weirdest people in the world? Learning processes (in a thin sense) are different from the objects they influence, and this distinction can provide a basis for when to expect uniformity and diversity in human cognition. We suggest that uniformity should be expected for learning processes, and diversity should be expected for objects whenever the relevant learning environments differ (and there are no relevant, direct selection pressures on the objects). We do not doubt that people are more diverse than contemporary cognitive psychology admits, and Henrich et al. do a great service in providing a descriptive characterization of diversity. They simply paint with too broad a methodological brush. ACKNOWLEDGMENT David Danks is partially supported by a James S. McDonnell Foundation Scholar Award.

Getting beyond the “convenience sample” in research on early cognitive development doi:10.1017/S0140525X10000294 Anne Fernald Department of Psychology, Stanford University, Stanford, CA 94305. [email protected]

Abstract: Research on the early development of fundamental cognitive and language capacities has focused almost exclusively on infants from middle-class families, excluding children living in poverty who may experience less cognitive stimulation in the first years of life. Ignoring such differences limits our ability to discover the potentially powerful contributions of environmental support to the ontogeny of cognitive and language abilities.

Arnett (2008) blames the narrowness of American behavioral research on a philosophy of science that focuses on universals in human cognitive and social psychology, ignoring variability and the factors that contribute to differences among people. Henrich et al. share the concern that researchers in these areas simply assume their findings are universal, but suggest that studies with children may provide more convincing evidence. I do not think that developmental psychologists should be let off the hook so easily. If the WEIRD (Western, Educated, Rich, Industrialized, and Democratic) sample that is studied so extensively in psychological research with adults consists of a privileged subset of 5% of the world’s population, then the children represented in the burgeoning literatures on “core knowledge” (Spelke & Kinzler 2007) and early language acquisition (Fernald & Marchman 2006) are drawn from an even smaller sliver of affluent and highly educated families. Parents with the time, resources, and motivation to bring their infant to participate in a developmental study at a university laboratory are demographically even less diverse than the college students who predominate in studies with adults. Why does this matter? Because differences in socioeconomic status (SES) are robustly associated with the quantity and quality of early cognitive stimulation available to infants, and early cognitive stimulation really does matter. Sixty years of developmental research show that parenting practices in infancy mediate links between SES and long-term cognitive outcomes (Hoff 2003; Milner 1951). Yet the hundreds of experiments in recent years exploring basic cognitive capacities at younger and younger ages have almost all focused on middle-class participants. At the 2010 International Conference on Infant Studies, less than 1% of the 1,000 research presentations reported including participants from disadvantaged families, although 20 – 40% of children in the United States are growing up in poverty (Wight et al. 2010). If the same studies conducted in all those university research centers were also run with infants in the lower-income neighborhoods that are often just a few miles from campus, the results would likely be different. We know, for example, that the

development of spatial abilities presumed to be species-specific is compromised in low-SES children, who have less opportunity to exercise spatial skills than do high-SES children who have access to toys, puzzles, bikes, and the freedom to explore a safe neighborhood (Levine et al. 2005). Yet developmental textbooks abound with claims about how “infants’ awareness of physical principles is evident at 3 months,” or how “infants use knowledge of phonotactics to segment words by 7.5 months.” Such statements may be true of the particular infants observed in the particular studies cited, but the results are often framed more broadly, as if these specific ages characterize human infants universally and differences in early experience are simply irrelevant. Would it matter if we discovered that these age-specific developmental milestones are in fact only characteristic of infants in middle-class families? If we found that infants living in poverty are actually one or several months slower than higher-SES infants to show evidence of “core knowledge of spatial relations” or “speech segmentation ability”? It should matter, because to ignore such differences is to ignore the potential role of environmental support in the ontogeny of these critical capacities. In our longitudinal research on the early development of fluency in language understanding, we have found robust relations between verbal processing speed in infancy and long-term outcomes in both high-SES English-learning children and low-SES Spanish-learning children. In both groups, infants who are faster in speech processing at 18 months are more advanced on later cognitive and language measures (Fernald et al. 2006; Hurtado et al. 2007). But the differences in performance between these groups are stunning. By 18 months, we find that low-SES children are already substantially slower in processing speed and vocabulary growth; and by the age of 5 years, we see the gap in developmental measures found in numerous studies since the 1960s (Ramey & Ramey 2004). This inconvenient truth has forced us to re-evaluate the assumption that our earlier research with children of affluent families licensed broad conclusions about the “speech processing abilities of 18- to 36-month-olds” in general, given that perfectly healthy 18- to 36month-olds from low-income families in the neighboring community performed so differently on the same tasks. But these findings have also led us to ask a question we had previously ignored: Could it be that differences in early experience with language contribute to the variability observed in children’s efficiency in real-time processing? It turns out that early practice with language is influential in the development of fluency in understanding. In a study with low-SES families, we found that those children whose mothers talked with them more learned vocabulary more quickly – and they also made more rapid gains in processing speed (Hurtado et al. 2008). These results suggest that child-directed talk not only enables faster learning of new vocabulary – it also sharpens the processing skills used in real-time interpretation of familiar words in unfamiliar contexts, with cascading advantages for subsequent learning. By examining variability both within and between groups of children who differ in their early experience with language, we gained insight into common developmental trajectories of lexical growth in relation to increasing processing efficiency, and also discovered environmental factors that may enable some children to progress more rapidly than others. Pinker (1994) once declared that “to a scientist interested in how complex biological systems work, differences between individuals are so boring!” In fact, many biologists these days are keenly interested in environmental influences on expression of the genetic code during early development and the resulting phenotypic differences (Gottlieb 2007; Zhang & Meaney 2010). New research on prenatal programming shows that fast- or slowgrowth trajectories set before birth have long-term developmental consequences for health and vulnerability (Coe & Lubach 2008). Developmental psychologists can now also address important questions about the crucial influence of early postnatal experience on cognition and language. But to do so we need to extend beyond the WEIRD “convenience samples” we have traditionally relied BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? on, to examine trajectories of growth in broader populations of children living in more diverse circumstances.

Cultural congruence between investigators and participants masks the unknown unknowns: Shame research as an example doi:10.1017/S0140525X10000087 Daniel M. T. Fessler Department of Anthropology and Center for Behavior, Evolution, and Culture, University of California, Los Angeles, CA 90095-1553. [email protected] http://www.sscnet.ucla.edu/anthro/ faculty/fessler/

Abstract: In addition to questions of the representativeness of Western, educated samples vis-a`-vis the rest of humanity, the prevailing practice of studying individuals who are culturally similar to the investigator entails the problem that key features of the phenomena under investigation may often go unrecognized. This will occur when investigators implicitly rely on folk models that they share with their participants.

Henrich et al. present a compelling empirical case for a reduction in the current reliance on participants from WEIRD societies in the study of the human mind. Awareness of these facts should motivate investigators both to recognize the need for caution in inferring the generalizability of results derived from parochial samples and to pursue more diverse samples, whether directly or through collaborative networks. Here I wish to suggest that additional impetus for such changes derives not simply from questions of representativeness, but rather from the fact that investigators themselves inevitably view the world through the lens of their own culture. As Henrich et al. observe, most psychological research is conducted by members of WEIRD societies, hence investigators are largely studying people very similar to themselves. By virtue of the fact that they, too, are enculturated humans, investigators will often share folk psychological models with participants drawn from their own cultural group. Although rigorous science is always based on the refinement of models through hypothesis testing, one has to begin somewhere, and I strongly suspect that social scientists in general, and psychologists in particular, often rely on their own folk models as a starting point in this process. When investigators share fundamental cultural commonalities with their participants, they run the risk of overlooking key features of the psychological phenomena at issue, as such features may be absent from, or downplayed by, the given folk models (see Levy 1973). In light of the above, it is interesting to consider a domain of research only addressed in passing by Henrich et al., namely, the study of emotions. Due, in part, to the centrality of claims regarding innateness in a number of seminal modern investigations of emotion, building on Darwin’s (1872) prior work on the subject, the study of emotion has long included a substantial cross-cultural component. From early on, cross-cultural research was employed to explore both the recognizability of emotional expressions (e.g., Ekman & Friesen 1971) and the qualia and elicitors associated with different emotions (e.g., Wallbott & Scherer 1986). Despite this emphasis, the relative neglect by prominent universalists of the importance of cultural meaning systems in the experience of emotion eventually led to bold claims by some cultural constructionists (e.g., Lutz 1988), wherein a panhuman psychology was seen as merely the seed from which radically diverse parochial forms of emotion spring. As the field matured further, polar differences between nativists and constructivists eventually dulled, with various middle grounds being found, wherein investigators sought to systematically examine the nature and extent of cultural variation in the elicitors of emotions, the non-emotion correlates of cultural variation in emotional experience, and similar facets (see, e.g., Kitayama & Markus 1994; Mesquita & Frijda 1992).

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While the psychology of emotion would thus seem to be one area where the call to action trumpeted by Henrich et al. has already long been heard, even here one can find signs of an underestimation of the importance of moving beyond cultural similarities between investigator and participant. For example, a burgeoning literature (a keyword search in PsycINFO produces almost 6,000 hits) explores shame. This literature focuses on self-consciousness and issues of moral and personal worth, often contrasting shame with guilt (see Tangney & Dearing 2002). However, results obtained from a small-scale non-WEIRD society, and hints provided by many non-Western languages’ emotion lexicons, suggest that, in addition to the aforementioned features, for many of the world’s peoples, the emotion of shame also encompasses what English speakers would call respect and fear, facets that primarily concern subordinance in a hierarchy rather than failure to conform to social standards (Fessler 2004). Although the subordinance aspect of shame is absent from the vast majority of the voluminous scholarly work on the subject, with guidance, English-speaking participants generally recognize this facet of shame in their own experiences. Importantly, however, they do not volunteer this association on their own. This is not surprising, given that Western folk models of shame entirely ignore the experience of subordinance, perhaps because this experience is arguably antithetical to the values common in WEIRD groups. This leads to my central question, namely, why have investigators not attended more to the subordinance facet of shame? I suspect that the problem is not merely that the participants studied by many students of shame differ in important ways from most of the world’s peoples, but also that the investigators studying those participants consistently find strong support for their own intuitions, and hence see less need to cast a wide net despite the history of cross-cultural research on emotions. Whereas early work on emotions included places such as New Guinea villages (Ekman & Friesen 1971), and recent work on pride, directly relevant to understanding shame, has been conducted in rural Burkina Faso (Tracy & Robins 2008), much current work on shame is confined to educated and affluent members of the state-level societies of North America, Western Europe, and, to a lesser extent, East Asia. Importantly, failure to recognize those aspects of shame that are downplayed or ignored in their own culture limits investigators’ ability to explain key features of the phenomena of interest. For example, the behavioral tendencies for flight and hiding so prominent in the experience of shame seem odd in the context of a moral emotion, but are readily understood once it is recognized that this moral emotion is built upon a simpler emotion active in the dangerous context of dominance negotiations (see Fessler 2007). The take-home lesson here is that we must be particularly cautious to avoid employing participants who are culturally similar to ourselves whenever the given enterprise involves testing predictions that resonate with our intuitions. Such samples are unlikely to reveal to us that which we do not know that we do not know, features that are masked by our own folk models.

(Dis)advantages of student subjects: What is your research question? doi:10.1017/S0140525X10000099 Simon Ga¨chter School of Economics, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom. [email protected] http://www.nottingham.ac.uk/Economics/people/simon.gaechter

Abstract: I argue that the right choice of subject pool is intimately linked to the research question. At least within economics, students are often the perfect subject pool for answering some fundamental research questions.

Commentary/Henrich et al.: The weirdest people in the world? Student subject pools can provide an invaluable benchmark for investigating generalizability across different social groups or cultures.

In their excellent article, Henrich et al. rightly caution us to be careful when we draw general conclusions from WEIRD subject pools, of which undergraduates are the most frequently used one, also in economics. My main comment is that the right choice of subject pool is intimately linked to the research question. Since the different behavioral sciences also have different research questions, the right choice of subject pool will also often be different across disciplines. In my own discipline, economics, students are actually often the best subject pool for quite a few (fundamental) research questions. Here is why I believe so. Economic theories normally do not come with assumptions (or even caveats) about the restricted validity to only a specific group of people; that is, they (implicitly) assume “generality.” Like the assumption of selfishness, “generality” is a good assumption in the absence of rigorous data. The tools of experimental economics have been deployed to investigate the empirical relevance of the selfishness assumption (see, e.g., Fehr et al. 2002) and are now also used to probe the “generality assumption,” that is, the importance of variations of behavior across population subgroups within a given society (e.g., Bellemare et al. 2008) or across societies (e.g., Herrmann et al. 2008). However, my main point is this: The “right choice” of subject pool depends on the research question. If the researcher is interested in understanding behavioral variation between particular groups of people, then the right choice is running experiments with these people. The landmark study by Henrich et al. (2005) is a shining example. Yet, at least in economics, substantial effort is also devoted to test formal theories or to detect interesting behavioral regularities (Bardsley et al. 2010; Croson & Ga¨chter 2010; Smith 2010). Because economic theories normally assume generality, any subject pool is in principle informative about whether theoretical predictions or assumptions contain behavioral validity. At that stage, generalizability to other subject pools is not (yet) an issue. Among the universe of potential subject pools to test a theory, students are often the perfect one: on average, students are educated, intelligent, and used to learning. These are very valuable characteristics because, in addition to the main aspect of a theory of interest to the researcher, economic theories often assume cognitive sophistication. It therefore makes sense to control for sophistication also by choice of subject pool (in addition to clear instructions), in order to minimize chances of confounding genuine behavioral reactions to the treatment of interest with lack of understanding of the basic decision situation. Take recent theories of social preferences (as surveyed, e.g., in Fehr & Schmidt 2006) as an example. In addition to otherregarding preferences, these theories all assume cognitive sophistication. When testing these theories, the main point of interest is not to find out whether people are as cognitively sophisticated as the theories (maybe wrongly) assume, but to see to what extent other-regarding motives exist, holding everything else constant. Because students are typically above average with regard to cognitive sophistication, they are often a perfect subject pool for first tests of a theory. Moreover, students, unlike most other subject pools, are readily available (and cost effective). Experiments can therefore also easily be replicated, which is important to establish empirical regularity and hard to achieve with any other subject pool. Of course, strictly speaking, observed results hold only for the subject pool from which evidence is collected. Generalizability is a generic issue in any empirical research (Falk & Heckman 2009). However, once a clear benchmark result is established, we can proceed by testing, for example, how age and life experience matter (e.g., Sutter & Kocher 2007b), or how results extend to more representative subject pools (e.g., Bellemare et al. 2008; Carpenter et al. 2008). Along the way, researchers often establish whether and how students differ from the general population.

As Henrich et al. point out, understanding the potential influence of cross-societal (or cultural) differences in (economic) behavior is a particularly interesting direction for investigating generalizability. But it poses further challenges, in particular if socio-demographic factors matter (as some of the above-cited research suggests). The reason is that socio-demographic influences might be confounded with genuine societal or cultural differences. The problem is exacerbated the more subject pools are actually being compared. Again, to ensure that confounds are minimized, student subject pools are often the best available choice (Bohnet et al. 2008; Herrmann et al. 2008) to establish a clean benchmark result on how people from different societal/ cultural backgrounds behave in the exact same decision situation – a fundamental question from the generality perspective of economics. The benchmark can – and should(!) – then be taken as a starting point for investigating generalizability to other social groups.

It’s not WEIRD, it’s WRONG: When Researchers Overlook uNderlying Genotypes, they will not detect universal processes doi:10.1017/S0140525X10000105 Lowell Gaertner,a Constantine Sedikides,b Huajian Cai,c and Jonathon D. Brownd a

Department of Psychology, University of Tennessee, Knoxville, TN 379960900; bCenter for Research on Self and Identity, School of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom; c Institute of Psychology, Chinese Academy of Sciences, Chaoyang District, Beijing 100101, China; dDepartment of Psychology, University of Washington, Seattle, WA 98195-1525. [email protected] [email protected] [email protected] [email protected]

Abstract: We dispute Henrich et al.’s analysis of cultural differences at the level of a narrow behavioral-expression for assessing a universalist argument. When Researchers Overlook uNderlying Genotypes (WRONG), they fail to detect universal processes that generate observed differences in expression. We reify this position with our own cross-cultural research on self-enhancement and self-esteem.

We dispute the level of analysis Henrich et al. have employed to conclude that members of Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies are non-representative of the human species and to determine, more generally, whether an observed cultural difference contradicts a universalist argument. Borrowing from the biological lexicon, our position is as follows: Analysis of difference at the level of a narrow phenotypic behavioral-expression precludes detection of human universals that operate at the level of an abstract genotypic process. Stated otherwise, When Researchers Overlook uNderlying Genotypes (our acronym WRONG), they will fail to detect universal processes that generate observed differences in expression (Kobayashi & Brown 2003). We first frame our position with an example and then reify our position with our own cross-cultural research on self-enhancement and self-esteem – phenomena from which Henrich et al. derived their WEIRD conclusion. The human diet exemplifies our position (Sedikides & Gregg 2008). When considered at the narrow level of observed behavior, human societies appear extraordinarily different in regard to what they eat (e.g., a Kosher diet precludes pork; a Jain diet is vegetarian). When considered more broadly, however, the diverse diets are connected and assimilated by a universal need for sustenance. It would be faulty indeed to conclude that the need for sustenance is less pronounced, if not absent, in one BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? society because it consumes less, if not any, of the foods consumed by another society – what is consumed depends on factors such as climate and custom. Our point, of course, is that human universals operate at the abstract level of processand-function, and the expression of the universal emerges in conjunction with contextual considerations (Schlenker 1974). To be clear, we do not oppose the study of concrete behavior. Cataloguing behavioral differences across societies certainly contributes to understanding the human condition. However, the presence of a behavioral difference per se is not evidence contrary to a universalist argument. The necessary consideration is whether the observed difference is produced by a process or function common across societies. Henrich et al. suggest that WEIRD societies are peculiar, in part, because they uniquely possess positive self-views. Such a conclusion, however, is a consequence of the WRONG strategy. Our own cross-cultural programs of research on the self-enhancement motive (i.e., need to maintain a positive sense of self) and self-esteem (i.e., an affective self-evaluation) indicate that a positive self-view is a human tendency. Our primary studies and meta-analytic syntheses indicate that both Westerners and Easterners self-enhance, but they do so on different attribute dimensions.1 Westerners self-enhance (i.e., deem self as superior to peers) on attributes relevant to individualism, and Easterners self-enhance on attributes relevant to collectivism. This is because Westerners deem individualism, and Easterners deem collectivism, as important. Here a common process (self-enhancing on important attributes) is differentially expressed (individualism vs. collectivism), because culture affects the expression, not the presence, of the enhancement motive (Brown & Kobayashi 2002; Sedikides et al. 2003; Sedikides et al. 2005; 2007a; 2007b). Furthermore, that common process has the same functional association with psychological adjustment in both cultures: Self-enhancing on important attributes promotes better adjustment (e.g., greater well-being, less depression,) among Easterners and Westerners (Gaertner et al. 2008; Kobayashi & Brown 2003; O’Mara et al. 2009). Therefore, when assessed at the abstract level of process and function, members of WEIRD societies are quite normal in their striving for a positive self-view. As Henrich et al. suggest, Eastern samples typically provide lower explicit reports of self-esteem than do Western samples (Heine et al. 1999). Such explicit reports, however, are compromised by a pervasive modesty norm in Eastern cultures (Brown, in press; Kurman 2003). Indeed, the cultural differences occur in reports of cognitive self-evaluation, not affective self-regard, and these differences vanish when modesty is statistically controlled (Cai et al. 2007). Similarly, cultural differences in self-esteem vanish when self-esteem is assessed with implicit measures that circumvent modesty norms (e.g., Yamaguchi et al. 2007). Furthermore, self-esteem reveals the same functional patterns across cultures. Self-esteem predicts greater well-being and lower depression in the East (Cai et al. 2009), just as it does in the West (Taylor & Brown 1988). Likewise, self-esteem bolsters against threats to self-worth in both cultures such that failure feedback more strongly erodes immediate feelings of worth for low rather than high self-esteem persons (Brown et al. 2009). Hence, when assessed at the abstract level of process and function, members of WEIRD societies are quite normal in their possession of a positive-self view. In summary, testing human universals at the level of narrow behavioral differences between societies is the WRONG strategy. Human universals operate at the abstract level of process and function, and such universals can generate observed differences. We conclude with application of our argument to the man-to-boy insemination rituals of New Guinea with which Henrich et al. began their article. At the narrow level of the observed behavior, the rituals seem bizarre in regard to WEIRD standards. When considered more abstractly in terms of process or function (i.e., a social practice marking a boy’s

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passage to manhood), the rituals connect with coming-of-age rituals practiced in other societies, such as the Bar and Bat Mitzvah, Credo-baptism, Debutante Ball, and Sweet Sixteen. The observed behaviors certainly differ, but the underlying psychological process is the same. NOTE 1. Here and in the following, we use the terms Eastern and Western for expedience in reference to samples from East Asia versus samples from the United States, Canada, and Western Europe.

Wired but not WEIRD: The promise of the Internet in reaching more diverse samples doi:10.1017/S0140525X10000300 Samuel D. Gosling,a Carson J. Sandy,a Oliver P. John,b and Jeff Potterc a

Department of Psychology, University of Texas, Austin, TX 78712; Department of Psychology, University of California, Berkeley, CA 94720; c Atof Inc., Cambridge, MA 02139. [email protected] www.samgosling.com [email protected] www.carsonsandy.com [email protected] http://www.ocf.berkeley.edu/ johnlab/ [email protected] http://research.outofservice.com/ b

Abstract: Can the Internet reach beyond the U. S. college samples predominant in social science research? A sample of 564,502 participants completed a personality questionnaire online. We found that 19% were not from advanced economies; 20% were from nonWestern societies; 35% of the Western-society sample were not from the United States; and 66% of the U. S. sample were not in the 18–22 (college) age group.

Henrich et al. show that the vast majority of research in the behavioral sciences continues to be based on populations the authors call WEIRD because they are unlikely to be representative of humankind. Even more alarmingly, much of the research published in top-tier journals is not even representative of the populations in WEIRD (Western, Educated, Industrialized, Rich, and Democratic) countries. For example, in the 510 samples published in the Journal of Personality and Social Psychology (JPSP) in 2002, 85% of them were student samples, 71% of the participants were female, more than 80% were white, and the mean age was 22.9 years (Gosling et al. 2004). What should we do about this? Henrich et al. conclude their article by urging institutions to improve the infrastructure for collecting data from non-WEIRD samples as well as the incentives for studying them. However, Henrich et al. offer very little in the way of concrete practical suggestions for expanding the reach of research in the behavioral sciences. We propose that the Internet holds great promise for broadening the participant base of research in the behavioral sciences (Gosling & Johnson 2010; Reis & Gosling 2010). Using the Internet, researchers can deliver to participants a broad range of graphics, photographs, and dynamic media (Krantz & Williams 2010); obtain informant reports (Vazire 2010); and administer surveys (Tuten 2010), questionnaires (Johnson 2010), ability tests (Schroeders et al. 2010), and experiments (Reips & Krantz 2010). Participants can be randomly assigned to experimental conditions, reaction times can be measured, and a broad range of incentives for participation can be offered (Go¨ritz 2010). Internet methods offer researchers many advantages over traditional methods in terms of improved efficiency, accuracy, cost effectiveness, and reach (Gosling & Johnson 2010; Gosling et al. 2004; Reis & Gosling 2010). But how do Internet samples fare regarding Henrich et al.’s concerns about generalizability and representativeness? We do know that Internet samples are

Commentary/Henrich et al.: The weirdest people in the world? generally more diverse than the “traditional” samples in the top psychology journals with respect to gender, socioeconomic status, geographic region, and age (Gosling et al. 2004). But do such samples offer any hope of reaching the kinds of participants that Henrich et al. rightly note are so under-represented in the behavioral sciences? To find out, we examined a dataset of personality questionnaires collected in Dutch, English, German, or Spanish via the Internet in 2009 (for details of the method, see Srivastava et al. 2003). We examined only those 564,502 participants (81%) who had indicated the country in which they resided and were aged between 9 and 90. We followed the four contrasts used by Henrich et al.: (1) people from modern industrialized societies versus small-scale societies; (2) people from Western societies versus non-Western industrialized societies; (3) people from the United States versus other Western societies; and (4) university-educated versus nonuniversity-educated people from the United States. With respect to the first comparison, our sample almost certainly fails to capture individuals living in small-scale societies, which is unsurprising given the written format and the languages used. So to provide some insight into the extent to which Internet samples capture participants beyond modern industrialized societies, we examined the percentage of our participants who were from the 34 countries classified by the International Monetary Fund (IMF) as “advanced economies” (WEO Groups and Aggregates Information 2009): 19% (N ¼ 104,928) were not from these advanced economies. With respect to the second contrast, we found that 20% (N ¼ 111,962) of the sample were from non-Western societies (i.e., not from North America, Israel, Australia, New Zealand, and Western Europe). With respect to the third contrast, within these Western societies, 35% (N ¼ 158,300) were not from the United States. With respect to the fourth contrast, we did not have information on whether the participants were college students. However, we do know that 66% (N ¼ 369,916) of the sample (who ranged in age between 9 and 90) were not from the 18 to 22 age group that characterizes the vast majority of college students. With respect to diversity in terms of ethnicity, a substantial 41% were non-white. All four contrasts suggest that Internet samples are not as dominated by WEIRD participants as are samples currently published in behavioral science journals. Moreover, even though the percentages of non-WEIRD participants in the Internet samples may seem modest, Internet methods permit the collection of large samples, so the absolute sample sizes of non-Weird participants can be quite impressive. For example, although the sample was predominantly North American (52% from the United States and 5% from Canada), the sample represented a breadth of geographic regions from around the world: 111 countries, from Albania (N ¼ 215) to Venezuela (N ¼ 1,920), were represented in the sample by at least 100 participants each. Our data were collected using a website that did not specifically target non-WEIRD samples and used Western languages (Dutch, English, German, Spanish) predominant in WEIRD countries; thus, the findings almost certainly underestimate the percentages of non-WEIRD participants that could be obtained in studies targeting non-WEIRD participants and using languages common in non-WEIRD samples. Like all methods, Internet-based methods are subject to limitations. The samples are certainly not representative of humankind, participants must have access to the Internet and know how to use a Web browser, and some studies cannot be delivered online (Johnson & Gosling 2010). However, the global spread of Web access will diminish the sampling biases. Moreover, current infrastructure and technology already provide the means for distributing compensation to participants (e.g., via Amazon.com’s Mechanical Turk) and for accessing populations that do not have their own Web access (e.g., via laptop computers, tablets,

and smart-phones administered by local research assistants, even those with minimal technical knowledge). No single method can rectify the field’s over-reliance on WEIRD samples. However, Internet methods are one promising tool that should be used in combination with others to combat the problem of WEIRD research. Our analyses suggest that Internet samples can help ameliorate the biases found in typical research samples and can help wean WEIRD researchers off WEIRD samples.

WEIRD walking: Cross-cultural research on motor development doi:10.1017/S0140525X10000117 Lana B. Karasik,a Karen E. Adolph,b Catherine S. TamisLeMonda,b and Marc H. Bornsteina a

National Institute of Child Health and Human Development (NICHD), Bethesda, MD 20892-7971; bDepartment of Psychology, New York University, New York, NY 10003. [email protected] [email protected] [email protected] [email protected]

Abstract: Motor development – traditionally studied in WEIRD populations – falls victim to assumptions of universality similar to other domains described by Henrich et al. However, cross-cultural research illustrates the extraordinary diversity that is normal in motor skill acquisition. Indeed, motor development provides an important domain for evaluating cultural challenges to a general behavioral science.

Henrich et al. remind us, like others before (e.g., Bornstein 1980; Graham 1992; Kennedy et al. 1984; Moghaddam 1987; Parke 2000; Russell 1984; Sexton & Misiak 1984; Triandis 1980), about the formative role of culture in all human behavior. Even basic psychological processes such as perception are subject to cultural variation (Segall et al. 1966). Nonetheless, psychological research remains largely ethnocentric. Consider basic processes in motor development. Cross-cultural comparisons serve as natural experiments revealing the effects of experience on motor development and highlighting diversity in developmental pathways and the range in human potential (Adolph et al. 2010; Bornstein 1995). Yet, the field suffers from long-standing assumptions of universality based on norms established with WEIRD populations. Historically, research on motor development focused on establishing universals. Led by Gesell, early pioneers established the practice of cataloging the ages and stages of motor development. In particular, Gesell’s (1928) testing procedures, test items, and developmental norms – explicitly and deliberately based on behaviors of WEIRD children – inspired the widely used Bayley (1969) and Denver Scales (Frankenburg & Dodds 1967), which describe the developmental timing and sequence of infants’ motor skills. Such normative templates are the current, accepted gold standard of motor development, and are regarded as prescriptions of what is desired, rather than relatively narrow descriptions of what may be acquired. Due to the prevailing emphasis on motor milestones, crosscultural research has been dominated by normative comparisons of onset ages. Recent evidence shows that cultural differences in daily childrearing practices can explain accelerated and delayed onset ages relative to WEIRD norms (see Adolph et al. 2010, for a review). For example, in some regions of Africa, the Caribbean, and India, caregivers vigorously massage and exercise infants as part of daily bathing routines, stretching infants’ limbs, tossing them into the air, and propping them into sitting and walking positions (Bril 1988; Super 1976). Infants who BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? receive massage and exercise begin sitting and walking at earlier ages than infants who do not (Hopkins & Westra 1988). Laboratory experiments with random assignment to exercise and control groups confirm these results: A few minutes of daily exercise accelerates walking onset (Zelazo 1983). Reciprocally, restricted practice can delay the age at which children reach motor milestones. In Northern China, the practice of toileting infants by laying them on their backs in sandbags for most of the day delays the onset of sitting, crawling, and walking by several months (Mei 1994). Among WEIRD families, the recent practice of putting infants to sleep on their backs rather than their stomachs has resulted in delayed onset of crawling and other prone skills (Davis et al. 1998). In cultures that do not encourage crawling (including WEIRD infants circa 1900), large proportions of infants skip crawling altogether (Hopkins & Westra 1988), either bum-shuffling or proceeding straight to walking (Fox et al. 2002; Robson 1984; Trettien 1900). Other aspects of motor development are also influenced by culture and context. For example, childrearing practices can affect the shape of developmental trajectories. In WEIRD cultures, upright leg movements show a well-known U-shaped trajectory: Newborn stepping disappears after about 2 months of age and upright stepping does not return until the end of the first year. But in cultures where caregivers exercise infants’ leg movements (and this is confirmed in laboratory experiments), stepping shows monotonic increase throughout the first year (Super 1976; Zelazo 1983). Foot-binding in China provides an extreme example of how cultural practices affect the form of movements. For 1,000 years, mothers deformed their daughters’ feet to give them the walking gait of a “tender young willow shoot in a spring breeze” (Chew 2005). Feet 3 inches in length were achieved through years of training and excruciating pain. The routine (typically beginning between 5 and 8 years of age) involved breaking four toes on each foot and bending and tightening them in place with bandages. Girls then relearned how to walk with altered balance constraints of their shortened feet. This custom was eradicated in the 1920s. Cultural practices also affect the endpoint of development. Daily tasks require peoples of Africa, Asia, and North America to develop walking and running skills that exceed the abilities of WEIRD adults. African women and Nepalese porters of both genders carry immense loads by modifying their walking gait to conserve mechanical energy (Heglund et al. 1995). They routinely carry more than their body weight for many kilometers (Bastien et al. 2005). Tarahumaran Indian children, women, and men of Mexico run 150 to 300 kilometers round-the-clock for fun and for persistence hunting (Bennett & Zingg 1935). Endpoints can also stop short of what is expected. Crawling on hands and feet before walking is typical in WEIRD infants, but some families of adults in rural Turkey crawl on hands and feet instead of walking (Humphrey et al. 2005). In contrast to most cultures, the parents of these adult children never encouraged walking, and the primary models for locomotion were siblings who also crawled instead of walked. Henrich et al. raise an important point about commonalities across cultures with different childrearing practices. Basic motor functions – manual, postural, and locomotor skills – that are universally useful and adaptive are present in every society studied. We are comparing the postural and manual capacities of 5-month-olds in disparate cultures on maternal handling practices (Karasik et al. 2010). Despite different support contexts, infants practice various postures with accompanying opportunities for object exploration. These data highlight developmental equifinality (Bornstein 1995): Although the routes to object exploration vary, the outcome is the same. Cross-cultural research on motor development is important for establishing general principles in developmental science and for revealing possibilities in human development hitherto

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unimagined. WEIRD infants sit at 6 months, but African infants sit at 4 months. WEIRD mothers would never dream of leaving their young infants unattended, but mothers in Cameroon leave their 5-month-olds (for 20þ minutes!) sitting alone on high stools. These sorts of phenomena can only be revealed with cross-cultural work providing the impetus for laboratory investigations to consider and test hypotheses previously not envisioned.

The socio-ecological approach turns variance among populations from a liability to an asset doi:10.1017/S0140525X10000129 Selin Kesebir, Shigehiro Oishi, and Barbara A. Spellman Department of Psychology, University of Virginia, Charlottesville, VA 229044400. [email protected] http://people.virginia.edu/ sk8dm [email protected] http://people.virginia.edu/ so5x/me2.htm [email protected] http://people.virginia.edu/ bas6g/

Abstract: We emphasize the value of the socio-ecological approach in addressing the problem of population variances. The socio-ecological perspective studies how social and natural habitats shape human behaviors, and are in turn shaped by those behaviors. This focus on system-level factors is particularly well-suited to studying the origins of group differences in human behavior.

The target article persuasively demonstrates the perils of building and testing psychological theories almost exclusively on Western, Educated, Industrialized, Rich, and Democratic (WEIRD) people. We wholeheartedly agree that knowledge based on WEIRD samples is limited at best and misleading at worst. The authors’ plea for broadening the knowledge base of psychological science is valid, timely, and of prime importance for the future development of psychological and behavioral sciences. Whereas the authors’ main recommendation for a better science is to sample participants strategically from diverse populations, we recommend an additional research strategy based on a socio-ecological perspective. Just as ecological biologists study animals’ behaviors in relation to their natural habitats (e.g., Stutchbury & Morton 2001), socio-ecological psychologists study how natural and social habitats affect human mind and behavior. The first step we recommend is, instead of dismissing the research on WEIRD people, to consider the simple yet critical question of “Why are WEIRD people so weird to begin with?” Once potential explanations are considered, researchers can go on to systematically test these potential causes of human diversity in mind and behavior as the second step. There already are several recent studies using a socio-ecological perspective that provide important clues as to why WEIRD people are so different from non-WEIRD people, and what factors might cause diversity in mind and behavior (see Oishi & Graham, in press, for a review). Fincher et al. (2008), for example, wondered why nations, and indeed, regions, vary on individualism versus collectivism (with the former being typical of WEIRD countries). They showed that historically low levels of pathogen prevalence might be one reason – people in nations with low pathogen prevalence do not need to draw sharp ingroup-outgroup distinctions the way people do in nations with high pathogen prevalence. Similarly, Kitayama et al. (2006) wondered about the origins of rugged individualism in the United States, and hypothesized that it is in part due to the history of the frontier spirit. They found that people in Hokkaido, the northern island of Japan with a history of frontier spirit, showed a greater degree of American-style individualism than did mainland Japanese who don’t have such a history.

Commentary/Henrich et al.: The weirdest people in the world? Oishi and colleagues (Oishi et al. 2007; 2009a) likewise wondered why Americans typically define themselves in terms of personality traits, skills, and abilities rather than collective attributes (Markus & Kitayama 1991) and show conditional group identification (Cialdini et al. 1976). They demonstrated that high residential mobility might be partly responsible for such patterns of self-concepts and group identification (see Oishi, in press, for review). Uskul et al. (2008) speculated that the degree of economic interdependence might be in part responsible for analytic thinking dominant in the United States. They examined whether herders (who are economically independent) would show a greater degree of analytic tendency than farmers and fishermen (who are more dependent on others in their economic activities) in the same single region of Turkey. Indeed, they found that Turkish herders showed more analytic tendencies than did farmers and fishermen. Likewise, Yamagishi et al. (2008) hypothesized that preference for unique choice (Kim & Markus 1999) among Americans is due in part to open as opposed to closed social systems, and they demonstrated that this was indeed the case. Finally, one of the target article authors’ own seminal research (see Henrich et al. 2005) has importantly shown that market integration and payoffs to cooperation in daily economic activities predicted cross-societal variations in behavioral responses in the Ultimatum Game. A socio-ecological perspective may help us to understand not only cross-societal variation, but variation within WEIRD populations, too. For example, mean punishment expenditures from the Public Goods Game described in the target article’s Figure 4 show high diversity within WEIRD samples: The United States and Australia are at one end and Germany and Denmark are at the other end. The socio-ecological perspective helps us to generate various hypotheses regarding this variation. For instance, the United States and Australia are nations high in residential mobility, whereas Germany and Denmark are nations low in residential mobility. Could these within-WEIRD variations be the result of societal differences in residential mobility (and temporariness of group membership)? If so, would there be a comparable within-nation variation in punishment behaviors between residentially mobile cities (e.g., Atlanta) and residentially stable cities (e.g., Philadelphia), a within-city variation between residentially mobile people and stable people, and even a within-person variation between the times when people are thinking about moving and the times they are thinking about staying? In the area of self-concept and conditional group identification, Oishi and colleagues have found such within-society variations as well as cross-society variations (see Oishi, in press, for review). We are of course not claiming that all variation between human populations is due to socio-ecological factors. There is no doubt that biological and evolutionary forces also play an important role. However, a socio-ecological perspective does provide a concrete framework for searching for the causes of diversity and universality of mind and behavior. In conclusion, we agree that psychological knowledge should not be solely based on WEIRD people. We also agree with Henrich et al. that it is important to include large and diverse samples in our science. We recommend two additional steps for researchers. First, ask the simple, yet important question of “Why are WEIRD people so weird?” Second, test whether any potential socio-ecological factors that might make WEIRD people weird account for societal, regional, and individual variations in a broad array of phenomena central to human psychology. These two additional steps are critical because they can convert the research on limited WEIRD samples from a major liability (as the authors suggest) to a major asset from which we can build and develop the type of psychological and behavioral sciences that the authors promote in their target article – the psychological and behavioral sciences that illuminate the causes of universality and diversity in mind and behavior. Instead of dismissing the research based on WEIRD people, we can start a

better science from it! We believe that a socio-ecological perspective is particularly helpful to this end.

Determinants of cognitive variability doi:10.1017/S0140525X10000130 Sangeet S. Khemlani,a N. Y. Louis Lee,b and Monica Bucciarellic a

Department of Psychology, Princeton University, Princeton, NJ 08540; Department of Educational Psychology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR of China; cDepartment of Psychology, Universita` degli Studi di Torino, via Po, 14– 10123, Torino, Italy. [email protected] http://www.princeton.edu/ khemlani [email protected] http://www.fed.cuhk.edu.hk/eps/people/leel.html [email protected] http://www.psych.unito.it/csc/pers/bucciarelli/bucciarelli.html b

Abstract: Henrich et al. address how culture leads to cognitive variability and recommend that researchers be critical about the samples they investigate. However, there are other sources of variability, such as individual strategies in reasoning and the content and context on which processes operate. Because strategy and content drive variability, those factors are of primary interest, while culture is merely incidental.

Henrich et al.’s thought-provoking article discusses two major issues – how psychologists should pursue research, and how culture leads to cognitive variability. In what follows, we address these issues in turn, and argue that any cognitive theory ought to account for not only culture, but also other sources of cognitive variability. First, Henrich et al. compare empirical data obtained from individual samples that fall under the umbrella description WEIRD (Western, Educated, Industrialised, Rich, and Democratic) with those obtained elsewhere, and draw the conclusion that “WEIRD subjects may often be the worst population from which to make generalizations” (sect. 7.1.2). For example, they report that people in many non-Western samples do not experience the Mu¨ller-Lyer illusion as strongly as do Americans, and query that “if visual perception can vary, what kind of psychological processes can we be sure will not vary?” (sect. 3.1, para. 5). However, while Henrich et al. caution against making sweeping generalizations from limited sets of data, they do not explain why they are permitted to make sweeping generalizations of relativism from their own data – the Mu¨ller-Lyer illusion is but one single phenomenon in visual perception, hardly representative of all visual perceptual processes. What would count as a representative sample of human psychology? The assertion that WEIRD participants are least representative of human psychology implies that there is a more representative sample, but Henrich et al. have failed to specify it. We believe that such a specification can only be arrived at empirically, and that it is impossible to specify a priori what a representative sample of human cognition should be. Psychological research is theory-driven. Hence, in the absence of any evidence or theoretical rationale suggesting otherwise, WEIRD samples are a convenient proxy for conducting research, and they allow researchers to draw tentative conclusions about the matter of investigation. We acknowledge that certain specific psychological phenomena observed in WEIRD samples may not occur in other populations, and such discrepancies may help researchers make more accurate predictions in future experiments. Henrich et al.’s results underline the point that tentative conclusions are needed in order to support or contravene a theory. Second, Henrich et al. have identified culture as a major source of cognitive variability, but we believe it is important to BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? examine other sources of variability, as well. Cognitive processes are by nature non-deterministic: Children do not employ a deterministic strategy to perform cognitive tasks (e.g., Siegler 1996), and patients with dementia, head injury, ADHD, and schizophrenia are even less consistent in their thinking (for a review, see MacDonald et al. 2006). Likewise, the same individual may perform a task differently at different times. In one of our studies (Bucciarelli & Johnson-Laird 1999), a group of participants had to draw deductions from syllogistic premises by using cut-out paper shapes representing syllogistic terms. Results showed that individuals’ strategies differed from trial to trial in terms of which premises to interpret first, how to interpret the premises, and how to diligently search for counterexamples. As a result, it was impossible to predict individuals’ cognitive operations based on their previous performance. What other sorts of factors affect the way we think? Maturational and psychopathological factors are clear determinants, but the content on which a cognitive process operates may affect the process itself. Individuals think about different contents because they differ in their experiences, education, and beliefs. Culture may explain variability in these factors only to a certain extent, and hence psychologists ought to develop theories that explain how a cognitive process (a) can be modulated by content, and (b) develops and decays under normal and pathological conditions, respectively. Consider the case of bicultural individuals. The behaviour of these individuals is guided by one internalized culture or the other at different moments (e.g., Ng & Lai 2009; Pouliasi & Verkuyten 2007), and they organize their cultural identities differently (Haritatos & Benet-Martı´nez 2002). When bicultural individuals’ cultures contain inconsistent moral values, they will experience moral dilemmas such as the following described by a 19-year-old second-generation Indian American: “I enjoy my Indian culture, I feel that it is rich in tradition, morality, and beauty; confused because I have been in many situations where I feel being both cultures is not an option . . . I feel like you have to choose one or the other” (Haritatos & Benet-Martı´nez 2002). How might a theory explain this phenomenon? Mental models theory (e.g., Johnson-Laird 2006) allows for individuals to entertain inconsistent beliefs, because we tend to rely on separate sets of beliefs in separate contexts (Johnson-Laird et al. 2000). This is evident in moral reasoning, in which moral intuitions and conscious moral reasoning are based on beliefs that are neither complete nor consistent (Bucciarelli et al. 2008). Our conception of culture therefore differs from that in cross-cultural psychology, which considers culture as a network of discrete, specific constructs that guide cognition only when they come to the foreground in an individual’s mind (Hong et al. 2000). How do cross-cultural differences in thinking emerge in a society? Henrich et al. explain the development of these differences by appealing to content (data perceived, norms, and connotations) and context (individuals’ contemporary environment, the environment during development, and the immediate experimental environment). We emphasize that an analysis of reasoning strategies can explain variability within the same individual. Therefore, if content, context, and strategy drive cultural differences, then those factors are of primary interest, whereas cultural differences are merely incidental. Cognitive theories should distinguish between the universal processes they propose and the specific contents on which they operate. For instance, our own theory of moral reasoning (Bucciarelli et al. 2008) posits that moral reasoning is simply normal deontic reasoning (Bucciarelli & Johnson-Laird 2005) applied to moral contents and contexts. Moral contents and contexts may differ across cultures, but the theory of deontic reasoning we propose is, and ought to be, domain-general. Such a dissociation between general computational operations and the contents they operate on allows researchers to construct theories that are sensitive not just to cultural differences, but to age-related, social, personality, and strategic differences, as well.

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Responsible behavioral science generalizations and applications require much more than non-WEIRD samples doi:10.1017/S0140525X10000142 Vladimir J. Konecˇni Department of Psychology, University of California –San Diego, La Jolla, CA 92093-0109. [email protected] http://psychology.ucsd.edu/people/faculty/vkonecni.php

Abstract: There are many methodological considerations – some intricately associated with the use of WEIRD samples – that adversely affect external validity as much as, or even more than, unrepresentative sampling does. Among suspect applications, especially worrisome is the incorporation of WEIRD-based findings regarding moral reasoning and retribution into normative expectations, such as might be held by international criminal tribunals in “cognitively distant” war-torn areas.

The article by Henrich et al. is a valuable contribution that goes beyond prior critiques of the deplorable lack of representativeness of a large proportion of participant samples that have been used in the behavioral sciences. The cogency of argumentation, and both the breadth and the detail of the empirical documentation that is provided, are impressive. Therefore, my commentary will not challenge the main thesis proposed by Henrich et al. Instead, its purpose is to supplement and increase the scope of their article’s argument. An important, although perhaps self-evident, observation is that the authors’ thesis concerning WEIRD samples would be even more useful (perhaps considerably more so) had they at least mentioned and briefly outlined some other factors – often closely, and sometimes unavoidably, associated with the research designs using WEIRD samples – which may even more detrimentally affect the generalizability (external validity) of the results than does the lack of WEIRD samples’ representativeness. An abbreviated list of such factors will have to suffice here: unrepresentative sets of independent variables; artificiality of research settings; a limited number of tasks (often a single task) through which the independent variables are presented; and relying on a single data-collection method (such as questionnaires, surveys, or rating scales) – and therefore obtaining a single dependent measure (or an uninformatively correlated set of measures) that is often qualitatively different from the one to which generalization is sought in the “real world.” The mentioned factors are highly relevant for a more complete understanding of the issues in some of the areas discussed in the target article, especially fairness and cooperation, punishment of “excessive” cooperators, personal choice, “fundamental attribution error,” and moral reasoning. Moreover, one must worry about the (statistical) interaction of the effect of WEIRD samples’ uniqueness (extremity, non-modal character) with the effects of these additional factors (e.g., the frequently highly artificial tasks), such that the overall result (especially when interactions are of a multiplicative form) would be even more misleading with regard to some real-world criterion and domain of desired application than is the case on the basis of WEIRD samples’ “differentness” alone. On the other hand, if, for example, a greater variety of tasks were used, the presently observed differences between WEIRD and various nonWEIRD samples might in some cases disappear. One simply cannot predict what would happen without doing the research. The above family of methodological observations has its root in the pioneering work of Campbell and colleagues (e.g., Campbell & Stanley 1963; Webb et al. 1966). Among the subsequent empirical demonstrations of some of the underlying principles were the studies by Ebbesen and Konecˇni: for example, of decisions under risk (in automobile driving; e.g., Ebbesen et al. 1977; Konecˇni et al. 1976) and of key decisions by judges, prosecutors, and other participants in the criminal justice system (Konecˇni & Ebbesen 1982b). An important aspect of this work has been the mustering

Commentary/Henrich et al.: The weirdest people in the world? of the theoretical and empirical support for the idea of validated simulations in behavioral science (Konecˇni & Ebbesen 1992). Among the judicial decisions studied in this research program were those of the setting of bail and, especially, the sentencing of felons (e.g., Ebbesen & Konecˇni 1975; Konecˇni & Ebbesen 1982a). This work utilized both WEIRD and non-WEIRD samples (as in the fourth “telescoping contrast” in Henrich et al.; see sect. 6) and supports the target article’s skepticism. Moreover, a more general, but logical, extension is to question the applicability of WEIRD-based findings regarding aggressiveness, retribution, fairness and equity, and moral reasoning in general (cf. sect. 4.4.) to international law. Here the most troubling possibility is the deliberate or unconscious incorporation of WEIRD-based findings into the normative expectations held by international bodies in “cognitively distant” war-torn areas – such as in Rwanda by the United Nations Assistance Mission for Rwanda and the International Criminal Tribunal for Rwanda. What must be very carefully taken into account are not only the enormous complexities of ancient tribal relations, but also those stemming from massive religious conversions by some of the warring parties under an external oppressor (as in Bosnia and Herzegovina, another internationally adjudicated conflict). In sum, there is far more to external validity than the unrepresentativeness of samples. The only truly solid reason to trust an experimental simulation (especially one that potentially involves enormous human costs) is to have had it validated by means of careful successive approximations to the real world, each step moving closer to the actual real-world phenomenon – not just with different participant samples, but also guided by a multi-method X multi-dependent-measure matrix (Konecˇni & Ebbesen 1992). Some additional observations are in order. Just as Nature Genetics requires all empirical papers to include data from two independent samples (target article, sect. 6.2, para. 3), the Journal of Personality and Social Psychology, for example, might begin to require not just the use of at least two different methods in the laboratory, but also both laboratory and field research – before researchers move away from psychology freshmen. If this were required, it seems likely that some “cute,” supposedly counterintuitive, task-specific effects (including in the area of heuristics and biases) would not be replicated even with different WEIRD samples. I am not as favorably disposed as Henrich et al. apparently are to Mook’s (1983) idea that the use of WEIRD samples is justified “when seeking existential proofs” (sect. 7.1.6, para. 1); nor to the authors’ admittedly clever idea of setting up research facilities in bus terminals and airports to capture non-university participants (sect. 7.3, para. 6) – if the same old suspect methods, such as “reactive” questionnaires and games with trivial pay-offs, would continue to be used. Henrich et al. believe that behavioral scientists’ tendency to claim “universality” for data obtained with WEIRD participants may in part be due to so many researchers themselves being WEIRD (sect. 7.1.1, para. 8). This fact may also be partly responsible for researchers’ relative reluctance to worry adequately about external validity and about the effects of complex higher-order interactions among type of participants, methods, and settings. A sustained interest in such interactions may require a contextual (“field-dependent”) worldview and a holistic reasoning style that is (according to Henrich et al.) less utilized by WEIRD people, who favor analytical reasoning.

When nurture becomes nature: Ethnocentrism in studies of human development doi:10.1017/S0140525X10000154 David F. Lancy Program in Anthropology, Utah State University, Logan, UT 84322. [email protected] http://www.usu.edu/anthro/davidlancyspages/index.html

Abstract: This commentary will extend the territory claimed in the target article by identifying several other areas in the social sciences where

findings from the WEIRD population have been over-generalized. An argument is made that the root problem is the ethnocentrism of scholars, textbook authors, and social commentators, which leads them to take their own cultural values as the norm.

I am grateful to the authors of the target article for illuminating this very serious problem in the social and behavioral sciences. I also have written critically on the issue, but without the courage to fully assert the fundamental weirdness of researchers’ favorite subjects. I will add to Henrich et al.’s catalog by briefly reviewing several areas where the WEIRD tribe can be shown to be extreme outliers. Culture and cognitive development. The best known model of cognitive development originated with the Swiss biologist, Jean Piaget. He derived his theory largely on his observations and interactions with his own very brainy and sophisticated children (Vidal 1994). As Piaget (and colleagues) tested his propositions, subjects were largely drawn from the same milieu of middleclass European society. Piaget led the vanguard but a veritable army of cognitivists followed in his wake. The models that emerged were rooted entirely in research with children from the WEIRD tribe. Had these scholars delved into the anthropological literature, particularly with respect to the cognitive processes implicated in native belief systems, they might have paused to consider the implications. Indeed, Alexander Luria, close colleague of Lev Vygostsky, traveled to Central Asia in the 1930s and easily discovered alternative patterns of thinking in the reasoning of Uzbek peasants (Luria 1976). Later, researchers working in West Africa (Dasen et al. 1978; Greenfield 1966) and Papua New Guinea (PNG) (Kelly 1971) sought to test these theoretical ideas about children’s cognitive development outside the West and found that they didn’t hold up very well, especially beyond early childhood. As Luria had earlier shown, scholars were finding that cognitive “development” was driven by exposure to modern institutions – schooling, in particular – rather than reliably erupting, like second molars (Cole et al. 1971). Others succeeded in showing very specific connections between cultural practices and cognitive skill (Price-Williams et al. 1969). Somewhat later in PNG, the typical two-culture (WEIRD vs. “other”) comparison was broadened to systematically assess cognition in a variety of societies with varying subsistence patterns and degrees of acculturation (Lancy 1983). These studies revealed that the patterns of cognitive behavior in the WEIRD population were uncommon compared to preferred local alternatives (Lancy & Strathern 1981). Culture and children’s social behavior. Social psychologist Millard Madsen began with the premise that Western middleclass children were markedly different. He devised a series of ingenious, game-like devices that unambiguously revealed whether a child was disposed towards a competitive or cooperative stance. In his initial work, he found that subjects in the United States made only competitive moves in the game (which only rewarded cooperative moves), whereas children from a Mexican village made only cooperative moves. Replicated in numerous other societies, the studies revealed U.S. children as outliers, being much more competitive than children from other societies (Madsen 1971). Further cross-cultural variation was neatly predicted by the child’s social circumstances, so village kids were found to be more cooperative than urban kids, for example. In the highlands of PNG, Melpa children from warring clans were less cooperative than pairs from the same or allied clans (Lancy & Madsen 1981). Culture and parent-child interaction. The problem identified by Henrich et al. arises, I believe, from a (likely universal) ethnocentrism. Contemporary orthodoxy regarding child development and child-rearing can turn nurture into nature. The way WEIRD parents raise their children becomes more than just the current fashion, it becomes “natural,” rooted in the phylogeny or history of the species. This can be quickly illustrated. Working among the Gusii of Kenya, LeVine (2004) has raised doubts about widely accepted tenets of the theory of infant attachment. Like many, if not the majority of mothers throughout BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? history, Gusii mothers respond to their infant’s need for sustenance but otherwise largely ignore them. Such behavior, if displayed by a Euro-American mother, would be grounds for a clinician to predict later pathological development. Of course, the Gusii children turn out fine. Closely related is the practice of talking to nonverbal infants using a special speech register (baby talk or motherese). Often assumed to be both universal and essential to the development of speech in children, it is in fact neither (Ochs & Schieffelin 1984). “Parenting style” theory (Baumrind 1971) cannot withstand cross-cultural scrutiny. Central African Bofi farmers fit the so-called authoritarian parenting style in valuing respect and obedience and exercising coercive control over their children. Bofi children should, therefore, be withdrawn, non-empathetic, and aggressive, and lack initiative. On the contrary, they display precisely the opposite traits, leading Fouts (2005) to conclude that the theory “has very little explanatory power among the Bofi” (p. 361). Parent-child play is another in this basket of parenting behaviors that illustrates how nurture is made out to be nature. A recent textbook describes variation in patterns of parent-child play, but never questions its universality (see Scarlett et al. 2005). One scholar of infancy claimed that the absence of mother-infant play signaled attachment failure or worse (Trevarthen 1983, p. 151). Empirical studies of mother-child play typically report that “Mothers were instructed to play with their [2- to 3-years-old] children as they would at home” (Stipek 1995, p. 244; emphasis added). Another common feature is the use of well-off, highly educated subjects, with no caveat about the limited generalizability of the results (see Sung & Hsu 2009, p. 432). However, in a recent review of the ethnographic record, with hundreds of cases, parent-child play was found to be extremely rare and distinctly incompatible with many native ideas about “best practices” (Lancy 2007). To parents, play’s chief value is in keeping children out of the way (Whittemore 1989, p. 92). Lastly, I would demur from the notion that parents’ active teaching of children is both universal and the essential component of cultural transmission (Csibra & Gergely 2009; Strauss & Ziv 2004). A thorough survey of ethnographic and historical cases shows teaching by parents to be extremely limited (Lancy & Grove 2010); children are expected to learn from observation, imitation, and practice. As Fiske (1997) notes, in the ethnographic record there is “much less child-rearing than there is culture-seeking” (p. 11).

BIZARRE chimpanzees do not represent “the chimpanzee” doi:10.1017/S0140525X10000166 David A. Leavens,a Kim A. Bard,b and William D. Hopkinsc a School of Psychology, University of Sussex, Falmer, East Sussex BN1 9QH, United Kingdom; bPsychology Department, Centre for the Study of Emotion, University of Portsmouth, Portsmouth, Hampshire PO1 2DY, United Kingdom; c Division of Psychobiology, Yerkes National Primate Research Center, Atlanta, GA 30322, and Department of Psychology, Agnes Scott College, Decatur, GA 30030. [email protected] http://www.sussex.ac.uk/psychology/profile114996.html [email protected] http://www.port.ac.uk/departments/academic/psychology/staff/ title,50477,en.html [email protected] http://userwww.service.emory.edu/ whopkin/

Abstract: Henrich et al. convincingly caution against the overgeneralization of findings from particular human populations, but fail to apply their own compelling reasoning to our nearest living relatives, the great apes. Here we argue that rearing history is every bit as important for understanding cognition in other species as it is in humans.

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Henrich et al. provide a timely cross-cultural analysis of perceptual and cognitive abilities in humans, concluding that a significant number of allegedly human-universal capacities are, in fact, confined to specific socio-cultural environments. Ironically, they caution against overgeneralizing from particular humans to all humans, everywhere, but fail to correctly generalize their own arguments to nonhuman species. In section 7.1.4, Heinrich et al. highlight both the need for and the limits of comparisons between human and nonhuman primates. Their point is that contradictory conclusions about the taxonomic distribution of cognitive abilities could be reached, depending upon which human cultures are sampled. What they fail to elucidate is that precisely the same arguments also apply to nonhuman primates, particularly the great apes, humans’ nearest living relatives. The over-reliance in psychology on one group of humans, WEIRD, to represent “the human” in cognitive terms has a strong parallel in the over-reliance in comparative psychology on chimpanzees raised in Barren, Institutional, Zoo, And other Rare Rearing Environments (BIZARRE) to represent “the chimpanzee.” Cross-species comparisons are appropriate only with careful consideration of specific rearing environments (e.g., Bard 1998; 2008; Bard & Leavens 2009; Boesch 2007; Bulloch et al. 2008; Leavens 2002; 2004; 2006; Leavens & Racine 2009; Leavens et al. 2005a, 2008; 2009; Lyn et al. 2010; Racine et al. 2008). The case of manual pointing provides an example of overgeneralization in both humans and nonhumans. Western scientists portrayed pointing with the index finger as a human species-specific gesture adapted for referential communication (Butterworth 2003; Eibl-Eibesfeldt 1989; Povinelli & Davis 1994; Povinelli et al. 2003). Degos (2001) recently asserted that “[t]he pointing gesture is exclusively human, and universal within mankind” (p. 263). In fact, although widespread, index-finger pointing is neither universal nor necessarily the predominant manner of pointing even where it is found (Enfield 2001; Wilkins 2003). The formerly popular idea that pointing with a particular finger extended reflects the shared evolutionary history of all humans, everywhere, exemplifies the kind of reckless disregard for cultural variability that Henrich et al. rightly criticize. Production and comprehension of pointing are widely viewed as indices to cognitive processes supporting reference, perspective-taking, and the construction of shared goals (e.g., Tomasello 2006). Like humans, chimpanzees display group differences in pointing. Figure 1 depicts three pointing phenotypes, all emanating from the same gene pool (Leavens 2004; Leavens et al. 2005a; 2008). As this figure makes clear, depending upon which chimpanzees are sampled, entirely contradictory conclusions can be reached as to whether pointing is a shared characteristic of great apes and humans. Consider the incidence of manual pointing: If home-reared or language-trained apes were compared with humans from cultures that prohibit manual pointing, one could reach the erroneous conclusion that great apes point manually, whereas humans do not; this would be a consequence of overgeneralizing from both of the specific human and chimpanzee populations sampled. Recently, numerous researchers have reached the erroneous conclusion that only humans point declaratively, because they have compared people of largely European descent (i.e., WEIRD) with wild chimpanzees or institutionalized (i.e., BIZARRE) chimpanzees. When chimpanzees are raised the way human children are raised, in richly interactive contexts, the chimpanzees will go on to point – point with the index finger, frequently point declaratively, and comprehend pointing by others (Itakura & Tanaka 1998; Leavens et al. 2005a; 2008; Lyn et al. 2010). Different populations of chimpanzees also display different communicative expressions and calls. For example, Figure 2 depicts Merv pointing with his index finger to a squeeze bottle filled with juice (out of camera view). The protruded lower lip is glossed as the “juice-me” face, and to our knowledge appears only in chimpanzees that experience juice delivery from a bottle with a downward-pointing spout. Chimpanzees adapt calls to serve functions in captive environments that differ from

Commentary/Henrich et al.: The weirdest people in the world?

Figure 1 (Leavens et al.). Pointing incidence, posture, function, and social use differ radically between different populations of chimpanzees. Manual pointing: Extension of arm and at least one finger in triadic contexts. Index finger: Pointing with outstretched arm and index finger. Declarative: Pointing to draw somebody’s attention to an object or event; includes responses to queries, such as pointing to an object when asked where that object is. Between apes: Pointing by one chimpanzee for another chimpanzee. Comprehension: Understanding pointing by others. [Sources: Pointing in wild chimpanzees: Inoue-Nakamura and Matsuzawa (1997); also see Pika and Mitani (2006). Pointing in institutionalized chimpanzees: de Waal (1982); Leavens and Hopkins (1998); Leavens et al. (1996; 2004a; 2005b); Savage-Rumbaugh (1986); among others. Pointing in home-reared or language-trained chimpanzees is nearly ubiquitous and has been described by Carpenter et al. (1995); Fouts et al. (1982), Kellogg and Kellogg (1933), Krause and Fouts (1997), Lyn et al. (2010), and Witmer (1909), among others. See Leavens and Racine (2009) and Leavens et al. (2009) for reviews.] their uses in the wild: chimpanzees in captivity selectively emit attention-getting calls and other auditory signals, tactically, as a function of whether an observer is looking at them (Hopkins et al. 2007; Hostetter et al. 2001; Leavens et al. 2004b; 2010). The validity of Henrich et al.’s argument does not suddenly collapse when applied to other species. We agree with Henrich

and his colleagues that scientists need to specify to “which people” their findings can be generalized (sect. 7.3). And we argue here, that, given the variability in communicative signaling characteristics in different populations of great apes, there is no rational justification for overgeneralizing from BIZARRE chimpanzees to the entire chimpanzee species. Like humans, chimpanzees evince different cognitive adaptations to different environments; no single environmental context can elicit the full range of chimpanzees’ cognitive capacities. ACKNOWLEDGMENTS We gratefully acknowledge discussions about these ideas with Sarah Boysen, the late George Butterworth, Josep Call, Malinda Carpenter, Deborah Custance, Mark Krause, Heidi Lyn, Simone Pika, Timothy Racine, and Jamie Russell.

Explaining why experimental behavior varies across cultures: A missing step in “The weirdest people in the world?” doi:10.1017/S0140525X10000178 Edouard Machery

Figure 2 (Leavens et al.). Merv points with his index finger and simultaneously displays the “juice-me” face, a facial expression unique to certain captive groups. Used with permission from the American Psychological Association (Leavens & Hopkins 1998).

Department of History and Philosophy of Science, University of Pittsburgh, Pittsburgh, PA 15260. [email protected] www.pitt.edu/machery/

Abstract: In this commentary, I argue that to properly assess the significance of the cross-cultural findings reviewed by Henrich et al.,

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Commentary/Henrich et al.: The weirdest people in the world? one needs to understand better the causes of the variation in performance in experimental tasks across cultures.

Henrich et al. review a large body of evidence showing that in numerous tasks Westerners (often Americans) behave differently from people in other cultures. The target article raises numerous important questions, including: Which psychological characteristics should we expect to vary from culture to culture (studying these would necessarily be cross-cultural) and which should we expect to be universal (studying these could be done on the basis of samples of convenience)? Why are most examples in Henrich et al.’s article drawn from social psychology? Is social cognition particularly likely to vary from culture to culture? Would we find the same cross-cultural variation if we focused on the phenomena discussed in perceptual and cognitive psychology textbooks (provided the cross-cultural data are available)? Do the findings reviewed by Henrich et al. suggest replacing the traditional search for human psychological universals (our human nature) with the search for laws that connect psychological variation to social and ecological conditions? Although these questions are fascinating, for the sake of space I will focus only on the following issue: It is unfortunate that Henrich and colleagues chose to say little about the causes of the cross-cultural variation they present evidence about, since the significance of this variation depends on the exact nature of these causes. To illustrate this point, I examine three possible explanations of the cross-cultural variation described by Henrich et al., and I consider how these explanations bear on the significance of the findings presented in the target article. It is well known that when participants conceptualize a given experimental task differently (perhaps because they have different beliefs about the experimenter’s goals), they behave differently. (In fact, this is a well-known experimental problem.) To give a single example, Westerners behave differently in economic games depending on how these are framed (e.g., Hoffman et al. 1994). Some of the findings discussed by Henrich et al. (perhaps many) may simply be due to the fact that experimental participants in different cultures conceptualize the experimental tasks differently. In fact, Henrich’s own findings (in previous work with other colleagues: Henrich et al. 2004; 2005) about the cross-cultural variation in the Ultimatum, Dictator, and Public Goods games can be at least partly explained in this way, as some researchers on Henrich’s team reported that participants assimilated the economic games to real-life situations (e.g., the Orma identified the Public Goods Game with the harambee, a local financial contribution to collective projects; see also Cronk 2007). Naturally, when participants in an experimental task (within a given culture or across cultures) behave differently because they conceptualize this task differently, this variation does not show that the individuals who behave differently differ psychologically (i.e., that they have different psychological mechanisms, capacities, characteristics, etc.): If one controlled for the differences in task conceptualization across cultures, the crosscultural variation would disappear. Thus, much of Henrich et al.’s thunder would be stolen if the findings they describe were due to people in different cultures conceptualizing the tasks differently. True, it would still be incorrect to expect people in different cultures to behave similarly in a given experimental task, but this would not entail that the American mind differs from, for example, the Peruvian mind or the Machiguenga mind. Consider now a second type of explanation. Human beings have evolved numerous domain-specific mechanisms that are designed to interact with the cultural, social, and ecological environment to produce typically (but not necessarily) locally adaptive psychological phenotypes. Although there is little space here to discuss the various ways of specifying this hypothesis (for detail, see Fessler & Machery, forthcoming), let us

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consider some possibilities. Some evolved mechanisms might have parameters which are set to different values in different environments during development. If the hypothesis of a universal grammar developed by Chomsky is correct, this is how natural languages work. Some evolved mechanisms might provide a template that is completed with culturally local information (see, e.g., Barrett’s [2005] hypothesis of a universal, evolved mechanism for learning concepts of dangerous animals). It might also be that humans typically possess various strategies for fulfilling a given psychological function (e.g., categorizing or making decisions under uncertainty) and that they learn to rely preferentially on the strategies that are most efficient in their environment, while being able to revert to the other strategies if needed or primed. Many cross-cultural findings might result from people learning to rely preferentially on a particular strategy among the toolbox of strategies that are available to them, since, as discussed by Henrich et al., it is sometimes easy to prime people to adopt the cognitive styles of cultures they do not belong to. If the cross-cultural variation in experimental tasks described in the target article were due to the interaction of universal processes and local environments, this variation would reflect a genuine psychological variation – a significant conclusion: It would show that across cultures, people do harbor different psychological processes (characteristics, styles, etc.), or, at least, that they preferentially rely on different processes (styles, etc.). However, this type of explanation would undermine the idea, suggested throughout Henrich et al.’ article, that the Western mind is really peculiar, since the psychological differences across cultures would emerge from the same basic psychological endowment. In a sense, the cross-cultural variation in psychological phenotype would be shallow (particularly if it is merely a matter of people relying preferentially on different strategies in different cultures). Furthermore, albeit being an incomplete research strategy, in need of complementary cross-cultural work, studying Western participants could cast some light on this basic endowment, exactly as one can learn about the universal grammar by studying English syntax. Consider, finally, a third explanation. Participants in different cultures behave differently in experimental tasks because people acquire different psychological processes, traits, or capacities across cultures as a result of cultural transmission, domaingeneral learning mechanisms, and the like. To give a single example, this third explanation plausibly applies to the cross-cultural variation in semantic intuitions (for review, see Machery, forthcoming). Philosophers of language have ignored the possibility that the semantic intuitions on which theories of reference are based might vary across cultures. However, evidence shows that while Americans tend to view the reference of proper names as determined by the causal and historical connections between these names and particular individuals, Chinese are more likely to view the reference of proper names as determined by the information speakers associate with these names (Deutsch et al. 2010; Machery et al. 2004). Follow-up studies even suggest that Americans are much more likely to hold the former kind of intuitions than are other Westerners such as French participants (Machery & Stich, forthcoming; Machery et al. 2009). If this third explanation explained not only the variation in semantic, epistemological, and other intuitions described by experimental philosophers (see Stich’s commentary), but also the findings summarized by Henrich et al., these findings would then reveal not only that the human psychological phenotype varies across cultures, but also that this variation does not merely result from the interaction of a basic psychological endowment and local environments. Furthermore, studying American participants, as most American psychologists have done for about a century, would often reveal nothing about universal properties of the mind; rather, American psychology would often just be the psychology of Americans.

Commentary/Henrich et al.: The weirdest people in the world?

WEIRD languages have misled us, too doi:10.1017/S0140525X1000018X Asifa Majid and Stephen C. Levinson Max Planck Institute for Psycholinguistics, Nijmegen 6500AH, The Netherlands. [email protected] http://www.mpi.nl/people/majid-asifa [email protected] http://www.mpi.nl/people/levinson-stephen

Abstract: The linguistic and cognitive sciences have severely underestimated the degree of linguistic diversity in the world. Part of the reason for this is that we have projected assumptions based on English and familiar languages onto the rest. We focus on some distortions this has introduced, especially in the study of semantics.

This commentary rounds out the picture convincingly advanced in the target paper by considering how scientific approaches to language have also ignored the diversity of behavior in the linguistic domain. In the BBS paper “The Myth of Language Universals,” Evans and Levinson (2009) argue that there is little evidence for the view that the variation in language structure is tightly bounded by linguistic universals. Instead, what we find is extensive variation on almost every dimension, with the main patterns understandable in terms of cultural evolution. Why does it matter to psychology that languages vary fundamentally on so many dimensions? Leaving aside that it is the highest learned human skill, and therefore has an interest in its own right, there are at least two further reasons: (a) Language is in many ways a “window on the mind,” and (b) semantic variation seems to correlate with psychological variation on a range of parameters. As a result, most of our ideas about how humans reason or what notions form natural categories are prompted by our own languages. We must leave to the historians of science an explanation for how the myth of language universals came to dominate the language sciences for 50 years. But one factor is almost certainly the view that familiar languages such as English are canonical. Yet many features of English are quite unusual – for example, only 1.6% of languages express Yes/No questions by word order inversion (Dryer 2008), and no other known language has verb inflection with non-zero exponent for third person (as in John come-s) but zero for all other persons (see rara#34 http://typo.uni-konstanz.de/ rara/). As we discuss here, there are languages without such “natural” concepts as left, in, arm, or green. English is, in fact, in a special, rather unusual corner of the design space for human languages, an outlier, not some kind of central default model at all. As surveyed in the target article (sect. 3.4), there appears to be significant variation in the spatial frames of reference employed crossculturally, largely predictable from language – that is, languages without spatial left and right terms are used by peoples who prefer allocentric coding. Some authors find these results hard to credit and have wondered whether they don’t reflect conscious ratiocination correcting an innate egocentric bias (Li & Gleitman 2002). Haun and Rapold (2009) now produce additional evidence for the depth of allocentric coding in a non-WEIRD culture, Haikom hunter-gatherers of Namibia. Haun and Rapold asked 8year-olds to learn a demonstrated dance, and then got them to repeat the dance under 180-degrees rotation. The Haikom kids maintained the dance moves in absolute (North-South-EastWest) coordinates under rotation, whereas a matched German sample maintained the dance moves in egocentric coordinates (in terms of left/right motions). Given the rigidly egocentric nature of neural coding for body position, these findings are quite unexpected and show the extent to which a different spatial framework can be adopted in the cognitive representation of spatial movement. The spatial encoding of topological relations between objects in non-WEIRD languages also challenges long-held assumptions about the human mind. Developmental psychologists and linguists have supposed that preverbal infants have a stock of prelinguistic concepts (either inborn or learned through sensorimotor

experience) and that language maps onto these universal primitives (e.g., Clark 1973; Slobin 1973). These include notions such as in, on, and under – exactly those concepts found in English. Infants less than a year old show sophisticated understandings of such relations (Baillargeon 1994). But subsequent crosslinguistic investigation has shown considerable variation in how languages express spatial concepts (Bowerman 1996; Bowerman & Choi 2001). Korean speakers distinguish between “tight-fitting” and “loose-fitting relations” instead of “containment” (as in in) or “support” (as in on). This finding has had a profound impact on how we currently think about the infant mind. Rather than the infant coming to the world with only English spatial categories, she comes now with Korean ones, too (Hespos & Spelke 2002; McDonough et al. 2003), and researchers are actively pursuing which of the myriad further distinctions coded in other languages may be present for the infant, too. In another domain, vision scientists have been impressed by the correspondence between the algorithms used by the visual system to parse the world around us and their reflection in language. Hoffman and Richards (1984, p. 82), after discussing how parts are assigned when viewing a face, conclude that: “It is probably no accident that the parts defined by minima are often easily assigned verbal labels” – the caveat being “in English.” The parts assigned verbal labels in other languages differ substantially. In Jahai, a language spoken in the Malay Peninsula by a group of nomadic hunter-gatherers, speakers make fine-grained segmentations of the body and face: there is no term corresponding to face, arm, or leg (Burenhult 2006). Compare this with Lavukaleve, spoken by some thousand-odd subsistence fishers and farmers on the Solomon Islands. Lavukaleve speakers have a much coarser-grained system and use a single term to refer to arms and legs, with no finer-level conventionalized terms for the limbs (Terrill 2006). The correspondence between English part categories and those identified by the vision sciences has caused researchers to seriously misjudge the issues involved in a theory of the language–perception interface (Majid 2010). Take color as yet another domain. Here language plays a critical role. Languages carve up the spectrum into a number of discrete categories, and it is these linguistic categories that are utilized in memory and perception (Davidoff et al. 1999; Gilbert et al. 2006). Speakers of languages from WEIRD societies make finer divisions in the color space than do speakers of most of the thousands of other languages of the world (Kay & Maffi 2008), and the pinnacle of color categorization (in terms of how many divisions of the color space a language makes) has been taken to be 11 – exactly the number that English has (Berlin & Kay 1969; Kay & Maffi 2008). But there is accumulating evidence that WEIRD societies may be surpassed in this domain. In Russian (Corbett & Davies 1995), Turkish ¨ zgen & Davies 1998), Greek (Thierry et al. 2009), and Japanese (O (Uchikawa & Boynton 1987), there are 12 terms (an additional distinction is made between dark and light blue). The new pinnacle is 15, as demonstrated by Korean (see Roberson et al. 2008). Human performance diversity offers a rich resource for cognitive scientists. It allows us to triangulate on underlying properties of mind that would be invisible if we were all culturally identical. Instead of lamenting the loss of the “psychic unity of mankind,” we should embrace the study of cognitive diversity as a window on human cognitive potential.

WEIRD societies may be more compatible with human nature doi:10.1017/S0140525X10000191 Alexandra Maryanski Department of Sociology, University of California –Riverside, Riverside, CA 92521. [email protected]

Abstract: Are WEIRD societies unrepresentative of humanity? According to Henrich et al., they are not useful for generalizing about

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Commentary/Henrich et al.: The weirdest people in the world? humans because they are at the extreme end of the distribution for societal formations. In their vision, it is best to stick with the “tried and true” traditional societies for speculations about human nature. This commentary offers a more realistic starting point, and, oddly enough, concludes that WEIRD populations may be more compatible with humans’ evolved nature than are most traditional societies.

While Henrich et al. exaggerate the extent to which undergraduates are used to generalize about pan-human traits (as many social scientists still refuse to subscribe to the premise that a human nature exists), it is true that such generalizations are often implicit in research findings. I also endorse the authors’ call for more cross-species research and new research designs. But I strongly disagree with the claim that WEIRD populations skew the traits that characterize Homo sapiens. Henrich et al. also overstate the value of the cross-cultural method for uncovering the evolutionary foundations of human cognition and social behavior. Let me highlight some of these limitations. What is to be learned from cross-cultural research? The crosscultural method has a long history It originated in the 19th century when ethnographers took to the field to “discover” the fundamental and universal core of humans and society. These ethnographies, along with the Human Relations Area Files (HRAF), are readily available in most libraries. What researchers found is that cultures are organized around systems of common meanings that members use to direct behaviors and organize social relations. A key finding is that despite a remarkable diversity, broad-based cultural universals do exist, and therefore, the cross-cultural method has proven to be useful in making generalizations about human cognitions, personality, culture, and society. But it has failed to uncover an anchor of underlying pan-human traits. Today, these populations speak only in the “ethnographic present.” Hunter-gatherer societies, who monopolized 95% of human history, are extinct. Most horticultural societies are also extinct or hardly recognizable from their past history, as they have had to adapt to a 21st-century industrialized world. The cross-cultural method is still valuable, of course, but no matter what samples of societies are used, claims about pan-human psychology and social behavior are not easily made from the data. We still confront the problems of earlier researchers: How do we distinguish the simplest, oldest, and most exotic traits from the most common traits? For, once humans abandoned the hunter-gatherer lifestyle, institutional arrangements and secondary adaptations were set into motion and passed down from generation to generation, including symbolic traditions unique to a population. Hence, even if we believe that a substratum of evolved human proclivities exists, they are always at the mercy of culture and social forces that can repress them, channel them towards more normative behaviors, or, in some societies, allow them to be expressed. How, then, do we identify an evolutionary-derived trait from a socio-cultural trait? Hence, on what empirical grounds can the authors claim that WEIRD people are not in tune with their evolved nature? What yardstick justifies such a claim? In the future, would it really be a surprise that as populations around the globe slowly industrialize and set up democratic institutions, they too may come to resemble WEIRD populations? Who, then, should we nominate as unrepresentative of our species? The authors’ call for new research designs for uncovering evolved traits, however, is much more promising. A call for new research programs. Do human proclivities exist that characterize our species? Rather than falling into assumptions about human nature, we do need new research designs that include an evolutionary perspective, cross-species research, and a true interdisciplinary approach. In particular, I think we need to start by searching our evolutionary past for clues. Let me illustrate with two basic questions on human biology and human sociality: (1) How much genetic variability exists in the human genome; and (2) are humans naturally social?

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Low genetic diversity. Recent findings reveal that the human genome has less genetic diversity than the chimpanzee (Pan), our closest living relative (Bakewell et al. 2007; Gagneux & Varki 2001; Kaessmann & Paabo, 2002: Kaessmann et al. 1999). This surprisingly low level of genetic variability suggests that humans today are all descendants of a small “mother” deme that may have numbered only in the hundreds (Long & Kittles 2003; Relethford 2002). If so, despite great human cultural variation, a high probability exists for a bedrock of evolved traits. In addition, the fossil record tells us that anatomically modern humans evolved about 150 thousand years ago (Garrigan & Hammer 2006; Smith et al. 2007). This means that hunter-gatherer populations were around for at least 140,000 years before horticultural farmers. As data on studied huntergatherers clearly demonstrate, they share a remarkable number of institutional arrangements with traits that include high individualism, reciprocity, and low levels of inequality; and their wellpreserved ethnographies are surely a more fruitful resource for clues into our evolved nature. How naturally social are humans? Humans are evolved apes This fact has important implications for how we might go about studying our biologically based propensities and social arrangements. One way to gain insights into the distant past is to use cladistic analysis, social network analysis, and evolutionary theory. One intriguing finding is that monkeys (which make up 70% of all primates) are high-density collectivists, whereas great apes are low-density and low-sociality individualists, with needs for autonomy and mobility. Witness the orangutan, which is nearly solitary. Thus, one starting point for clues into our evolved nature is to study our primate relatives along with past huntergatherer societies. These data suggest that the high-sociality, collectivist touchstone we have used to characterize Homo sapiens may be misguided. And oddly enough, since the days of hunter-gathering, the society that best fits this view of human nature – at least in terms of placing a high value on individualism, mobility in space, relative autonomy, verification of self, sexual equality, and freedom of choice – are WEIRD populations. For, despite all the multiple ills of industrialized societies, WEIRD societies may be more compatible with our human nature than the high-density kinship constraints of horticultural societies or the “peasant” constraints of agrarian societies with their privileged few (for data on this argument, see Maryanski & Turner 1992; Turner & Maryanski 2005; 2008).

It’s not just the subjects – there are too many WEIRD researchers doi:10.1017/S0140525X10000208 Michael Meadon and David Spurrett School of Philosophy and Ethics, University of KwaZulu-Natal, Howard College Campus, Durban 4041, South Africa. [email protected] http://ionian-enchantment.blogspot.com [email protected] http://ukzn.academia.edu/DavidSpurrett

Abstract: A literature in which most data are outliers is flawed, and the target article sounds a timely alarm call for the behavioural sciences. It also suggests remedies. We mostly concur, except for arguing that the importance of the fact that the researchers themselves are mostly outliers has been underplayed. Improving matters requires nonWestern researchers, as well as research subjects.

Henrich et al. provide an important piece of “consciousness raising”: They remind us that human beings are complicated and diverse, and review evidence that the variance in some types of behaviour and cognitive performance is far greater than has often been assumed. These facts have been obscured by the WEIRD-ness of the vast majority of the research subjects

Commentary/Henrich et al.: The weirdest people in the world? in behavioural science: people are not interchangeable in the same sense in which a sample of gold, whatever its origin, can be taken to the laboratory and treated as representative of gold generally. No one geographical, cultural, or socio-economic subgroup of people (least of all, perhaps, contemporary Western undergraduates) is representative of humanity. This means theories tested only or largely against a narrow range of subjects are likely to be incomplete or outright false. Behavioural scientists, in other words, have engaged in precipitate theorizing: outliers are unlikely to constrain explanatory theories correctly or inspire the right sort of hypothesizing. To remedy this bias, ideally, we need globally representative samples of subjects in order to generate thorough descriptions of cognitive phenomena, guide hypothesis formation, and rigorously to test theories. The logistical and financial challenges of conducting research on this basis are immense. Overcoming them requires, among other things, changes in the research culture and to academic incentives (here Henrich et al.’s recommendations are spot on). In psychology, for example, the current incentives produce many small, single-institution, lowpowered studies aimed at discovering novel effects. What we need, however, is not, say, evidence of yet more ways of modulating implicit associations in American undergraduates. Rather, we need large, prospectively designed, highly powered, cross-cultural studies that can answer specific questions more definitively. Medical research provides a model behavioural researchers would do well to emulate. In general and in outline, before some clinical intervention is approved for use, research must proceed through three stages. This begins with small openlabel studies in Phase I; proceeds to larger, single-blind trials in Phase II; and culminates in large, multi-center, randomized, double-blind, placebo-controlled trials in Phase III. Behavioural scientists conduct too many studies in the equivalent of Phases I or II, with too few large, definitive and cross-cultural studies. There is still room for small studies – important novel phenomena undoubtedly await discovery. We merely suggest more “Phase III” research. Pooling resources, exploiting various online collaboration tools (e.g., Nielsen 2008), and shifting editorial policies and research priorities should result in more large, cross-cultural studies being conducted. That noted, Henrich et al. underplay – to the point of missing – that how the behavioural sciences research community itself is constituted introduces biases. That the subject-pool of behavioural science is so shallow is indeed a serious problem, but so is the fact that the majority of behavioural researchers are themselves deeply WEIRD. People in Western countries have, on average, a remarkably homogenous set of values compared to the full range of worldwide variability (Inglehart & Welzel 2005), and the data Henrich et al. adduce suggest similarly population-level homogeneity in cognitive styles. Moreover, academics are more uniform than the populations from which they are drawn (as the target article’s Contrast 4 suggests), so it is not implausible to think behavioural scientists are even WEIRD-er than their most common subjects. Henrich et al. review a body of studies and experiments that did not strike those who designed and conducted them as focused on outliers. Intelligent scientists acting in good faith conducted, peerreviewed, and published this research, honestly believing in many cases that it threw light on human nature. This forcefully illustrates the power of the biases on the part of researchers themselves. It also suggests that, besides widening the pool of subjects, there are significant gains to be made by broadening the range of inputs to the scientific process, including in the conception, design, and evaluation of empirical and theoretical work. Given that diverse groups are demonstrably better at some kinds of problem solving (e.g., Hong & Page 2004; Lakhani et al. 2006), as things stand, the WEIRD-dominated literature is robbed of potentially worthwhile perspectives, critiques, and hypotheses that a truly global research community could provide. Clearly, simply increasing the number of behavioural sciences

researchers will, in general, be beneficial. Our key contention, though, is that the marginal benefits of additional Western researchers are much smaller than the marginal benefits of more non-Western researchers, among other things, just because they are non-Western. The non-Western world, in short, can contribute not only additional subjects to experiment upon – the main focus of the target article’s recommendations – but also additional researchers, with novel perspectives and ideas and who are less affected by WEIRD biases. (Naturally, these researchers will have biases of their own. Our claim is not that there is someone who consistently knows better than WEIRD researchers. It is that diverse groups of investigators can avoid some kinds of error.) Clearly, these researchers will have to be educated, will likely be middle class, and, since science flourishes in politically open societies, they will tend be concentrated in liberal countries. Nevertheless, additional non-Western researchers, even if they are educated and relatively wealthy, could be a boon to the behavioural sciences. A direct and powerful way to remedy both sources of bias – too many WEIRD subjects and too few non-WEIRD researchers – is to foster research capacity in the non-Western world. Non-WEIRD researchers tend to study non-WEIRD subjects, so increasing their number will deepen the subject pool and widen the range of inputs to the scientific process at the same time. Building research capacity, however, should not merely involve collaborations led by WEIRD researchers; it should aim to generate studies led and initiated by non-Western researchers. Committed and long-term inter-institutional collaboration between Western and non-Western universities focused on remedying the deficits in the behavioural sciences literature should include internships at Western universities for nonWestern researchers, stints at non-Western universities for WEIRD researchers, and extensive student exchange programmes (especially for graduate students). Unlike many existing scholarship and exchange programmes in the sciences, a key point of the necessary programmes should be for the learning to proceed in both directions.

Development: Evolutionary ecology’s midwife doi:10.1017/S0140525X1000021X Karthik Panchanathan,a Willem E. Frankenhuis,a and H. Clark Barretta,b a

Center for Behavior, Evolution, and Culture, Department of Anthropology, University of California –Los Angles, CA 90095-1553; bFoundation for Psychocultural Research (FPR) – UCLA Center for Culture, Brain, and Development, University of California –Los Angles, CA 90095-1553. [email protected] http://buddha.bol.ucla.edu/ [email protected] http://www.willem.frankenhuis.org/ [email protected] http://www.sscnet.ucla.edu/anthro/faculty/barrett/

Abstract: We agree with Henrich et al. that documenting cultural universality and variability provides an indispensable window into human nature. We want to stress the mediating role development plays between evolution and culture. Moving beyond the mere documentation of universality or variability, developmental approaches can provide mechanistic explanations, linking ecology to phenotype. Combining phylogeny and adaptationism, evolutionary approaches can explain the properties of developmental systems.

The target article epitomizes a growing appetite for interdisciplinary research, bridging balkanized fields such as psychology, economics, anthropology, and biology. This integration requires “a research program that can explain the manifest patterns of similarity and variation by clarifying the underlying evolutionary and development processes” (sect. 7.2, para. 2). Development BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? must be a foundation stone upon which this new program is built – if not, to stretch our titular metaphor, the conceived integration will be stillborn. Confronted with a catalogue of human universals (e.g., social exchange) and cultural variation (e.g., rituals), social scientists traditionally link universals with evolutionary processes and variation with developmental ones. Echoing Henrich et al., we believe this is a false choice. Among other reasons, developmental mechanisms, including the learning abilities giving rise to cultural transmission, are products of natural selection; universality can arise for several reasons, natural selection being one of them (Jablonka & Lamb 2005); and, evolutionary processes can result in adaptive plasticity, developmental systems capable of constructing a range of adaptive phenotypes, contingent on the local ecology. To illustrate how natural selection can tailor development to local conditions, let us consider the soapberry bug, a half-inchlong, seed-eating insect dwelling in the Southeastern United States (Carroll 1993; Carroll & Corneli 1995). While it takes a male soapberry bug only ten minutes to copulate with a female, he may spend hours more anchored to her by means of specially designed genital hooks. This mate guarding increases fitness by preventing rival males from copulating with a female before she lays her eggs. However, guarding comes at a price – males could be copulating with additional females. The local sex ratio arbitrates this opportunity cost: fewer females leads to more mate guarding. In Oklahoma, where sex ratios vary between populations, males exhibit adaptive plasticity, calibrating the amount of mate guarding to the sex ratio experienced during development. By contrast, in Florida, where sex ratios don’t vary, male soapberry bugs engage in a fixed amount of mate guarding, and, when raised in lab conditions with variable sex ratios, are incapable of calibrating. Soapberry bugs teach us why natural selection and development should not be seen as opposites. Natural selection designs developmental mechanisms, and these mechanisms give birth to phenotypes adapted to their local ecologies. When environments routinely vary, natural selection can engineer developmental mechanisms that use experience to facultatively adjust behavior. Some of the variation across human cultures may be due to calibration, analogous to mate guarding in soapberry bugs. For example, women growing up in harsh environments – where life-expectancies are lower – exhibit earlier onset of menarche and younger age of first birth than women growing up in safe environments (Ellis et al. 1999). The explanation for this accelerated reproductive strategy, seen in other animals as well, may be a quantity/quality trade-off: When long life is a given, organisms invest in prolonged growth and development, resulting in fewer but higher quality offspring; when life is short, organisms forgo further growth and development, focusing instead on maximizing the number of offspring (Belsky et al. 1991). The developmental system, in this case determining the timing of reproduction, can thereby produce a correlation between ecology and behavior. Variation in cultural practices, such as coming of age rituals, may then partially reflect the interaction between evolved developmental processes and the state of the environment. Of course, humans are more complex than soapberry bugs. In addition to calibration, human developmental systems can be “open,” enabling the acquisition of novel skills and information. When combined with culture, a repository of wisdom accumulated across generations, novel skills and information can be passed directly to other individuals, bypassing genetic transmission (Richerson & Boyd 2005). Infants and children are thus tasked with extracting adaptive cultural information in order to become competent adults; while adults are tasked with teaching them. The lesson, here, is not that social learning precludes evolutionary explanation; instead, the psychological mechanisms subserving cultural transmission should be viewed as adaptations (e.g., Csibra & Gergely 2009).

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However, culture is more than a consequence of social learning adaptations; culture can impose selection pressures on developmental processes, altering their genetic compositions (i.e., gene-culture coevolution; Richerson & Boyd 2005). As Henrich et al. discuss, the ability of some people to consume milk into adulthood, particularly those from European and some African populations, provides a clear-cut example: A cultural adaptation, pastoralism, and its consequence, the prevalence of milk, created a novel selection pressure on genes, prolonging the production of lactase, an enzyme needed to digest milk sugars. Research on how coevolutionary processes shaped human cognition and development is still in its infancy. Although Henrich et al. survey the rich breadth of the human experience, it is worth underscoring just how special we are: No other animal occupies as many different ecologies, no other animal deploys a comparable range of subsistence techniques, and no other animal exhibits as wide a range of social structures. The propensity for this plasticity makes sense only in the light of evolution. Fear not: An evolutionary explanation need not be simplistic; a thorough explanation of human plasticity requires, at a minimum, phylogenetic, paleo-ecological, cross-cultural, and adaptationist considerations. For example, recent research on past climates points not to a static evolutionary ecology, but to one in which climate change was the norm (reviewed in Richerson et al. 2001). Further, the timescale of this climatic variation was short, particularly during the late Pleistocene (120,000 to 10,000 years ago) when environments changed radically on the order of hundreds to thousands of years, a situation best tracked by cultural adaptation, rather than genetic evolution (too slow) or individual learning (too error prone). Our human nature, housing a rich array of evolved developmental mechanisms capable of open-ended, facultative adaptation, may have been conceived in this nurturing cradle of change. Learning precisely how the human mind emerged from the evolutionary process poses a challenge that some believe insurmountable (e.g., Lewontin 1998). We remain optimistic. Progress will be made as research becomes increasingly interdisciplinary. Scholars interested in developmental processes will benefit from attending to cross-cultural studies, as processes often reveal themselves through their manifestations in different ecological contexts. An understanding of developmental processes will benefit students of culture, as development links ecology to behavior. An evolutionary perspective can illuminate why humans have the particular developmental mechanisms they do, given our species’ evolutionary history.

ODD (observation- and description-deprived) psychological research doi:10.1017/S0140525X10000221 Tage S. Raia and Alan Fiskeb a Department of Psychology, University of California –Los Angeles, Los Angeles, CA 90095; bDepartment of Anthropology, University of California – Los Angeles, Los Angeles, CA 90095. [email protected] [email protected] [email protected] www.rmt.ucla.edu

Abstract: Most psychological research consists of experiments that put people in artificial situations that elicit unnatural behavior whose ecological validity is unknown. Without knowing the psychocultural meaning of experimental situations, we cannot interpret the responses of WEIRD people, let alone people in other cultures. Psychology, like other sciences, needs to be solidly rooted in naturalistic observation and description of people around the world. Theory should be inductively developed and tested against real-world behavior.

We applaud Henrich et al. for their cogent demonstration of the need for more representative samples in psychological research

Commentary/Henrich et al.: The weirdest people in the world? in order to permit generalization to the human species. However, even if participant samples are representative, the psychology elicited by experiments that require participants to make judgments in response to hypothetical situations, answer abstract questions, or behave in response to artificial laboratory tasks may not be representative of – or even very informative about – human psychology across most domains of life. To understand human nature, our methods must explore the psychology of natural human experience. By this, we mean that psychological theory must be grounded in detailed observation and description of everyday life across cultures in order to understand the cognitive mechanisms that operate in the naturally occurring situations to which humans are adapted. Observation- and description-deprived (ODD) research programs often wander far from real-life psychology because they become increasingly oriented to addressing the precedents and frameworks of previous ODD research and theory. For example, Henrich et al. show how theories about judgments of fairness and cooperation have to take into account the culture of the people making those judgments. But there is a further problem with basing our psychological theory on studies of economic games: Behavior in artificial games does not correlate strongly to social behavior in the community (Gurven & Winking 2008). If the cooperative behavior and fairness judgments we want to understand are those that occur in everyday behavior in communities, rather than only those that are specific to the particular artificial framework of the Ultimatum Game, then we need real-world validity. That is, we need experiments that make sense to participants because the psychology of the experiment matches the psychology of behavior in the real world. ODD psychological research programs rarely provide evidence regarding the ecological validity of the results based on experiments that typically use extremely impoverished stimuli, severely restrict responses, or are based on hypothetical scenarios and Likert-scale responses to questions about abstract concepts. Moreover, we can only interpret data if we know how participants have interpreted the research situation, the task, and the stimuli. For example, WEIRD people are used to identifying themselves and stating their interests and values, and typically welcome the opportunity to do so. But even WEIRD Scandinavians find this an uncomfortably unnatural practice; on the first day of seminar, a Scandinavian student whose turn comes to “say a bit about yourself” is embarrassed and confused by this American practice, which feels uncomfortably self-promoting (Lotte Thomsen, personal communication). In Burkina Faso, Moose informants find any personal questions threatening and demeaning: “To ask about my thoughts, desires, or activities is to seek control over me, possibly in order to thwart or harm me.” Given this interpretation of an interview or questionnaire, Moose responses mean something quite different from the responses of Americans, who tend to perceive personal questions as a welcome invitation to assert themselves and make themselves look good. If we do not know the psychocultural meaning of an experience, we cannot understand the meaning of responses to the experience. Attachment research is based on infants’ behavior when their caretakers leave them in a “strange situation.” In most African cultures, infants are on the body of their mother or other close kin much of the time, sleep with them, and are never out of sight of their immediate family members. Families expect children to form inalienable bonds of interdependence with them. In contrast, German infants sleep alone, are often left in daycare for many hours with strangers, and in early childhood are expected to play alone and are often left at home unattended (LeVine & Norman 2001). German parents foster self-reliance and autonomy. Consequently, being separated from the mother in the strange situation has completely different meanings for African and German infants, so their responses cannot be directly compared.

Psychological theory over the past 40 years has been formulated mostly on the basis of prior theory, data, and intuitions. As researchers are largely from WEIRD populations, the theoretical constructs that inform experimental design tend to be based on WEIRD intuitions and stimulated by ODD data and theory. If our goal is to understand human thinking and behavior in the world, we must leave our desks and begin collecting an extensive and rich body of naturalistic descriptive data based on various kinds of observation. Currently, experimental papers are accorded the highest prestige in psychology and comprise the vast majority of studies published in top journals (Cialdini 2009). Experimental controls are invaluable, but they are useless if the constructs being tested are invalid or the experiment elicits unnatural psychology. A natural science of psychology should be based on naturalistic study in the real world of diverse situations in diverse cultures. Just as botany, zoology, ecology, geology, astronomy, chemistry, and physics grew out of, constantly return to, and must ultimately be validated by observations of the natural world, so psychology should be. In addition to relying on analytic and functional approaches, psychological constructs should be cultivated inductively from observation and we should grow our theories by contemplating naturally occurring patterns of action. Experiments are one way to test such constructs and theories, but they are not the only way. Often they are insufficient if we want to make inferences about behavior outside the lab, beyond key presses and pencil marks.

What is really wrong with a priori claims of universality? Sampling, validity, process level, and the irresistible drive to reduce doi:10.1017/S0140525X10000233 Philippe Rochat Department of Psychology, Emory University, Atlanta, GA 30322. [email protected] http://www.psychology.emory.edu/cognition/rochat/lab/index.html

Abstract: Catchy acronyms such as “WEIRD population” are good mnemonics. However, they carry the danger of distracting us from deeper issues: how to sample populations, the validity of measuring instruments, the levels of processing involved. These need to be considered when assessing claims of universality regarding how the mind works “in general” – a dominant and highly rewarded drive in the behavioral and brain sciences.

As behavioral scientists, we have to come to terms with the fact that, if we want to survive in our culture, publishing and building a reputation that brings recognition, not to mention salary and grant money, it is better to account for what is stable and predictable. It often has better academic pay-off than trying to account for what is diverse and variable. In academia, a priori claims of universality sell better than diversity, which complicates rather than simplifies matters. Universality claims get more attention because they are cleaner and sharper, projecting more encompassing control and predictive power in the field. Such claims are also better didactic tools. They have all-around greater impact and appeal. This tends to relegate diversity to noise rather than a primary object of study. There is no clear escape from such reality. Yet, as scientists, we have to be constantly reminded of how bully-ish and presumptuous we intrinsically are in our reductions and generalizations. This is what Henrich et al. do pointedly in their target article. They deserve to be congratulated for their effort, in the footsteps of Arnett (2008) and Rozin (2009). However, there are some important points that they either omit, or at least seem to gloss over too quickly. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? First of all, the serious issue of universal validity cannot be resolved by just testing a larger, more culturally diverse population. The sampling of diversity is crucial. The idea of setting up labs at international airport terminals, as recommended in the article, will simply not do it. Think about it: Who is traveling to foreign countries by plane? Those internationally “rich” ones would arguably be no more diverse (hence representative) than college students. They might be as “weird” and non-representative, not a good measure of what is universal or variable about the mind. As a measure of the sampling issue problem, be reminded that 80% of the world’s population lives on a family income of less than $6,000 a year, with half of the world’s population living on an average of 2 dollars a day (Kent & Haub 2005; UNDP 2006). Who would claim that poverty has no impact on shaping the mind? However, dividing the world into rich and poor would include major confounds, and the question is where to set the limits of the population category boundaries? The question, somehow eluded in Heinrich et al., is: how to sample diversity and what rationale to adopt? What are the criteria? Geographic? Ecological? Physiological? Socioeconomic? Demographic? Cultural? These are important considerations and, in the end, it all depends on the theory behind the quest for universality. Diversity cannot be controlled just for the sake of it, particularly if the ultimate motive is to determine what is invariant and what changes across populations. The delicate question, somehow glossed over by the authors, is: On what basis do we cluster populations? Such decision can often be “shaky.” For example, catchy acronyms like “WEIRD” for a population sample are good mnemonics. However, they carry the danger of distracting us from deeper issues. The last letter, D, for example, stands for “Democratic.” What does this mean, given that many Eastern cultures would not consider themselves as non-democratic, having universally elected parliaments in their countries? In using such an acronym to characterize a population sample, the authors must have a theory about what democrats and a democracy mean. They must also have some intuition as to what kind of impact such a regime might have on its citizens, as opposed to another. The democratic criterion would deserve more articulated rationale. It is not as easily measurable compared to race, wealth, or education, yet it probably captures something important. The question is what? The name of the game in science is to generalize from limited population samples: samples that are easily accessible, easy to work with, and closer to the culture we as scientists know best. In this respect, there is nothing wrong about ethnocentrism. From such sampling we keep building paradigms on how “children,” “folks,” and “the mind” work. In the scientific context, the claim of universality is neither wrong nor presumptuous. It is a goal and a measure of scientific truth. The problem is that such a measure is always relative to its instrument. What seems to be really wrong with a priori claims of universality is not the lack of population diversity, per se, as implied in Heinrich et al. It is the use of Western “WEIRD” instruments to measure behavioral phenomena across cultures. The use of Westernized procedures and experimental paradigms thought out to fit Western intellectual ecology and preoccupations (e.g., two-dimensional symbolic optical illusions such as the Mu¨llerLyer one, instead of ecologically more valid, hence potentially more universal, optic phenomena such as the “moon illusion”; the use of “weird” anonymous, and on the whole abstract, oneshot economic games; “weird” abstract questionnaires and other I.Q. testing) might well account for why typical Western samples are systematic outliers displaying extreme, non-representative behaviors. Behavioral sciences, historically a WEIRD practice, create culturally specific objects and tools of investigation to measure our predominantly WEIRD population against other populations. This is the true guilty aspect of ethnocentrism. Last but not least, it is essential to distinguish levels at which universality claims may apply. The macro levels of economic

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games or self-assessment surveys are incommensurably more subject to population variability compared to lower level processes such as face recognition or emotional expression. This needs to be taken into consideration, the problem being not only how different populations are, but instead and more importantly, how populations vary in their distribution. But this distribution certainly depends on whether we are dealing with a low-level processing, such as emotion recognition, or higher level, such as self-concept. To conclude, it would be disingenuous to think that we are not in the business of trying to approximate natural laws, whether these laws account for the homogeneity or diversity (context and culture dependence) of phenomena that, by consensus, need to be falsifiable. This is the context in which scientific claims of universality should be read and discussed. Whether population variability is ignored or not, the assumption of universality is true until proven wrong. As in any scientific debate, there is no end in sight.

The weirdest people in the world are a harbinger of the future of the world doi:10.1017/S0140525X10000312 Paul Rozin Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104-6241. [email protected]

Abstract: Although North American undergraduates represent about 0.2% of humanity, and a very unrepresentative subset, they actually provide an advance look at what humanity is becoming. In the face of globalization, this is all the more reason to study the wonderful variants of the human condition before they become homogenized.

I am enthusiastically positive about Henrich et al.’s target article. The message is central. Although the bottom line has been mentioned before in print by some of the authors and others (including myself), never has it been so thoroughly documented and elaborated into all the domains in which it is relevant. And never so convincingly. For studies of humans in their social world, the North American undergraduate (NAU) does not serve as the fruit fly or E. coli has served for genetics. But at the level of basic psychological processes, such as learning, motor organization, or vision, the NAU is probably a pretty good fruit fly. Many basic psychological processes seem about the same in almost all humans, and great progress made by psychologists in areas such as vision can be attributed in large part to the appropriateness of NAU subjects, as well as animal models, of course. However, in the social world, in understanding humans as whole creatures negotiating the world, the NAU is a very unfortunate choice. Convenience, of course, is the justification, and this is very important; fruit flies and E. coli are very convenient ways to study genetics. North Americans constitute an atypical 5% of humanity, and North American freshmen and sophomores at research universities constitute an even more atypical 0.1 or 0.2% of humanity. But even for North Americans, the freshman or sophomore is very atypical, because this person is at a unique life transition, between family life and an entirely peer-centered life usually away from direct family influence. I have shown that on a number of beliefs about the social world, the grandparents of NAUs are as much like Asian Indian college students as they are like their American grandchildren (Rozin 2003). A sample of 30-year-old Americans would give us a much better picture of American adulthood, because such individuals usually have a reasonably settled life course and family. It is ironic that the one special value of undergraduates for psychology

Commentary/Henrich et al.: The weirdest people in the world? has to do with how humans, in one important cultural setting, manage the transition to independence. Psychologists, at various points in time, have been interested in transition periods such as weaning, toilet training, entry into the school system, and adolescence. Here is another interesting transition point that receives surprisingly little study, for a population that is highly appropriate to examine human adaptation to change. But the main point of my commentary is that although the NAU is truly anomalous, this subspecies of Homo sapiens is a vision of the future. With the Internet, ready availability of information of all sorts, computer fluency as key to success in the world, and ease in negotiating a world where text as opposed to face-to-face interactions are the meat of human relationships, the NAU is at the vanguard of what humans are going to be like. So study of them has some justification, not for understanding the human condition now or in history, but as to what it may become more like in the future. This is, as it were, a lucky break for academic psychology, a consequence of globalization and the computer/media revolution. Already, there has been a major change in an important area of socialization. It is now parents who are learning from their children how to navigate the Internet, order online, and navigate the modern electronic world. This is a real change in the direction of knowledge transmission. Insofar as the social world has transmuted to email, Facebook, and ordering online, it is the undergraduates who best illustrate how humans will function. Globalization, the growing availability of access to computers and the Internet, Internet dating services, the decline of face-to-face markets, automated telephone answering services, WalMart, and the like, are rapidly homogenizing the world, making more and more people like NAUs. Because of globalization, it is especially important that we understand now the different worlds that humans have created – the physical worlds (e.g., cities, markets, architecture), the institutional worlds, the social alliances, and the mental maps of the world – before they become much more homogenized. So the prescient virtues of the NAUs make it more important that we fully explore human potential and human history at this time. We don’t have much time, and the distinctive and elaborated different cultural worlds of interpersonal interactions, institutions, value systems, and the like, are a threatened species. The wide range of cultures in the world are wonderful and enlightening examples of the human condition and human potential, and we should cherish them and rush to understand them.

Donald Campbell’s doubt: Cultural difference or failure of communication? doi:10.1017/S0140525X10000245 Richard A. Shweder Department of Comparative Human Development, University of Chicago, Chicago, IL 60637. [email protected] http://humdev.uchicago.edu/people/faculty/shweder.shtml

Abstract: The objection, rightfully noted but then dismissed by Henrich et al., that the observed variation across populations “may be due to various methodological artifacts that arise from translating experiments across contexts” is a theoretically profound and potentially constructive criticism. It parallels Donald Campbell’s concern that many cultural differences reported by psychologists “come from failures of communication misreported as differences.” Ironically, Campbell’s doubt is a good foundation for investigations in cultural psychology.

Near the end of the last essay ever written by the great psychologist Donald T. Campbell, one reads:

I would like to close . . . with a focus on the most ubiquitous source of error in efforts to know the other. This is to interpret as a cultural difference what is in reality a failure of communication. . . . I personally am convinced that many of the cultural differences reported by psychologists and others using questionnaires or tests come from failures of communication misreported as differences. (Campbell 1996, p. 165)

I hope the present target article by Henrich et al. has an impact on the way research is designed and reported in psychology: that there will be more specification of the boundary conditions for generalizations about human behavior, a greater reluctance to rush to claims about fundamental processes, and far less reliance on collecting data from college undergraduates. Here I focus on Don Campbell’s doubt. I suggest that the objection, rightfully noted but then dismissed by Henrich et al., that “the observed variation across populations may be due to various methodological artifacts that arise from translating experiments across contexts” (sect. 7.2, para. 2) is a theoretically profound and potentially constructive criticism. Evaluating claims to universality of psychological theories in the light of cross- cultural evidence is of course not a new enterprise. An early example is Margaret Mead’s (1932) critique of Jean Piaget’s claims in his book The Child’s Conception of the World (Piaget 1929). Piaget described young Swiss children as animistic and proposed a universal ontogenetic sequence in which animism waned over time. Mead produced evidence from a small-scale society in the Admiralty Islands suggesting that among the Manus, the young children were dreary literalists and only learned to be animists later in life. Henrich et al. write: “As children already show large population-level differences, it is not obvious that developmental work can substitute for research across diverse human populations” (sect. 3.1, para. 5). Margaret Mead would not be surprised. An even earlier example is the 1895 Cambridge University Torres Straits expedition headed by the anthropologist A. C. Haddon, which set up an experimental psychological laboratory in New Guinea (see Cole 1996). William McDougall ran experimental tests of sensation concerning the limen of dual impression upon the skin, using a small pair of carpenter’s dividers with blunt metal points. Native subjects were asked to perform an apparently straightforward task following an apparently easy-totranslate instruction: to say “one” or “two” as they judged whether one or two points touched the skin. McDougall reported that “Murray Islanders have a threshold of tactile discrimination of which the value, in terms of distance of two points touched, is just about one half that of Englishmen, or we may say in other words, that their power of tactile discrimination is about double that of Englishmen” (quoted in Titchener 1916, p. 206). The claim was challenged by E. B. Titchener, whose 1916 essay defending the proposition that “human nature is much the same the world over” should be read by every anthropologist and cultural psychologist who believes otherwise (and I am one of them; see Shweder 1990; 1991; 2003). Titchener raised a series of methodological objections, including the way subjects interpreted the purpose of the task. He argued that the subjects in New Guinea, when deciding whether to report “one” or “two,” were looking for “a sensation perceptively different from that yielded by a single point,” which is not exactly the same as looking for two distinct points of sensation. Educated Englishmen, Titchener suggested, simply interpreted the notion of duality of impression in a stricter sense. He judged that the evidence from New Guinea did not show that the Murray Islanders’ powers of tactile discrimination were substantially different from those of subjects in his own WEIRD (Western, Educated, Industrial, Rich, and Democratic) society. The relevant methodological point can be expressed this way: Evidence of psychological differences between cultural groups may simply (although significantly) demonstrate that different stimulus situations produce different responses. The identity of a stimulus situation (e.g., an experimental task) does not exist BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Henrich et al.: The weirdest people in the world? independent of the subject’s interpretation of its meaning. Hence, the more different the culture studied from the culture where the stimulus situation was invented, the more likely the meanings of the task will be harder to communicate with the requisite exactitude in distant field contexts. A revealing illustration can be found in cross-cultural research on the Ultimatum Game (see Henrich et al. 2005). Were crosscultural researchers working across 15 cultures in fact able to standardize the practice and subjective meaning of “playing a game with an anonymous other”? Consider, for example, the behavior of the Au and Gnau peoples of Papua New Guinea. Many “proposers” offer more than 50% of the available currency. Many of these offers are turned down by the “responder,” leaving both “players” with nothing. The “WEIRD” populations of the world don’t play that (apparently strange) way. What description of goals, values, and pictures of the world can help us understand what the stimulus situation actually meant to those New Guinea subjects? Henrich et al. (2005) offer an explanation: “The rejection of seemingly generous offers, of more than half, may have a parallel in the culture of status-seeking through gift-giving found in Au and Gnau villages, and throughout Melanesia. In these societies, accepting gifts, even unsolicited ones, implies a strong obligation to reciprocate at some future time” (p. 811). Is that not prima facie evidence that the very idea of playing with an anonymous other did not compute or translate well in the mind or language of those non-WEIRD subjects, and that in effect they were not playing the same game as the one played by a typical “weird” American college student? Indeed, the very idea of cultural difference might well be described as not playing the same game. Hence, we learn much about the culture-specific mentality of Melanesian peoples by trying to give a “thick description” of their local goals, values, and pictures of the world, so as to understand how and why the Ultimatum Game becomes a different stimulus situation (a different affordance, if you prefer that concept) as it crosses borders and travels around the world. I suspect Don Campbell would have been pleased to see the field of cultural psychology built on the basis of his methodological doubts.

Philosophy and WEIRD intuition doi:10.1017/S0140525X10000257 Stephen Stich Department of Philosophy, Rutgers University, New Brunswick, NJ 08901-1107. [email protected] http://www.rci.rutgers.edu/ stich/

Abstract: From Plato to the present, philosophers have relied on intuitive judgments as evidence for or against philosophical theories. Most philosophers are WEIRD, highly educated, and male. The literature reviewed in the target article suggests that such people might have intuitions that differ from those of people in other groups. There is a growing body of evidence suggesting that they do.

In the opening pages of Plato’s Republic, Cephalus suggests that what justice requires is speaking the truth and paying one’s debts. But Socrates immediately offers a thought experiment to show that Cephalus’s account of justice is not correct: Suppose that a friend when in his right mind has deposited arms with me and he asks for them when he is not in his right mind, ought I to give them back to him? No one would say that I ought or that I should be right in doing so, any more than they would say that I ought always to speak the truth to one who is in his condition. (Plato 1892, The Republic, Book I, p. 331)

When Cephalus agrees, Socrates concludes that “speaking the truth and paying your debts” is not an adequate account of justice.

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Philosophy has changed in many ways in the 2,400 years since Plato wrote the Republic, but the method Plato uses in this passage is still one of the most basic tools in the philosopher’s toolkit. Although there is some debate about how, exactly, the method should be characterized, the basic outlines are clear enough. A philosopher describes a real, or more often an imaginary, situation and asks his audience whether the people or objects or events described have some philosophically important property or relation: Is the agent’s action unjust? Was it morally wrong to push the large man off the footbridge to stop a trolley that would otherwise kill five people? (Thomson 1976) Does the person who holds a ticket in a lottery where the odds are one in a thousand know that the ticket won’t win? (Kyburg 1961, p. 197; see also Hawthorne 2004). Does the “Chinese Room” understand the story? (Searle 1980) When things go well (as they always did in Plato’s dialogues!), the audience will agree that the answer is intuitively obvious, and the philosopher will conclude that the content of these intuitive judgments is probably true. If it is intuitively obvious that it is wrong to push the man off the footbridge, and that is what the philosopher’s theory entails, then this counts as evidence for his theory. If it is intuitively obvious that the Chinese Room does not understand the story or have other intentional states, but a philosopher’s theory entails that it does, this counts as evidence against his theory. The overwhelming majority of philosophers who use this method are WEIRD. Moreover, as reflected in my choice of pronouns, the majority of those who teach in North American and European philosophy departments are male. And, of course, on at least one dimension, professional philosophers are extreme outliers among WEIRD people, as most of them have undergone five or more years of training and vetting in one of the 30 or 40 leading graduate programs. About a decade ago, as we became acquainted with the emerging literature reviewed by Henrich et al., my colleagues and I began to wonder whether these WEIRD philosophers might have weird philosophical intuitions – intuitions that differed from those of people who did not share their cultural and educational background. To find out, we did something philosophers often do – we scoured the literature. We also did something that philosophers rarely do: We designed and ran our own experiments aimed at finding out whether people who were not WEIRD shared the intuitions that play such a central role in Western philosophy. Although this work is still in its infancy, I think it has begun to provide an important addendum to Henrich et al.’s survey suggesting that, in a number of areas of philosophy, the intuitions of philosophically trained, WEIRD males are indeed quite different from the intuitions of people in other cultural groups. Space does not permit an exhaustive discussion of the literature, but here are some of the highpoints. Epistemology. 1. Weinberg et al. (2001) and Nichols et al. (2003), showed that American students of European ancestry and American students of East Asian ancestry have different intuitions about a variety of thought experiments that have played a central role in contemporary philosophy. They also report differences in intuitions between high- and lowsocioeconomic status (SES) participants, where years of education was the major determinant in classifying a participant as high or low SES. 2. Starmans and Friedman (2009) found a large gender difference in intuitions on a “Gettier” case similar to those that are widely discussed in the philosophical literature. In recent years, there has been growing concern about the under-representation of women in philosophy (Haslanger 2008). Though the phenomenon almost certainly has many causes, this striking finding suggests one that most philosophers have been loathe to consider: Many women students may have intuitions that differ from those their male professors insist are correct. Ethics. 1. The classic work of Brandt (1954) reports some dramatic differences between the moral judgments of Hopi people and white Americans that apparently cannot be explained by differences in factual beliefs. 2. Abarbanell and Houser (in press) report that in a variety of carefully controlled experiments, rural Mayan participants did not exhibit the

Response/Henrich et al.: The weirdest people in the world? “omission” bias that has been widely documented in a number of largescale societies. Philosophy of language. Machery et al. (2004) report that Hong Kong Chinese and Americans of European ancestry have different intuitions on a thought experiment that has played a central role in the philosophical theory of reference. Deutsch et al. (in preparation) reinforce these findings, and Machery et al. (2009) include a preliminary report of additional studies in Mongolia, India, and France. (For further discussion, see Machery’s commentary.) There is no shortage of debate about the robustness of these findings and about their implications for the viability of the venerable philosophical practice of using WEIRD people’s intuitions as evidence (Mallon et al. 2009; Sosa 2009; Stich 2009). My own view is that these studies pose a major challenge to that practice, because, when the intuitions of different groups diverge, there is no reason to think that WEIRD people’s intuitions are more likely to be true.

Authors’ Response Beyond WEIRD: Towards a broad-based behavioral science doi:10.1017/S0140525X10000725 Joseph Henrich,a,b Steven J. Heine,a and Ara Norenzayana a Department of Psychology, University of British Columbia, Vancouver V6T 1Z4 Canada; bDepartment of Economics, University of British Columbia, Vancouver V6T 1Z4, Canada. [email protected] http://www.psych.ubc.ca/ henrich/home.html [email protected] [email protected]

Abstract: In our response to the 28 (largely positive) commentaries from an esteemed collection of researchers, we (1) consolidate additional evidence, extensions, and amplifications offered by our commentators; (2) emphasize the value of integrating experimental and ethnographic methods, and show how researchers using behavioral games have done precisely this; (3) present our concerns with arguments from several commentators that separate variable “content” from “computations” or “basic processes”; (4) address concerns that the patterns we highlight marking WEIRD people as psychological outliers arise from aspects of the researchers and the research process; (5) respond to the claim that as members of the same species, humans must have the same invariant psychological processes; (6) address criticisms of our telescoping contrasts; and (7) return to the question of explaining why WEIRD people are psychologically unusual. We believe a broad-based behavioral science of human nature needs to integrate a variety of methods and apply them to diverse populations, well beyond the WEIRD samples it has largely relied upon.

Frankly, we are stunned. We expected that our target article would provoke ferocious counter-attacks among a substantial cross-section of researchers from several fields. Awaiting the commentaries, we steeled ourselves, bracing for harsh and relentless rebukes. One renowned social psychologist, who had read an early draft, warned us that our colleagues would probably spit on us. What arrived were 28 commentaries from an esteemed and

diverse set of scholars, including anthropologists, economists, linguists, neuroscientists, philosophers, primatologists, and sociologists, as well as cognitive, developmental, personality, and social psychologists. These commentaries largely cohere as an emerging synthesis, offering important expansions and extensions of our argument, as well as raising several interesting points for debate and discussion. There is now sufficient evidence from diverse human populations to indicate that researchers can no longer continue to – explicitly or implicitly – infer the universality of psychological processes or behavior from studying only WEIRD people and their children. Our reading indicates that 23 of 28 commentaries largely support our main thesis, although they raise important issues and fruitful points for debate. Of the remaining five, only one is in decisive disagreement (Gaertner, Sedikides, Cai, & Brown [Gaertner et al.]), with the other four (Khemlani, Lee, & Bucciarelli [Khemlani et al.], Machery, Maryanski, and Shweder) seeming somewhat ambiguous or ambivalent as to their precise views. Of course, it is possible that those who disagree most strongly with our assessment chose not to comment. We look forward to engaging representatives of this position in the future. Our reply is ordered as follows: We (1) consolidate the additional lines of evidence, extensions, and amplifications of our target piece made by various commentators; (2) discuss the importance of integrating experimental and ethnographic methods, and show how researchers using behavioral games have done precisely this; (3) present our concerns with arguments from several commentators that separate “content” and “representations” from “computations,” “learning,” or “basic” psychological processes; (4) address concerns that the patterns we highlight marking WEIRD people as psychological outliers arise from aspects of researchers and the research process; (5) respond to Gaertner et al.’s claim that being members of the same species means we must have the same invariant psychological processes; (6) address criticisms of our categories and rhetorical strategy; and (7) return briefly to the question of explaining why WEIRD people are psychologically unusual. R1. Additional evidence, extensions, and amplifications Here we consolidate additional evidence, extensions, and amplifications of our target article. Seven commentaries reviewed empirical evidence that we did not present. All of this evidence supports the notion that WEIRD people are unusual, and none of it challenges that claim. Several of these lines of evidence are complementary with each other, and suggest some theoretical reasons for the unusual nature of WEIRD people, an issue that we return to in the final section. Nine additional commentaries supplied insightful amplifications, nuances, or extensions of our efforts. R1.1. Additional support for the argument that WEIRD populations are unusual

1. Chiao & Cheon point out that the vast majority of cognitive neuroscience findings are based on WEIRD brains. They then review findings from the nascent field of cultural neuroscience showing how population-level differences in experimental findings reveal themselves in BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Henrich et al.: The weirdest people in the world? brain activity. Some of the psychological differences we discussed regarding the self and holistic versus analytic reasoning can be observed in differential patterns of brain activation. These commentators also highlight differences in brain activation based on socioeconomic status. Their commentary underscores the point that neither imaging brains nor measuring hormones allows one to avoid the challenge of population-level variation. Moreover, because sophisticated cultural learning is an aspect of the evolved human repertoire, our brains are partially self-programmable, so even cultural differences are biological differences (though not genetic differences) – for example, see Nisbett and Cohen (1996). 2. Majid & Levinson emphasize the importance of considering the world’s immense linguistic diversity for studying and theorizing about psychology, especially in light of the unusual nature of English on several important dimensions (e.g., see next item). They also highlight additional evidence indicating how deeply some aspects of language imprint themselves on nonlinguistic aspects of cognition. They point to the uncanny coincidence that the fundamental stock of prelinguistic concepts hypothesized by cognitive scientists corresponds closely to those available in English – the language of many of the researchers and most of the participants – but not to those found in other languages. 3. Amplifying Majid & Levinson’s points with further examples, Machery and Stich discuss studies in America, France, Mongolia, India, and China showing that people have different philosophical intuitions, with English-speakers (and Americans) at one end of the spectrum and Chinese at the other. Apparently, some philosophical theories of reference are based on these “English intuitions.” 4. Stich, in considering the implications of our efforts for philosophy, highlights novel work from experimental philosophy showing population-level differences in philosophical intuitions and moral judgments, including a recent finding on the lack of any “omission bias” in the moral judgments of Mayans. This lack of omission bias contradicts previous claims of universality based on work done purely in industrialized populations (and on the Internet). These initial findings, if replicated and extended, suggest that important elements of philosophical theorizing and conceptual analysis are rooted in local folk intuitions that do not extend to the rest of humanity. 5. Karasik, Adolph, Tamis-LeMonda, & Bornstein (Karasik et al.) review evidence on differences in motor development across human populations, and recent data linking developmental differences to childrearing practices. They also highlight how, in some cases, different developmental trajectories arrive at the same outcome. 6. Fernald, in underlining the extreme narrowness of the samples used by developmental psychologists, reviews evidence linking socioeconomic status, early cognitive stimulation, and long-term cognitive outcomes. Since most developmental work is with infants from WEIRD families, the oft-highlighted developmental milestones for various cognitive and linguistic abilities may reside at the extreme end of the true underlying species distribution. 7. Lancy reviews both ethnographic and experimental work on children from across diverse human populations 112

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showing just how unusual the worlds are of children who grow up in WEIRD societies, and how different their cognitive development can be. R1.2. Extensions of our points

Here we lay out five different ways in which various commentators highlighted, amplified, and extended our efforts. R1.2.1. Dealing with the predominance of WEIRD researchers. One important issue that we were not able

to spend much space on in our target article revolves around the fact that most researchers are themselves WEIRD people. This directly impacts the choice of subject pools, since many researchers study those around them. However, this also may impact theory building and experimental design in a number of ways (e.g., Majid & Levinson, Stich). First, Fessler emphasizes how researchers use their own intuitions, at least at the start, in either theory-building or experimental design. His discussion of research on shame shows how American cultural models of emotion lead to basic elements of this emotion being missed by U. S. researchers, despite the fact that these elements are highly salient elsewhere. He argues that researchers, at least for some topics, would be better off not studying people who are culturally similar to themselves. Second, Bennis & Medin worry about this same point, arguing that researchers’ cultural biases influence the choice of topics and phenomena that are considered interesting. Further magnifying the problem, instruments are then developed and honed in particular populations, which may not be suitable to other populations (also see Rochat). Finally, Meadon & Spurrett suggest that one important way of addressing these challenges is to bring more non-WEIRD researchers into the process. Empirical findings should be peer reviewed by researchers who bring different cultural models and implicit expectations to the problem. We agree with all these suggestions: Researchers can view phenomena from a novel perspective, not constrained by their own intuitions, when they study those from other cultures, and can potentially discover phenomena that they otherwise would not see. However, we disagree with an extreme version of this argument, which proposes that researchers should entirely avoid studying people from their own culture. Researcher’s intuitions about the ways people in their own cultures think can be a useful source of understanding in building theories and in honing research instruments. More non-WEIRD researchers should be brought into the discussion, as well as onto collaborative research teams. Research teams themselves that better reflect broad global diversity can more effectively address the challenges delineated by Fessler, Rochat, and Bennis & Medin. With regard to these points, it is instructive to consider why psychology is more dominated by American research than any other science (May 1997). One possibility is that pursuing a career in psychology is a luxury that people cannot afford until the countries and societies in which they live have achieved sufficient economic development. This may be part of the explanation, although this would not explain why universities in wealthy societies like those of Japan and Western Europe typically have

Response/Henrich et al.: The weirdest people in the world? proportionately smaller complements of psychology researchers and majors than do North American universities. Another possibility, which we highlight here, is that the field’s emphasis on WEIRD samples, coupled with the guiding assumption of universal psychological processes, tends to unintentionally marginalize international research. If non-WEIRD researchers are interested in extending findings initially established with WEIRD samples in their home populations, such as findings associated with motivations for self-enhancement, they may well be unable to replicate the American results. The implicit assumption that self-enhancement motivations are similar everywhere would suggest that such failed replications are not due to the nature of the samples studied but instead due to some kind of unspecified deficiency in the methods of the non-WEIRD researchers. American researchers have a distinct advantage in that the field’s key theories were largely constructed on data from American participants, and we suggest that this is likely why American research constitutes 70% of the field’s citations. International research suffers from the disadvantage of trying to extend American-based theories with participants who often have different psychological tendencies, yielding results that are difficult to interpret while embracing an untested assumption of universal psychological processes. In contrast, if the field comes to recognize that psychological phenomena cannot be assumed to be universal until demonstrated as such, then research conducted by non-WEIRD researchers, guided by non-WEIRD intuitions, and studied with non-WEIRD samples, would come to be viewed as particularly important for understanding human psychology. R1.2.2. Existential proofs. Ga¨chter’s commentary also

extends one of our discussion points by underlining the fact that, depending on the research question, WEIRD subjects may be suitable, or even ideal. In our target article we wrote that Research programs that are seeking existential proofs for psychological or behavioral phenomena, such as in the case of altruistic punishment discussed earlier (e.g., Fehr & Ga¨chter 2002), could certainly start with WEIRD samples. That is, if the question is whether a certain phenomenon can be found in humans at all, reliance on any slice of humanity would be a legitimate sampling strategy. (sect. 7.1.6)

We pointed both to Kahneman and Tverky’s work on rationality (e.g., Gilovich et al. 2002) and to Rozin’s work on magical thinking (Rozin & Nemeroff 1990) to highlight situations in which WEIRD samples are either suitable or ideal. However, if one’s goal is ultimately to construct (rather than tactically falsify) theories of human behavior, it is hard to see how that could be done without expanding beyond WEIRD subjects. R1.2.3. Differences among chimpanzee populations. Two

commentaries expand on one of our points (Note 14 of the target article) by highlighting the challenge that population-level psychological variation creates for programs comparing humans and chimpanzees (Boesch and Leavens, Bard, & Hopkins [Leavens et al.]). Applying our argument to chimpanzees (though not to other animals?), these commentaries make the point that chimpanzee populations may also vary in their psychological

abilities and motivations, and that this difference is important for comparing wild and captive populations. Broadly speaking, we agree with this point and feel it needs careful attention. Nevertheless, we offer some cautionary notes. First, there are both theoretical and empirical reasons to believe that human population-level psychological variation is substantially greater than that found in chimpanzees. While chimpanzees are a cultural species, with local traditions and some imitative abilities (Horner & Whiten 2005; Whiten et al. 1999), humans are a runaway hyper-cultural species whose genetic endowments, including abilities to adapt ontogenetically, have been shaped by a long history of cumulative cultural evolution, social norms, institutions, and culture-gene coevolution (Henrich 2008; Laland et al. 2010; Richerson & Boyd 2005). With the same basic genetic endowments, humans expanded as foragers to all major continents, across substantial bodies of ocean, and into an immense diversity of environments. Meanwhile, chimpanzees remained stuck in a narrow band of African tropical forests. The impact of culture-gene coevolution has become increasingly clear from studies of the human genome (Laland et al. 2010). Therefore, although we agree that understanding chimpanzees also requires the study of diverse samples, we suspect that populationlevel variation is a far more significant issue for understanding human psychology than for understanding chimpanzee psychology. Second, we note that it is far from clear which way the use of captive chimpanzees in psychological experiments might bias empirical findings. The aforementioned two commentaries provide opposing claims on this issue. Boesch suggests that if populations of human foragers were compared with wild chimpanzees, then the observed psychological and motivational differences would be minimized because of the impoverished social environments of captive chimpanzees and the unusual psychology of WEIRD people. Leavens et al., somewhat contrastingly, summarize evidence indicating that captive and humanreared chimpanzees have declarative pointing abilities more similar to humans (or at least to their human captors) than is found in wild chimpanzees. This suggests that captive chimpanzees may be more similar to some humans than to wild chimpanzees. It certainly seems plausible that life with humans might make chimpanzees more similar to humans, not less. This remains an open and important question, the answer to which is likely to vary depending on the phenomenon under investigation. Finally, some of Boesch’s specific indictments are off the mark. For example, he suggests that Silk et al. (2005) designed experiments with the “ethnocentric assumption that sharing should be preferred over nonsharing,” and then affirmatively cites Henrich et al.’s (2006) work, which argues that these differences result from different culturally evolved norms. It turns out, however, that Henrich was a co-investigator on both projects, and that these chimpanzee experiments were designed with full knowledge of the cross-cultural results, and precisely to test the “cultural norms” hypothesis against alternatives. Moreover, these experiments were done with chimpanzees in Louisiana, which Boesch criticizes, but were also replicated in Bastrop, Texas, prior to publication (Silk et al. 2005), and then replicated again in Leipzig (Jensen BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Henrich et al.: The weirdest people in the world? et al. 2006). Despite the substantial variation in the social environments of these chimpanzee populations, the experimental results were identical in all three sites. Finally, although differences between captive and wild chimpanzees may be important, there is nothing inconsistent about field observations of chimpanzee cooperation and Silk et al.’s experimental results. Pure self-interest can generate plenty of sharing and cooperation in some contexts, and seems to explain much about chimpanzees’ social behavior (Gilby 2006; Tennie et al. 2009). R1.2.4. Generalizing across contexts. The commentaries

by both Konecˇni and Ceci, Kahan, & Braman (Ceci et al.) call attention to another concern regarding generalizability: How well do research findings generalize beyond the methods that are used to test specific hypotheses in laboratory settings? Relatedly, Rochat argues that the choice of narrowly conceived psychological instruments may limit the generalization of findings, as well. We agree with Konecˇni, Ceci et al., and Rochat that there are potential artifacts underlying the findings of many experimental paradigms. Highlighting the need to broaden participant samples does not obviate the need for researchers to study their phenomena with a variety of methods and in different contexts to assess whether their findings are meaningful and generalizable.

many disorders have distinct cultural boundaries, and can best be understood as culture-bound syndromes, such as (1) bulimia nervosa in the West (Keel & Klump 2003), (2) hikikomori in Japan (Sakai et al. 2004), and (3) koro in Southeast Asia (Ngui 1969). Moreover, many universal mental disorders manifest themselves in quite distinct ways across populations, such that presentations of depression (Kleinman 1988), social anxiety disorder (Okazaki 1997), or even schizophrenia (WHO 1973) are associated with different symptoms and prognoses. In his recent book, Crazy Like Us: The Globalization of the American Psyche, Watters (2010) documents how psychiatry has been exporting American models of psychopathologies around the world, such as post-traumatic stress syndrome to Sri Lanka, anorexia nervosa to Hong Kong, and depression to Japan, often with disastrous consequences. The problem lies in diagnosing and treating indigenous presentations of pathologies according to how they appear through the prisms of the culturally limited diagnostic categories of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders, which often increases the distress for both the patient and the community. When real-life interventions are based on a body of research – however extensive – that is disproportionately drawn from WEIRD samples, the implications extend far beyond the accuracy of our theories and into human lives.

R1.2.5. Implications beyond the laboratory. The unusual

nature of WEIRD samples is not solely a problem for researchers; it has implications that extend well beyond the laboratory. We think that Konecˇni is correct in highlighting how automatic assumptions of psychological universality can be problematic when people from one society apply and enforce new norms and policies in another society. As he notes, there can be enormous costs in “the deliberate or unconscious incorporation of WEIRD-based findings into the normative expectations held by international bodies in ‘cognitively distant’ wartorn areas – such as in Rwanda.” International interventions that are based on WEIRD research, or inspired by untested universalist assumptions, may generate ineffective and potentially destructive policies. We emphasize, however, that an awareness of population variability is not a call for unbridled cultural relativism. Findings that reveal population differences do not imply an absence of a universal human nature, but they do indicate that what is universal might not be the same as what emerges from WEIRD participants. The investigation of universals can play a central role in the endeavor to manage international disputes and humanitarian crises, because they stand to possibly provide the only legitimate criteria by which any particular cultural practice or belief system may be understood. As Fox (1973, p. 13) has argued, “We could not plead against inhuman tyrannies if we did not know what is inhuman.” Understanding what is human or inhuman necessarily requires studying people from a diversity of populations. Theories based on narrow sampling also have disturbing implications for the field of psychiatry and the treatment of mental health across diverse cultural contexts. As in the behavioral sciences, psychiatric models have largely been constructed on an empirical foundation that was gathered from WEIRD people. The burgeoning field of cultural psychiatry (Tseng 2001), however, has revealed that 114

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R2. Methods for an interdisciplinary science of human behavior We agree with Rai & Fiske, Astuti & Bloch, and Shweder that a fully interdisciplinary study of human psychology demands an integration of ethnographic and experimental methods. Panchanathan, Frankenhuis, & Barrett (Panchanathan et al.) also recognize the need for research that goes beyond disciplinary boundaries. Experimental methods provide instruments for better measurement and permit the testing of causal hypotheses. Ethnographic methods provide crucial insights for developing theory, designing experiments, and interpreting results, as well as important information for understanding the proximate causes (e.g., ontogenetic processes) of psychological differences (Henrich & Henrich 2007, Ch. 1). However, because our target article was aimed principally at experimentalists, we wrote in the language of experiments. Too often, ethnographers have railed against experiments, but little communication has occurred because ethnographers generally have refused to become fluent in the local language of experimental thought (which is ironic). There are important differences here, however. Our own view is more in line with Rai & Fiske, than with Astuti & Bloch and Shweder, who seem to be emphasizing an approach based on qualitative ethnography with an emphasis on “thick description.” Ethnographic work must be based on systemic, quantitative, and replicable research protocols that quantify the theoretically relevant aspects of life. Alongside in-depth interviews and participant observation, this might involve time allocation, systematic observation, social network measures, conversational recordings, and formal cognitive tasks (e.g., pile sorts). The integration of experimental techniques with ethnography will partially return anthropology to its broader scope,

Response/Henrich et al.: The weirdest people in the world? prior to the intellectually destructive epidemic of postmodernism (Slingerland 2008). As Shweder points out, field anthropologists used to integrate experiments with ethnography (e.g., Edgerton 1971; Mead 1932; Rivers 1901b). What we do not need is greater reliance on ethnographic impressionism, which delivers such spectacles as the Mead-Freeman-Orans debate (Freeman et al. 2000) on the nature of Samoan adolescent sexuality. In the 21st century, scattered teams of highly interdisciplinary researchers have already begun to demonstrate how to integrate ethnographic and experimental findings in a manner that takes advantage of their synergies, going well beyond “thick description” (e.g., Atran et al. 1999; 2005; Barrett & Behne 2005; Cohen 2007; Fessler 2004; Henrich et al. 2005a; 2006; Henrich & Henrich 2007). In Section R2.1 below, we discuss Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies (Henrich et al. 2004), which explicitly integrates insights from long-term fieldwork with findings from behavioral games. R2.1. Meanings and misunderstandings: Behavioral game experiments

In expressing concerns about how experimental participants from diverse societies interpret or conceptualize particular experiments, some commentators have criticized the use of “economic games” across diverse societies (Shweder, Rai & Fiske, Baumard & Sperber, and Astuti & Bloch). We address these specific issues in two ways. First, we show how these criticisms arise from an incomplete reading of the work that has been done using economic games across diverse populations. Second, we use these cross-cultural game projects as an example of how in-depth ethnographic studies can be combined with experimental tools by interdisciplinary teams to address important theoretical questions. Understanding the utility of an experiment requires understanding the theoretical debates that those experiments aim to address. The Roots of Human Sociality Project, which consists of two phases of experiments and ethnography performed in 22 different small-scale societies by a team of anthropologists, psychologists, and economists (see Henrich et al. 2001; 2006; 2010), was designed to examine particular hypotheses related to the evolution of large-scale complex human societies. One hypothesis for the emergence of large-scale human societies proposes that cultural evolution, driven by competition among societies and institutions, favored the evolution of particular kinds of social norms. These norms harness and extend evolved social motivations to foster cooperation, trust, and exchange with ephemeral partners, beyond each individual’s stable local network of kin and repeat interactants. Such norms permitted the formation of market institutions, which encourage market expansion, trade, and economic success. Psychologically, this hypothesis suggests that the inhabitants of large-scale, complex, market-integrated societies will possess default sets of prosocial beliefs, motivations, and expectations about how to treat ephemeral interactants (e.g., strangers or anonymous others). Under this view, the institutions of complex societies, such as markets, ought to correlate positively with prosocial behavior in these contexts.

An alternative hypothesis proposes that cooperation, trust, and exchange in large-scale societies result directly from the misapplication of evolved kin- and reciprocitybased heuristics for interacting in small-scale societies to individuals in larger social spheres (Burnham & Johnson 2005; Dawkins 2006), eventually including nation states. These heuristics for life in small-scale societies, which are not favored by natural selection in large-scale societies, misfire in large-scale societies because these societies have spread only in the last 10 millennia. Crucial to this hypothesis is that cultural evolution cannot substantially alter the social motivations and calculations that determine sociality. These two hypotheses make quite different predictions about the context-specific behavior and motivations of people from different societies toward ephemeral interactants. What kind of experiments might allow us to measure these differences? Ideally, the experiments should have real costs and benefits with the same underlying material payoffs, so we can comparatively measure motivations and expectations. However, there ought to be cues that will tap the predicted sets of context-specific motivations and expectations (norms) for interacting with individuals in the absence of information about their particular relationships (e.g., cues about status, sex, kinship, or future interaction). With their salient cues of cash and anonymity, and their lack of other cues, economic games seem ideally suited for testing the above hypotheses (Henrich et al. 2010). From the beginning, the Roots team leaders knew that deploying these experiments across diverse societies would be challenging, requiring experts on each local culture and qualitative ethnographic information to assess local meanings and interpretations. Long-term anthropological fieldworkers were recruited to design and implement the protocols. Although the Phase I findings did show that market integration was indeed important for predicting prosociality in these contexts, there were also a few cases in which the experiments happened to cue local prosocial norms – interpretations or meaning systems – unrelated to the targeted set of default norms for exchanging with strangers or anonymous others. The project team attended to these alternative interpretations, arguing that it is essential to understand the mapping between the experiments and local norms (Alvard 2004; Ensminger 2004; Henrich & Smith 2004; Hill & Gurven 2004); they also captured much of this variation with a variable related to non-market cooperative domains in their statistical analyses. The observation of how daily life influences the experiments was so important that it was one of the five major points in the team’s 2005 BBS paper (Henrich et al. 2005a). Each ethnographer also wrote a chapter in Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence and in Fifteen Small-Scale Societies, in which they deployed their own interviews, participant observation, and years of ethnographic experience to illuminate the local meanings of our experiments. An important example of this focus on understanding local meanings comes from the team’s investigation of their experimental findings from the Au of New Guinea (see Shweder). This investigation began when ethnographer David Tracer, who speaks Au and had been BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Henrich et al.: The weirdest people in the world? working in New Guinea for 12 years prior to the Roots collaboration, published two papers illuminating his experimental findings (Tracer 2003; 2004). To further address this in Phase II, the Roots team recruited another longterm linguistically skilled New Guinea ethnographer, Alex Bolyanatz. In this second phase, Tracer replicated and extended his prior findings, while Bolyanatz returned home with findings parallel to those of Tracer (Bolyanatz, under review; Tracer et al., under review). It seems likely that in New Guinea, behavioral games map onto prosocial norms that have little or nothing to do with markets or complex societies. This is consistent with decades of ethnography emphasizing the broad-ranging importance of reciprocity norms in New Guinea (Fiske 1991; Sillitoe 1998). After the Phase I findings became known, many researchers expressed the same concerns that have been highlighted by commentators Baumard & Sperber, who write “participants in these games have no information about the rights of each player over the stake and are asked to make a ‘blind’ decision. But who owns the money? . . . Who is the other participant? . . . Does he or she have rights over the money?” Whereas the Roots team argued against these concerns for Phase I (Henrich et al. 2005b), Phase II’s design directly and explicitly addressed them in the standardized game instructions, pre-game tests of participants’ understanding, post-game interviews on game interpretations, contextualized game variants, and games with double-blind anonymous partners. Phase II’s results replicated and extended the Phase I findings in various ways, showing that, among other things, the modifications made to address the concerns raised by Baumard & Sperber have little impact on the results (Henrich & Ensminger, n.d.; Henrich et al. 2006; 2010). Rai & Fiske suggest the experimental games are not meaningful because they do not correlate with anything important in the real world (Levitt & List 2007). They cite Gurven and Winking (2008), who show that socializing, food-sharing, beer-brewing, and well-digging are not correlated with three bargaining experiments among the Tsimane in Bolivia. We question what theory predicts that those domains should be correlated? The above described evolutionary approach to social norms predicts that, if game play does indeed tap norms evolved for interacting with strangers or anonymous others, then the games played by Gurven and Winking ought to be associated with things such as market integration, social scale (community size), and other features related to the operation of largerscale societies – features that capture those elements of social interactions not governed by durable personal relationships. Looking across diverse populations, market integration is indeed highly correlated with experimental measures of prosocial behavior in these bargaining games (Henrich et al. 2010). Similarly, antisocial punishment (Figure 3 in the target article) is highly negatively correlated with GDP (gross domestic product), and predicted by national measures of the strength of the rule of law and measures of norms of civic cooperation (Herrmann et al. 2008). Within populations, trust game measures of trustworthiness predict repaying loans in a microfinance program (Karlan 2005), and predict alumni donations (Baran et al. 2009). Dictator game offers are correlated with donations to hurricane victims (Kam et al., n.d.) and political participation (Fowler & Kam 2007). 116

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Once properly theorized, not only are economic games highly correlated with important real-world phenomena, but we can predict with which real-world phenomena they should correlate. R2.2. Merely methodological artifacts?

Beyond economic games, Shweder, as well as Baumard & Sperber, ask whether the population-level variations found in the psychological literature we reviewed are a product of methodological artifacts arising from different meanings assigned to the experimental settings, or from communication failures between researchers and participants. Could it be that, for example, the extensive cognitive differences we reviewed in perceptual judgments, visual illusions, analytic-holistic thinking, and folkbiological reasoning arise not from differences in cognitive processes, but from different interpretations of the questions or the tasks? While we agree that researchers working across populations should take such methodological concerns seriously, several lines of evidence speak against this being a general problem. First, we emphasize that diverse methodological techniques have often been used that yield consistent findings. For example, Western participants are found to privilege analytic cognitive strategies, whether the tasks measure reaction times in categorization, free recall, patterns of bias in deductive reasoning, or eye tracking in scene recognition. It is hard to see how task interpretation or meaning issues would affect all of these tasks and yield responses in the same direction as the predicted differences in cognitive processes. Moreover, many of the psychological studies we included in our review (Choi & Nisbett 1998; Norenzayan et al. 2002) included control conditions in which there were no population-level differences, and none were expected. This helps establish the meaning equivalence of the experimental contexts and procedures across populations, and undermines a purely methodological interpretation of differences found. Finally, it is important to recognize that the same or similar methods and instruments that have revealed population-level differences have also revealed population-level invariances, often in the same study with the same participants (Atran & Medin 2008; Atran et al. 2005; Haun et al. 2006; Henrich et al. 2006; Norenzayan et al. 2002; Segall et al. 1966). As we point out in our target article, if certain methods count toward establishing invariant aspects of psychological processes, then data that indicate variation have to count as well. As an illustration, consider recent work showing both universal and variable aspects of numerical cognition. In contrast to evidence from WEIRD samples, experimental work from two small-scale Amazonian societies, the Piraha and the Munduruku, suggests that the ability to distinguish quantities digitally beyond the first couple of integers is poor in these groups, whose languages do not include numerals above 3 (Gordon 2004; Pica et al. 2004) – a pattern common in many such societies (Everett 2005). These same experiments also demonstrate that the cognitive ability to estimate quantity approximately, or an analog “number sense” (Dehaene 1997), is found to be strikingly similar irrespective of linguistic variation in counting systems. This analog system is also present in numerous nonhuman species (Hauser & Spelke 2004).

Response/Henrich et al.: The weirdest people in the world? R2.3. Accessing non-WEIRD samples

Commentators Gosling, Carson, John, & Potter (Gosling et al.) discuss an important tool that should allow the behavioral sciences to obtain more diverse samples by reaching non-WEIRD participants through the Internet. We agree that this is a potent addition to the researcher’s toolbox. The advantages of the Internet are the ease and affordability by which international samples can be accessed. We suspect that some fields are probably more likely to change in the ways we prescribe if the changes do not require researchers to alter their habitual practices, or leave their home universities to go out into the world. While the Internet is undoubtedly a valuable tool that needs to be fully exploited, there are some limitations to this approach, as Gosling et al. note. First, though the Internet is amenable to some kinds of psychological experimentation, it will not facilitate the kind of integrated research program that synthesizes tools from across the human sciences, including direct observation, naturalistic field experiments, biomarkers, and qualitative ethnography, into longitudinal studies across the life cycle. Second, the segments of many countries that have Internet access probably share many attributes with WEIRD people already (see Rozin) – they will tend to be rich, educated (at least literate), and often disproportionately from particular ethnic groups. For example, Internet users in Africa are far more likely to be cultural outliers in WEIRD ways relative to the general African population, compared to, say, Internet users in Europe relative to the general European population. Third, many people overestimate the current reach of the Web. Our Table R1 gives the percentage of the total population in each of the world’s major regions that comprises of Internet users (i.e., percentage of Internet penetration across the globe). The percentages for Africa, Asia, and Latin America are not only low, but the distributions are highly skewed. Most African countries have Internet penetrations of less than 1%; and the overall African penetration is distorted upward by the higher distribution in Egypt, South Africa, and Nigeria. The aggregate statistics Table R1. Global distribution of Internet penetration as of 20091

World Regions Africa Asia Europe Middle East North America Latin America/ Caribbean Oceania/ Australia WORLD TOTAL

Population

Internet Users

Penetration (% of Population)

991,002,342 3,808,070,503 803,850,858 202,687,005 340,831,831

67,371,700 738,257,230 418,029,796 57,425,046 252,908,000

6.8 19.4 52.0 28.3 74.2

586,662,468

179,031,479

30.5

34,700,201

20,970,490

60.4

6,767,805,208

1,733,993,741

25.6

for Oceania/Australia are also deceptive, as more than half the countries comprising that region have less than 15% penetration, with six countries showing less than 5% penetration (Solomon Islands, Kiribati, Papua New Guinea, Nauru, Marshall Islands, and Samoa). Fourth and finally, we hope that the ease of this method does not discourage researchers from considering other ways to broaden their research programs to integrate diverse populations. Overall, we are in favor of any additional methodological tools that can be used to study diverse human samples, and we believe the field will be best off by using a wide variety of different tools.

R3. “Basic level” processes, learning, and computational operations Four commentaries emphasize a bipartite partition between (1) mental content and (2) basic or universal, psychological processes (Rozin), variously labeled as “learning” (Danks & Rose), “computational operations” (Khemlani et al.), or “low-level processing” (Rochat). Each commentary emphasizes that while content varies, the underlying computational machine is constant. Danks & Rose write, “there is a natural, defensible distinction between the cognitive ‘objects’ of the mind, and dynamic mental ‘processes.’ Cognitive objects include representations, knowledge structures, and so on.” Similarly, Rozin notes, “But at the level of basic psychological processes, such as learning, motor organization, or vision, the NAU [North American undergraduate] is probably a pretty good fruit fly.” Maybe this is how the human mind operates, like the computer on your desk, but how do we know until we sample a broad range of human diversity? Human learning or computational processes might be self-modifying to adapt to local conditions, so, while all human fetuses might begin with (roughly) the same cognitive equipment, acquired content could provide feedback and alter these “basic”-level learning or computational processes through phenotypic plasticity or cultural transmission. Or, culture-gene coevolution, which is increasingly recognized as a powerful force in human evolution (Laland et al. 2010), could genetically adapt local populations to more effectively acquire and process the local stable cultural representations. Our own folk model of human psychology, which is also rooted in a computer metaphor, conforms to that suggested by these commentators. However, we think the available evidence ought to make us question this metaphor. Let’s first consider vision, since Rozin highlights this. By all appearances, vision seems to be the product of “basic” processes. We have already discussed the population-level variation in the Mu¨ller-Lyer illusion, which, according to Khemlani et al., is “but one single phenomenon in visual perception, hardly representative of all visual perceptual processes.” However, as we noted, there is also substantial variation in the Sander Parallelogram and two versions of the Horizontal-Vertical illusion. But forget illusions. Suppose one was studying why people see so poorly underwater, compared to on land. Can one make universal generalizations regarding the human ability to see underwater by exclusively studying undergraduates? Turns out, no (Gislen et al. 2003). The BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Henrich et al.: The weirdest people in the world? Moken are nomadic sea foragers who live in an archipelago off the coast of Burma. From a young age, Moken subsist by collecting food from the sea floor. Comparisons of underwater visual acuity between Moken and European children show that Moken children have more than twice the visual acuity of their European counterparts. The Moken appear to have acquired the ability to constrict their pupils underwater, thus improving acuity, rather than widening them in the dimmer light, as Europeans do. That is, the pupils of Moken and Europeans do opposite things when they enter water, one adaptive, the other, not so much. Isn’t pupil dilation a “basic” part of visual processing? Rozin also mentions “motor organization” as a basic process. It is not clear to us precisely what this means, but Karasik et al.’s commentary reviews evidence showing how important aspects of motor development vary across human populations, and suggests how this might be related to childrearing or parenting practices. Karasik et al. also discuss how children in some smallscale societies never crawl – instead, they “butt-scoot” or “bum-shuffle.” A “crawling stage” per se is neither universal nor necessary for adult bipedalism. Perhaps a non-psychological example will further sharpen the problem. Suppose you wanted to study the nature of human running. Can you build a universal model of human running that is based on undergraduates, or other WEIRD people? Interestingly, WEIRD people would be one of the worst populations to select for such an investigation. Recent research shows that cushioned running shoes lead to dramatic modifications of the human running profile by causing runners to land principally on their heels instead of the balls of their feet. This difference has substantial implications for how we understand the evolved design and engineering of human feet (Lieberman et al. 2010). If one studies life-long sneakerwearers, the engineering of human feet appears ill-suited for long-distance running. In contrast, studying barefoot runners, particularly life-long barefoot runners, suggests a marvelous evolved design for the human foot, possibly specialized for long-distance running (Bramble & Lieberman 2004). Hence, if you study WEIRD runners exclusively, you again get the wrong answer. The difference between the feet of shod and unshod people has implications even for the interpretation of ancient hominid evolution. In 1978 –79 a 27.5-meter-long trail of footprints hardened in volcanic ash dating to 3.5 million years ago was unearthed at Laetoli, Tanzania. Comparisons of these ancient prints with those of urban North Americans suggested that although bipedal, these ancient hominids were not bipedal in the way Homo sapiens are. The ancient footprints show a separation between the big toe and second toe, an anterior “fanning,” and a substantial arch – all indicating differences compared to the feet of WEIRD humans. However, when the Laetoli prints were compared with those of the Machiguenga, who live a barefoot life of hunting, gathering, and horticulture in the Peruvian Amazon, the Laetoli footprints could not be distinguished from these non-WEIRD footprints (Tuttle et al. 1990; 1991). It turns out that WEIRD people have flat, narrow feet with underdeveloped big toes, which are the product of a lifetime of having one’s feet bound in cushioned shoes. One cannot even safely identify universals about human foot anatomy by exclusively studying WEIRD people! 118

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The assumption that “basic” processes are invariant also needs to stand up to evidence that human brains change in response to experience and cultural routines. Work on neuroplasticity has shown how training and expertise can create both functional and structural differences in brains. But the demands and incentives of developmentally adapting to local social organizations, status hierarchies, performance norms, carpentered corners, and other culturally evolved features of developmental environments persist for longer periods, are probably more constant, and are arguably more intensive than the behavioral regimes associated with occupations, such as those associated with musical training, mathematics, or taxi driving (Reynolds Losin et al. 2010). Yet, musical training creates structural alterations in brains, such as enlarging the anterior corpus callosum and altering the motor and somatosensory maps. Taxi driving increases gray matter in the hippocampus, while training as a mathematician increases grey matter in the parietal cortex. Consequently, however one conceives of these hypothesized invariant “basic processes,” such processes would have to remain constant in the face of the structural and functional modifications in brains that inevitably arise from ontogenetically adapting to culturally constructed environments (Reynolds Losin et al. 2010). As Panchanathan et al. recognize, rather than demanding universal psychological processes, it might be fruitful to think about evolved ontogenetic processes that construct and calibrate diverse psychological processes to local environments, at least for some domains. Recent evidence emerging from collaborations between cognitive psychologists and anthropologists further challenges the distinction between process and content that stands as a virtual axiom in parts of the cognitive sciences. Cultural differences in what people think about loops back to impact how people think (Bang et al. 2007). For example, in folkbiology, differences in what people believe about plants and animals affect memory organization and ecological reasoning about living things. Finally, a key point that is missed by the prevailing arguments supporting a distinction between “universal process” versus “variable content” is that universality is not an all-or-nothing phenomenon. In order to draw meaningful conclusions about what is universal and what is not, one must also make distinctions between different levels of universals that are grounded in empirical observation. For example, the cognitive ability to estimate quantity approximately, discussed earlier, appears to be quite invariant in that it produces cognitive responses with identical effect sizes across populations. In contrast, many processes of central interest to psychology and cognitive science, such as rule-based categorization, geocentric spatial reasoning, or some egocentric motivational biases, are universal in a much weaker sense. They may exist in the psychological repertoires of all peoples, but their use and relative dominance over other competing strategies are contingent on population-level variability in cultural routines and practices. See Norenzayan and Heine (2005) for a theoretical framework for identifying levels of universals. In sum, it is not clear to us what kinds of psychological processes are a priori more likely to be universal. More empirical evidence regarding the degree to which psychological phenomena vary across populations will be of much

Response/Henrich et al.: The weirdest people in the world? utility in addressing this important question. Perhaps, as Machery alludes, such evidence will reveal that social psychological phenomena (whatever those are?) are especially likely to vary across populations. But at this point, there is insufficient evidence to support such a conclusion.2 R4. Are WEIRD populations actually unusual? While agreeing with our central thesis, Bennis & Medin worry that the seemingly extreme nature of WEIRD populations on many dimensions may result from various biases created by the fact that most researchers are themselves WEIRD, and that psychological instruments are developed and honed in WEIRD populations. We are sympathetic with their concerns, although we quibble with some of the details of their critique. Bennis & Medin begin their critique by proposing that a consideration of base rates would suggest that something is amiss with our claim. Their logic implies that it was essentially a random process that determined which society, of all historical and extant societies, happened to accumulate sufficient experimental findings about human behavior from enough populations to begin to consider the question at hand. However, the right way to think about the question they are posing is to ask: What is the probability that a society is psychologically unusual, given that it is the first to aggregate sufficient experimental findings from diverse societies to even explore the question? For starters, such a society has to inherit a scientific and experimental tradition. The society has to be economically successful enough to create occupational specializations for experts in human psychology and behavior. The society has to be willing to place sufficient value on these activities, despite their questionable economic utility. This society must be willing to fund data collection in some diverse populations. And, at least some members of the society have to find this endeavor sufficiently interesting to dedicate their lives to pursuing it. Given all these prerequisites, which limit the number of candidate societies to a handful, we think it is quite plausible that the society which first had sufficient experimental findings to explore the question at all, would itself be psychologically unusual. In fact, many of the findings reviewed in our article and in these commentaries could plausibly be linked to being in a position to explore the question. Commenators Kesebir, Oishi, & Spellman (Kesebir et al.) note that Americans have low pathogen loads and high residential mobility, both of which are correlated with individualism and may promote economic growth. Together, Lancy and Fernald suggest that American (and Western, more generally) childrearing practices may speed the cognitive development of particular skills. We pointed out the unusual lack of co-sleeping in the United States, which may influence independence and self-reliance. Individualistic notions of the self may increase Americans’ curiosity about psychology, which might explain why psychology is so dominated by U.S.-based research. Differences in holistic versus analytical thinking may be linked to epistemic social norms about what counts as “good thinking” (Buchtel & Norenzayan 2008), and may be rooted in the very origins of science (Nisbett 2003). Visual illusion

variability and folkbiological anomalies may be linked to growing up in built urban environments with ample twodimensional representations (artwork, photographs). Motivations for fairness towards anonymous others are highly correlated with market participation, whereas motivations for antisocial punishment strongly negatively predict GDP; both of these factors can impact economic performance. In short, it appears that a society that has conducted and amassed a large majority of the extant psychological data may, for precisely the same underlying reasons, be a psychological outlier, at least on many important dimensions. These are not independent processes, as Bennis & Medin would have it. Having clarified this, we do agree with Bennis & Medin’s more general concern that WEIRD samples may look particularly unusual on those measures that have been selected, developed, and honed for use in WEIRD populations. If Indian psychologists first identified a specific phenomenon that was of particular concern in India, they too might hone their methods to enhance their effects, resulting in Indian participants being outliers for that phenomenon. However, this is precisely the problem that the behavioral sciences face – initial theories are largely derived from WEIRD samples, and, as such, we fail to consider phenomena that might be of greater concern elsewhere. As our target article emphasized, it remains an open question whether, when a full accounting is taken of all the psychological phenomena that exist throughout the world, WEIRD samples will remain any more unusual than other societies. At present, we lack the empirical data to evaluate this possibility, and hope that researchers strive to contribute to this database by identifying and studying phenomena that are more of a concern in other populations. R5. Does variability conceal psychological universals? Is WEIRD WRONG? Gaertner et al.’s commentary stands as the sole one that explicitly rejects our basic claim regarding populationlevel variability in psychological processes. Although this commentary is unique in its criticism of our key arguments, we suspect that many behavioral scientists share the intuitions underlying these commentators’ critique, and we therefore devote substantial space to this in our response. Gaertner et al. maintain that behavioral scientists study the culturally variant phenotypes of an underlying universal genotype. We note that this point is dependent on researchers being able to discern what the underlying universal psychological process is in the first place. But behavioral scientists do not have direct access to this underlying genotypic level; rather, we are in the business of studying questions at the phenotypic level, such as, whether people view themselves more positively than they are viewed by others, whether people succumb to the Mu¨ller-Lyer illusion, or at what point people reject low offers in the Ultimatum Game. The genotypic level is inferred on the basis of the phenotypic evidence; and when the phenotypic evidence comes from a narrow sample, such as North American undergraduates, researchers very well might incorrectly infer what is going on at the genotypic level. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Henrich et al.: The weirdest people in the world? As an analogy, consider the case of understanding pregnancy sickness. All of the major hypotheses for why women get nausea and vomiting during their first trimester are evolutionary explanations. Some hypothesize that pregnancy sickness is an evolutionary byproduct of the body’s need to alter hormonal levels, to permit the growth of a fetus. Other explanations propose that pregnancy sickness is an adaptation triggered by either threats from pathogens (in meat) or toxins (from plants). Which evolutionary explanation is correct? It turns out that key evidence comes from studies of small-scale societies that rely on very little meat and use corn as a staple (which does not have the toxins hypothesized to spark pregnancy sickness as a defense). These societies have plenty of pregnancy, but no pregnancy sickness, thus undermining the byproduct hypothesis (Fessler 2002; Flaxman & Sherman 2000). So, assuming evolved universality does not preclude the need for comparative evidence to adjudicate among alternative hypotheses. Now consider the self-serving attributional bias, which is operationalized as the tendency for people to take personal credit for their successes but to direct blame externally for their failures. An extensive meta-analysis revealed that the effect size for this bias is d ¼ 1.05 for Americans but d ¼ 20.30 for Japanese (Mezulis et al. 2004): that is, the phenotypes are diametrically opposed. What is the underlying universal psychological process here? Just like the blind men trying to identify the elephant, one would reach an entirely different conclusion if one had Japanese data than if one had American data. The most complete view of the underlying beast requires multiple sources of evidence; the more diverse the populations studied, the better researchers will be able to triangulate on the underlying processes, be those universal psychological or ontogenetic processes. The guiding assumption of much of the behavioral sciences has been that human behavior is the expression of universal underlying psychological processes. We submit that it is because of this assumption that the samples studied are as narrow as Arnett’s (2008) analysis reveals. Indeed, there is little point to trek into the New Guinea highlands if highlanders share the same universal psychological processes as the undergraduates in a researcher’s home university. In the target article we questioned whether this assumption is tenable – how does it stand up to the empirical evidence? For some domains (e.g., personality structure; sex differences in some mate preferences) the evidence for universality, at least at the level of functional universals (see Norenzayan & Heine 2005), seems solid. In other domains (e.g., fairness motivations, moral principles, spatial perception), the high degree of variation among populations makes identifying an underlying universal psychological process more interesting (and more work). But we cannot address whether this assumption of universality is supported for a given hypothesized process until there is sufficient comparative data. In some domains, it might be the case that there is not a common, universal psychological process. Many in the behavioral sciences have yet to take seriously the implications of epigenetic inheritance and culture-gene coevolution. A rising tide of evidence from epigenetics is showing how genetic systems modify gene expressions to adapt to local circumstances without altering the 120

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underlying DNA. This can create lasting heritable variation in individuals (epigenetic inheritance), and differences among populations, without any underlying differences in DNA base pair sequences – only differences in gene expression (Jablonka & Raz 2009). Monozygotic twins diverge in their gene expression as they age because their epigenetic response systems modify their gene expressions (Fraga et al. 2005). In addition, potent forces of culture-gene coevolution could mean that different populations have genetically adapted to stable elements of their cultures, such as in the case of lactose tolerance (Laland et al. 2010). This does not mean that there are no basic principles to account for psychology and behavior; it just means that we must move one step back to principles from genetics, epigenetics, culturegene coevolution, and cultural evolution to explain human variation. It seems to us that Gaertner et al. are offering an unfalsifiable hypothesis. They suggest that studying diverse populations will either yield evidence of similarities because of an underlying universal psychological process, or it will yield evidence of differences, which mask the underlying universal psychological process. They do not offer any means for discerning an underlying universal process in the face of population-level variability. Indeed, they do not seem willing to entertain alternative hypotheses that, for example, propose universal ontogenetic processes that give rise to different psychological processes under different conditions during development. This is particularly evident in their discussion of population-level variability in self-enhancement motivations. Gaertner et al. challenge us by claiming that the crosscultural evidence regarding self-enhancement points to much universality. We tackle this claim at length here as it is central to their rejection of our thesis, and it is an ongoing controversy.3 First, they argue that this motivation is universal but expressed differently: Westerners enhance themselves in domains that are important to them (i.e., individualism), while East Asians enhance themselves in domains that are important to them (i.e., collectivism). This question has been investigated using a number of different methods. The results from the “better-thanaverage effect” largely support this hypothesis (Brown & Kobayashi 2002; Sedikides et al. 2003). These are the only findings cited by Gaertner et al. However, the other 11 methods that have addressed this same question (viz., the false-uniqueness bias, actual-ideal self-discrepancies, manipulations of success and failure, situation sampling, self-peer biases, relativelikelihood and absolute-likelihood optimism biases, open-ended self-descriptions, automatic self-evaluations, social relations model, and a corrected better-thanaverage effect) yield an opposite pattern of results – that is, East Asians do not self-enhance more in domains that are especially important to them (whereas Westerners do: Falk et al. 2009; Hamamura et al. 2007; Ross et al. 2005; Su & Oishi 2010). A meta-analysis including all of the published studies on this topic finds no support for this hypothesis (Heine et al. 2007a; 2007b); the meta-analyses cited by Gaertner et al. (viz., Sedikides et al. 2005; 2007a) find different results because they excluded most of the studies that yielded contrary findings. Further, many research programs have documented that the better-than-average effect is a compromised measure of

Response/Henrich et al.: The weirdest people in the world? self-enhancement as it includes a number of cognitive biases that exaggerate estimates of self-enhancement (Chambers & Windschitl 2004; Hamamura et al. 2007; Klar & Giladi 1997; Krizan & Suls 2008; Kruger 1999; Windschitl et al. 2008). That is, the sum total of the available evidence contradicts Gaertner et al.’s claim that East Asians are self-enhancing in domains of special importance to them. A second argument for the universality of self-enhancement that Gaertner et al. offer is that the populationlevel variability is a function of different modesty norms such that the cultural differences disappear with nonexplicit measures. However, other studies of self-enhancement that employ hidden behavioral measures (which Gaertner et al. do not cite) find equally pronounced differences as those with explicit measures (Heine et al. 2000; 2001). Along these lines, Gaertner et al. cite evidence that cultural differences do not appear with the Implicit Association Test (IAT) measure of self-esteem (Greenwald & Farnham 2000). This is the only method out of 31 that did not find a population-level difference in the magnitude of self-enhancement motivations between Westerners (average d ¼ 0.87) and East Asians (average d ¼ 20.01: Heine & Hamamura 2007), yet none of the results from these 30 other methods are discussed by Gaertner et al. Further, the IAT measure of self-esteem has the least validity evidence of any of the IAT attitude measures (Hofmann et al. 2005), and this measure does not correlate reliably with other implicit measures of self-esteem, measures of explicit self-esteem, or other external validity criteria (Bosson et al. 2000; Falk et al. 2009). Hence, at present it is unclear what the self-esteem IAT measures, and it is noteworthy that it is the method that stands alone in not finding a difference in self-enhancement motivations between Westerners and East Asians. Finally, Gaertner et al. argue that self-enhancement promotes adjustment equally in both Westerners and East Asians. But the relationship between self-enhancement and adjustment continues to be hotly debated, even among Western samples, with divergent results emerging depending on the methods used. In general, evidence for this is best with measures of the better-thanaverage effect and self-report measures of adjustment, where the individual answers items regarding both how positively people view themselves relative to others and how positively they view themselves with regards to their adjustment. The evidence is much weaker, and often contradictory, for studies that utilize objective benchmarks of self-enhancement (Colvin et al. 1995; Paulhus 1998; Robins & Beer 2001; but see Taylor et al. 2003). Further, the only published study that measured selfenhancement and depression in both East Asian and Western locations finds a significantly weaker relation between the two constructs among Japanese than Canadians (Heine & Lehman 1999). It is also worth noting that, although evidence for self-enhancement is far weaker among East Asians than Westerners, epidemiological studies find depression rates in East Asia to be approximately one-fifth that of North America (Kessler et al. 1994; Weissman et al. 1996) – a pattern that is difficult to explain if self-enhancement promotes well-being equally across populations. In sum, Gaertner et al. claim that East Asians selfenhance similarly to North Americans, and that this

reveals the universality of self-enhancement motivations. They are only able to make this claim by ignoring the vast majority of the relevant data. The assumption of underlying universal psychological processes is nothing more than that – an assumption, which needs to be evaluated against alternative hypotheses with empirical evidence from diverse human populations.

R6. Misleading categories and contrasts? While agreeing with our major point, Astuti & Bloch suggest that our series of telescoping contrasts, and especially our contrasts between small-scale and industrialized societies and between Western and non-Western societies, distorts or exaggerates the unusual nature of WEIRD people. They charge us with the “uncritical lumping together of a variety of disparate societies” and with using “under-theorized labels.”4 We agree with their concerns about uncritically lumping societies, which is why we sought to carefully avoid such pitfalls. To begin, in our introduction we wrote, “We emphasize that our presentation of telescoping contrasts is only a rhetorical approach guided by the nature of the available data. It should not be taken as capturing any unidimensional continuum, or suggesting any single theoretical explanation for the variation” (target article, sect. 1, para. 7). The first sentence in this quotation was meant to explain how our particular choices of telescoping contrasts were strictly driven by the lumpy and sparse distribution of the available data, not by any theorizing about the nature of the variation. The second sentence, regarding the unidimensional continuum, was meant to explicitly avoid any suggestion of what Astuti & Bloch term a “unilineal path.” Next, because of our concern about lumping disparate societies, we displayed the data whenever possible. Figure 2 in our target article displays the 14 small-scale societies for the Mu¨ller-Lyer illusion, along with two industrialized populations. We also included all the samples of children on that figure. This allows the reader to draw his or her own conclusions, and highlights the degree of variation among small-scale societies. Figures 3A, 3B, and 3C display the means for each of 15 smallscale societies on three different behavioral measures related to fairness. Again, this kind of graphical display was specifically intended to lay all the cards on the table, and avoid concealing population-level variation in broad categories. In the text we wrote, For Dictator Game offers, Figure 3A shows that the U.S. sample has the highest mean offer, followed by the Sanquianga from Colombia, who are renowned for their prosociality (Kraul 2008). The U.S. offers are nearly double that of the Hadza, foragers from Tanzania, and the Tsimane, forager-horticulturalists from the Bolivian Amazon. . . . [F]or Ultimatum Game offers, the United States has the second highest mean offer, behind the Sursurunga from Papua New Guinea. (target article, sect. 3.2, para. 5)

How is this lumping the small-scale societies together? Figure 4 from our target articles displays the available data from nine non-Western and seven Western societies for both the punishment of free-riding and for antisocial punishment. The reader can see the interesting variation within both Western and non-Western societies, BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Henrich et al.: The weirdest people in the world? and can even apply his or her own categorization schemes. In Figure 5, we show the percentage difference between analytic and holistic judgments for six different samples, which we compiled ourselves from different sources in order to illustrate the variation as accurately as possible. Astuti & Bloch express forceful charges against our efforts, while agreeing with our main point. Given this, we believe it would have been more constructive to have explained how they would have presented the data and made the case more effectively. R7. Explaining the variation: Why are WEIRD samples so unusual? For the purposes of our target article we remained largely agnostic regarding explanations for the peculiar nature of WEIRD psychology, although we did point out that this should not be unexpected, given the rather odd ways of life of most WEIRD people, especially compared to those of small-scale societies and the environments of ancestral humans. Contra Danks & Rose, we did not mean to suggest that this variation is fundamentally inexplicable, or that we should not try to explain it. In fact, all three of us (Henrich, Heine, and Norenzayan) have been engaged in trying to explain various elements of it for much of our careers. Several commentaries, when viewed together, suggest two proximate explanations for why WEIRD subjects are often unusual. First, following Majid & Levinson, the English language apparently occupies an obscure corner of the design space of possible languages, potentially giving theorists misleading points of departure or unusual folk intuitions. Majid & Levinson worry that this “English-bias” may be impacting theorizing in the cognitive sciences, while Machery and Stich show that it has impacted philosophical inquiry. By citations, the top four sources of research in psychology are all from Englishspeaking countries (see May 1997): (1) the United States, (2) the United Kingdom, (3) Canada, and (4) Australia (for comparison, the top sources of research in physics are [1] the United States, [2] Germany, [3] Japan, and [4] France). The second explanation combines arguments and evidence offered by Lancy, Fernald, and Karasik et al., suggesting at least the proximate end of a theory that may illuminate a wide range of cognitive difference between WEIRD populations and others. Lancy lays the groundwork by highlighting the relative strangeness, in a broad global and historical context, of modern middleand upper-class American beliefs, values, cultural models, and practices vis-a`-vis childrearing. Fernald and Karasik et al. review evidence that is beginning to document how these practices impact cognitive, linguistic, and motor development, including long-term cognitive outcomes. At a more ultimate level, we speculate that in the context of mobile, meritocratic societies like those of the United States, Western Europe, and Australia, cultural evolutionary processes rooted in our evolved tendencies to imitate successful and prestigious individuals (Henrich & Gil-White 2001) will favor the spread of child-rearing traits that speed up and enhance the development of those particular cognitive and social skills that eventually 122

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translate into social and economic success in these populations. This kind of cultural evolutionary process may be part of what is driving the dramatic increases in IQ observed in many industrialized nations over the last century (Flynn 2007), along with increases in biases toward analytical reasoning and individualism. It would also explain the obsession with active instruction of all kinds shown by middle- and upper-class Americans (Lancy 2008). In our target article we wanted to avoid making any theoretical claims regarding the origins of the psychological differences we highlighted. We suggested that WEIRD psychology probably arises from a myriad of different proximate causal sources that, at best, have been aggregated in WEIRD populations. The two hypotheses mentioned above illustrate this. It is likely a coincidence that the first population to seriously engage in the systematic study of psychology and decision-making happened to speak English. However, it is probably not a coincidence that the economic system of this population happened to favor certain child-rearing practices and ways of reasoning. Overall, we suspect that many of the phenomena for which WEIRD samples occupy extreme positions do so for quite distinct causal reasons, including some researcher-created biases. Once the behavioral sciences accept the existence – or potential existence – of broad-ranging variation among populations, we can commence with the more interesting endeavor of explaining that variation at both proximate and ultimate levels of inquiry.

R8. Closing words We have a vision for the future of scientific efforts to understand the foundations of human psychology and behavior. Research programs need to increasingly emphasize large-scale, highly interdisciplinary, fully international research networks that maintain long-term, ongoing, research projects among diverse populations that collect data over the full life cycle using an integrated set of methodological tools, including wide-ranging experimental techniques, quantitative and qualitative ethnography, surveys, brain imaging, and biomarkers. Questions and methods are best devised and designed at collaborative meetings of these international research networks. NOTES 1. Data from http://www.internetworldstats.com. 2. Machery asks, “Why are most examples in Henrich et al.’s article drawn from social psychology? . . . Would we find the same cross-cultural variation if we focused on the phenomena discussed in perceptual and cognitive psychology textbooks?” Our target article reviews findings from four visual illusions, induction, categorization, memory, attention, spatial cognition, prospect theory, decision biases, perceptual biases, IQ, and spatial abilities. These are all foci in cognitive psychology texts. Several commentaries and our response review evidence/findings on underwater vision, motor development (Karasik et al.), cognitive development (Fernald), and neural activation in the amygdala (Chiao & Cheon). These are not domains of social psychology. 3. Readers interested in the controversy can see Brown and Kobayashi (2002), Heine (2005), Heine and Hamamura (2007), Heine et al. (2007a; 2007b), and Sedikides et al. (2005; 2007a; 2007b).

References/Henrich et al.: The weirdest people in the world? 4. Astuti & Bloch suggest that “small-scale” is a euphemism for “primitive.” The discrete label small-scale societies is a descriptive term, meant to distinguish social groups living in small, geographically distinct populations (e.g., villages) that range in size from a handful to a few thousand. Prototypically, the social organization of these groups is local, and often kin-based. The division of labor is not extensive, and households typically produce a substantial fraction of their own food. Interactions are mostly face-to-face. Of course, all these dimensions are continuous, so this label, like many, is meant to roughly distinguish one region of an n-dimensional space. The term is regularly used by anthropologists in the 21st century (Borgerhoff Mulder et al. 2009; Walker et al. 2006) without carrying any sense of mental or physical “primitiveness.”

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Comorbidity: A network perspective Ange´lique O. J. Cramer Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands [email protected] www.aojcramer.com

Lourens J. Waldorp Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands [email protected] http://users.fmg.uva.nl/lwaldorp

Han L. J. van der Maas Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands [email protected] http://users.fmg.uva.nl/hvandermaas/

Denny Borsboom Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands [email protected] http://sites.google.com/site/borsboomdenny/dennyborsboom

Abstract: The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy. Keywords: comorbidity; complex networks; generalized anxiety; latent variable models; major depression

1. Introduction If suffering from a single mental disorder is bad, suffering from multiple mental disorders (i.e., comorbidity) is worse. Compared to suffering from a single mental disorder, comorbidity is consistently associated with a greater demand for professional help, a poorer prognosis, greater interference with everyday life, and higher suicide rates (e.g., Albert et al. 2008; Brown et al. 1995; Schoevers et al. 2005). Also, among people who meet diagnostic criteria for one mental disorder, approximately 45% receive additional diagnoses (e.g., Kessler et al. 2005b). Thus, comorbidity is a widespread and serious problem, the underpinnings of which need to be unraveled. Indeed, the comorbidity issue has been studied extensively in the past decades (e.g., Anderson et al. 1987; Angold et al. 1999; Boyd et al. 1984; Brown et al. 2001; Kashani et al. 1987; Kessler et al. 1994; 2004; 2005a; Low et al. 2008; Merikangas et al. 1998; Moffitt et al. 2007; Neale & Kendler 1995). # Cambridge University Press 2010

0140-525X/10 $40.00

However, although considerable progress towards furthering our understanding of comorbidity has been made, some pivotal questions remain unanswered. Probably the most crucial question is what we observe when two disorders covary: a genuine phenomenon that is independent of our diagnostic criteria, measurement scales, and measurement models, or (in part) an artifact of the structure of these criteria and models (e.g., see Borsboom 2002; Neale & Kendler 1995)? The former possibility holds that a genuine source of comorbidity rates exists. As such, the disorders themselves are comorbid, which causes the symptoms of such comorbid disorders to correlate. The latter possibility holds that comorbidity is produced by the way we empirically identify these disorders; for instance, because disorders often share a number of symptoms, which leads to an artificially increased comorbidity rate. Thus, in this view, comorbidity is largely an artifact of the diagnostic system. 137

Cramer et al.: Comorbidity: A network perspective In this article, we argue that these possibilities are not exhaustive. Specifically, we argue that comorbidity is not an artifact. However, we do contend that comorbidity, as it has been studied so far, is dependent on the way we psychometrically portray disorders and comorbidity between them: namely, with a latent variable model (e.g., factor models, item response models). Within this psychometric framework, comorbidity is generally conceptualized as a (bi)directional relationship between two latent variables (i.e., disorders) that underlie a set of symptoms. In our view, there are good reasons to doubt the validity of the psychometric assumptions that underlie this approach. We discuss these reasons and propose an alternative conceptualization of the relation between symptoms and disorders that offers a natural way of explaining comorbidity. The central idea is that disorders are networks that consist of symptoms and causal relations between them. In a nutshell, what binds, say, the set of depression symptoms, is that they are thus connected through a dense set of strong causal relations. With regard to comorbidity, such a network approach presents a radically different conceptualization of comorbidity, in terms of direct relations between the symptoms of multiple disorders. In contrast to existing perspectives, it is inappropriate to say that the symptoms measure the disorder in question. The reason is that the presence of direct causal relations

ANGE´ LIQUE CRAMER is a Ph.D. student in the Department of Psychology at the University of Amsterdam. She works on the application of network models in psychopathology. Her interests concern psychometric modeling, measurement invariance, complex systems, and psychopathy. LOURENS WALDORP received his master’s degree in methodological psychology in 1998 and his Ph.D. in 2004 from the University of Amsterdam. He was a postdoctoral fellow in 2004 at both the University of Amsterdam and the University of Maastricht and is currently Assistant Professor of Psychology at the University of Amsterdam. His main research interests include mathematical statistics, statistics in psychophysiological experiments, signal processing, graphical models, and neuroimaging data. HAN VAN DER MAAS is full Professor of Psychological Methods at the University of Amsterdam. He works on the mathematical, statistical and computational formalization of psychological theories in diverse fields of psychology. DENNY BORSBOOM is Associate Professor of Psychology at the University of Amsterdam. His work has focused on conceptual analyses of psychometric models; topics include the theoretical status of latent variables, the concept of validity, the definition of measurement in psychology, and the relation between different test theoretic models. He has published on these topics in journals such as Psychological Review, Psychological Methods, and Psychometrika, and has authored the monograph Measuring the Mind: Conceptual Issues in Contemporary Psychometrics (Cambridge University Press, 2005).

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between symptoms contradicts the essential assumptions that underlie psychology’s main class of measurement models (latent variable models; e.g., Borsboom 2005; 2008; Borsboom et al. 2003). In fact, a network approach nullifies the need to invoke latent variables as an explanation of the covariance between symptoms. In a network approach, the relation between symptoms and disorders (or, more generally, test scores and constructs) should not be viewed as one of measurement, but as one of mereology: The symptoms do not measure the disorder, but are part of it (see also Markus [2008] for a discussion of the role of mereology and causality in statistical modeling). This is consistent with McGrath’s (2005) observation that theoretical terms in psychology, such as “depression” may often refer to complex constellations of variables, rather than to a single latent structure. Hence, it is likely that comorbidity’s true colors are obscured by methodological problems that spring from the assumptions underlying such techniques. The specifics of those problems vary, but all bear one striking resemblance: they are at least in part attributable to the notion that one can focus on diagnoses in current comorbidity research, because diagnoses serve as reliable proxies for the latent variables that supposedly underlie them. In this article, we provide an in-depth discussion of these problems and show that the network approach avoids them. The structure of this article is as follows. First, we introduce the network approach by contrasting it to the latent variable model. We subsequently propose an integrative way to visualize comorbidity as a symptom network, and discuss the basic features of an empirical network for major depressive disorder (MDD) and generalized anxiety disorder (GAD), based on data from the National Comorbidity Survey Replication1 (NCS-R) (Kessler et al. 2004; 2005a; 2005b). Then, we discuss three additional methodological problems that characterize current comorbidity research and argue that adopting a network approach may help in answering questions that are, in our view, crucial when painting an accurate picture of comorbidity: How important are symptoms that overlap between two disorders as sources of comorbidity? Can we identify symptoms of a disorder that put someone at more risk of developing a second disorder compared to other symptoms? Is there an order in which people generally develop one particular disorder first and another disorder second? 2. Mental disorders: Networks of directly related symptoms instead of latent variables Measurement models used in clinical and personality research have one thing in common: the assumption that there is some attribute we cannot observe directly (i.e., is “latent”) – MDD or extraversion, for instance – and therefore, must be measured indirectly through the presence or absence of certain observable variables (e.g., MDD is measured by depressed mood and extraversion is measured by party-going behavior; McCrae & Costa 2008; see Michell [2005] for a detailed explanation of measurement in science). In doing so, latent variable models are consistent with the hypothesis that the latent attribute has causal relevance for the observed values of symptoms (e.g., see Borsboom 2008; Borsboom et al. 2003; 2004; Hood 2008): In this view, for instance,

Cramer et al.: Comorbidity: A network perspective depression (i.e., the latent attribute) causes the occurrence of symptoms such as fatigue. In line with this idea, it is commonly hypothesized that comorbidity arises due to some direct relation between two latent variables; for example, a substantial correlation as depicted in Figure 1 (e.g., MDD and GAD; Neale & Kendler 1995). Some theorize even further, and hypothesize that a direct relation between two latent variables actually reflects the existence of a “super disorder” – for example, in models in which the super disorder “negative affect” causes a variety of mental disorders (e.g., depression) which, in turn, cause observable symptoms (e.g., see Barlow et al. 2004). In accordance with both views on comorbidity, current comorbidity research mainly focuses on diagnoses as proxies of the latent disorders and computes tetrachoric correlations or odds ratios between those proxies. Although this methodology has yielded important insights (e.g., Brown et al. 2001; Kessler et al. 1994; 2005b; Merikangas et al. 1998; Moffitt et al. 2007), the latent variable model may not always offer the best psychometric perspective to conceptualize mental disorders (see also Borsboom 2008). To see this, it is useful to consider the essence of latent variable modeling, the common cause hypothesis, in more detail. The common cause hypothesis posits that a latent variable causes its observable indicators. If one adopts this hypothesis for a particular set of variables, then one has to accept an important consequence: The observable indicators cannot be directly related; that is, if a single common cause is held responsible for the occurrence of a particular set of variables, then covariation between those variables is entirely attributable to the common cause. It is important to note here that we are referring to the psychometric as opposed to a clinical interpretation of a latent variable model. In the clinical interpretation, clinicians adhere to the existence of a latent variable while at the same time acknowledging direct relations between symptoms. In a strict psychometric sense, a latent variable model does not allow for many direct relations since the majority of covariance between symptoms needs to be explained by the common cause. As such, psychometric latent variable models imply that correlations between observable indicators are, in a nontrivial sense, spurious. When statistically modeling the

relationship between a hypothesized latent variable and a set of indicators, the fact that the indicators cannot be directly related results in the statistical assumption of local independence (such assumptions are made, for instance, in the models used in Aggen et al. [2005], Hartman et al. [2001], and Krueger [1999]): when fitting a latent variable model to observed data, any two indicators are conditionally independent given the latent variable (Lord & Novick 1968). As such, local independence is a statistical consequence of adopting the hypothesis that a common cause structure gave rise to the associations in the data. In our view, a common cause structure is unlikely to hold for symptoms of mental disorders. For instance, consider “sleep disturbances” and “fatigue,” both of which are DSM-IV symptoms of MDD (see Diagnostic and Statistical Manual of Mental Disorders, 4th edition; American Psychiatric Association 1994). If one adopts the common cause hypothesis, a high positive correlation between these symptoms is entirely due to the common influence of the latent variable, MDD. It is questionable whether this is plausible. For instance, a direct causal relationship between those symptoms is likely to hold in at least a subset of people who experience them: If you don’t sleep, you get tired. Another example: Is it plausible to assume that GAD necessarily causes both chronic worry and a difficulty to concentrate? It may well be that a direct causal relationship exists between these symptoms: the more you worry, the more difficult it is to concentrate at other things. Thus, it appears likely that latent variable models do not optimally conceptualize the relationship between mental disorders and their symptoms. This is not to say we object to the notion that symptoms of various disorders tend to cluster together in predictable ways and that, as such, disorders may be pragmatically useful to denote such clusters (e.g., see Hartman et al. 2001). However, we do suggest that mental disorders may not explain covariation between symptoms in the way a latent variable model pictures the situation. If this is so, then even though the application of latent variable modeling may have considerable instrumental utility (e.g., in facilitating predictions or gauging rough differences between people), one cannot plausibly say that the symptoms

Figure 1. A model of comorbidity between disorders A and B, under the standard assumptions of latent variable modeling. The circles represent the disorders (i.e., latent variables) and the rectangles represent the observable core symptoms of those disorders (i.e., X1 2 X5 for disorder A, and Y1 2 Y5 for disorder B). In this model, comorbidity is viewed as a correlation between the latent variables, visualized by the thick bidirectional edge between disorders A and B. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Cramer et al.: Comorbidity: A network perspective actually measure a latent variable. Therefore, we consider it important to examine relationships between individual symptoms more closely. Initiating such an endeavor is a major goal of this article. As a starting point, we propose to use the theory of complex networks. This theory has provided major contributions to current knowledge about the structure of the World Wide Web, power grids, and neural systems (e.g., see Albert & Baraba´si 1999; 2002; Boccaletti et al. 2006; Strogatz 2001; Wang 2002). The basic idea of the network approach is straightforward: We define and analyze relationships between symptoms, without assuming a priori that such relationships arise from a mental disorder as a common cause (Borsboom 2008; Van der Maas et al. 2006). Simply put, in such a network, a disorder is conceptualized as a cluster of directly related symptoms. In a fairly recent study, Kim and Ahn (2002) showed that this conceptualization comes naturally to some clinicians: depression, anorexia nervosa, antisocial personality disorder, and specific phobia were all characterized as clusters of causally related symptoms. And, adhering to such a network perspective cannot be reconciled with the psychometric properties of a latent variable model. Thus, when modeling comorbidity, we no longer assume a direct relation between two latent variables. Instead, we model comorbidity in terms of a set of direct relationships between symptoms of distinct disorders. A network model represents symptoms as nodes in a graph and the relationships between them as edges. Figure 2 depicts an example of such a graph for two disorders: two sets of symptoms belong to two distinct mental disorders. Within each disorder, all symptoms are connected with one another, but between disorders, there are fewer (or weaker) edges between the symptoms. There are also symptoms that do not clearly belong to one or the other disorder, because they receive and send out effects to the symptoms in both of the disorders (i.e., overlapping symptoms). If such symptoms overlap perfectly, they can be collapsed into a single symptom, which we propose to call a bridge symptom. We hypothesize that in clinical practice, such bridge symptoms turn up as symptoms that are used in diagnostic schemes, such as the DSM-IV, for multiple disorders.

Figure 2. Comorbidity under a network approach. Disorder A consists of bidirectionally related symptoms X1 2 X5, and disorder B consists of symptoms Y1 2 Y5. Symptoms B1 and B2 are bridge symptoms that overlap between disorders A and B. In this model, comorbidity arises as a result of direct relations between the bridge symptoms of two disorders.

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Our hypothesis regarding the crucial role of bridge symptoms in explaining comorbidity can be tested, just as a host of hypotheses can be tested with latent variable models. For binary data, a statistical parameterization of the network is a loglinear model, which is implemented in the gRbase package for R (Dethlefsen & Hojsgaard 2005). In short, with a loglinear model, one searches for the most parsimonious model – among models ranging from only main effects through models with nth-order interactions – that accounts for the distribution of cases in contingency tables of categorical variables (e.g., see Agresti 2002). If the main effects model should turn out to be the best model, then the MDD and GAD symptoms are statistically independent, and our hypothesized bridge model should be rejected accordingly. Thus, in gRbase, we fitted a model like the one shown in Figure 2 to the NCS-R MDD and GAD data: All symptoms of MDD/GAD, including the bridge symptoms, are connected with one another, and comorbidity arises only through connections between overlapping symptoms, on the one hand, and other symptoms of MDD/GAD, on the other hand.2 We used the Akaike Information Criterion (AIC) to compare the fit of three models: (1) with only main effects, (2) with first-order interactions within disorders (including bridge symptoms, as in Fig. 2), and (3) with secondorder interactions within disorders (including bridge symptoms). Of these three models, the best-fitting model according to the AIC is the one with first-order interactions (AIC differences are: (2) – (1) ¼ -177.551 and (3) – (2) ¼ 347.123). Thus, according to this analysis, the bridge model holds with all variables being statistically dependent on one another. Naturally, such a single fit is not sufficient to conclude that this model is the best choice, especially since – considering parsimony – such a low chi-square value with so many degrees of freedom cannot be interpreted in a straightforward manner. Nonetheless, this model fit shows that our hypothesis about the importance of bridge symptoms in explaining comorbidity is not a priori wrong. The network approach is based on the hypothesis that symptoms are related directly. It is important to qualify this terminology to prevent misunderstandings. We intend the term “directly” to mean that the relation between symptoms is real; that is, not spurious in the sense that a latent variable model assumes it to be. This does not imply, however, there may be no intermediate processes or attributes involved. For instance, the influence of one symptom on another is likely to be mediated by, or instantiated in, a chain of processes that are not directly observable. Even the influence of the symptom “sleep disturbances” on “fatigue,” mundane as it may seem, will invoke various intermediate mechanisms concerning the homeostatic processes involved in sleep regulation (Achermann 2004; Borbe´ly & Achermann 1999; Finelli et al. 2000). Thus, within a network framework, it makes perfect sense – and is naturally necessary – to introduce non-symptom causal processes such as homeostasis that partly explain relations between symptoms. Also, such processes may involve pathways that contain some of the other symptoms in the network; for instance, a lack of sleep may lead to a loss of concentration via fatigue. Finally, the causal effect of a symptom may feed back into that same symptom via a loop. For instance, fatigue may lead to a lack of concentration, which may

Cramer et al.: Comorbidity: A network perspective lead to thoughts of inferiority and worry, which may in turn lead to sleepless nights, thereby reinforcing fatigue. In such a case, we have a vicious circle, or negative spiral, a well-known phenomenon to any practicing clinical psychologist. In some disorders, the existence of feedback loops is in fact considered to be a core aspect of the disorder; an example is panic disorder, in which “fear of fear” appears to play a crucial role; for instance, when the fear of having a panic attack itself contributes to the occurrence of such an attack (McNally 1994). It is therefore notable, and problematic, that in standard psychological measurement models, such phenomena cannot arise because latent variable models, being instantiations of a common cause structure, are directed graphs which, by definition, do not contain feedback relations3 (Pearl 2000). Moreover, targeting such relationships between symptoms or processes that influence such relationships is a major goal of many successful therapeutic interventions such as cognitive therapy (e.g., lessen the impact of cognitions on relationships between symptoms: “If I do not finish all tasks I set out to do during the day, I am a worthless person and it is better for everyone if I were gone”; see Beck et al. 1979) and exposure therapy (i.e., breaking the link between seeing a particular object and responding to it with fear by repeatedly exposing a patient to the feared object; see, e.g., Kamphuis & Telch 2000; Rothbaum & Schwartz 2002). It is therefore also problematic that such successful and common therapeutic interventions do not naturally arise from a latent variable perspective. This is not to say that targeting relations between symptoms is prohibited by a latent variable perspective; the more logical consequence of adopting such a perspective just seems to be to target the latent variable: eliminating the common cause will result in the disappearance of its indicators (i.e., the symptoms). In the case of major depression, for example, finding the common cause was therefore a major goal in research, with serotonin shortage being the most likely candidate. However, treatment with antidepressants that specifically target that shortage turned out to be beneficial for only some people, thereby ruling out serotonin as the common cause of depression symptoms (e.g., see Nierenberg et al. 2008). No other plausible common causes have ever been found, in our opinion due to the fact that there simply is no common cause that explains the entirety of depression symptoms.

3. An integrative method to visualize symptom associations through graphical models Many of the efforts in complex systems theory have been aimed at providing adequate visual representations of networks, and this has yielded a number of algorithms to optimally represent networks (De Berg et al. 2008; DiBattista et al. 1994; Herman 2000), as well as freely available software to visualize them; most notable, in this respect, are the programs Cytoscape (Shannon et al. 2003 – used in constructing the graphs for this article), aiSee (http:// www.aisee.com), and igraph (Csa´rdi & Nepusz 2006 – used in this article for the detection of community structures). We therefore propose that the study of comorbidity through network models may best start by constructing insightful visualizations.

Among a plethora of possibilities to define and visualize both nodes and edges (see, e.g., Boccaletti et al. 2006; Krichel & Bakkalbasi 2006), we propose an integrative method that, in our view, optimally visualizes key aspects of comorbidity on a symptom level. Figure 3 provides the complete key to such a comorbidity network for MDD and GAD, which is presented in Figure 4.4 First, the thickness of the edges is determined by the co-occurrence of two symptoms: the more two symptoms co-occur, the thicker the edge between them. Second, the color of the edges is determined by the log odds ratio between two symptoms5 (i.e., strength of the association; results available at: http://www.aojcramer.com): the higher the log odds ratio, the darker blue the edge between symptoms. (Note that other options exist to define some measure of the strength of the association between two symptoms: for instance, tetrachoric correlations.6) Third, the size of the nodes is determined by the raw frequency: the more frequent a symptom, the larger the node. Finally, the color of the nodes is determined by their individual node strength (see, e.g., Boccaletti et al. 2006; Krichel & Bakkalbasi 2006). The node strength is simply the sum of the weights of all edges that are incident in that node. In the complex networks literature, the node strength is taken to be a measure of the centrality of a node such that the more strength, the more central a node is in the network. In addition, we propose the following two rules for the positioning of the nodes in a comorbidity network (see also Fig. 4): First, we propose that from left to right (i.e., the x-axis), non-overlapping symptoms of two disorders are placed on the extreme left and right while the overlapping symptoms are placed in the middle of the graph (see our Note 2). As such, one can immediately see whether comorbidity between two disorders runs mostly through the overlapping symptoms or (also) exists independently from them. Second, we propose that from top to bottom (i.e., the y-axis) the nodes are placed based on descending node strength. As such, one can immediately see which symptoms are more central in the network (i.e., top of the graph). 4. The basic structure of the depression and generalized anxiety comorbidity network A few characteristics of the MDD and GAD comorbidity network stand out in particular (see Fig. 47). First, GAD symptoms are more frequent than MDD symptoms (i.e., GAD nodes are generally larger than MDD nodes). At first sight, this may appear at odds with the higher prevalence of MDD compared to GAD that is usually reported (Carter et al. 2001; Kessler et al. 2005b). However, on a diagnosis level, only respondents who display a certain number of MDD or GAD symptoms with a certain duration qualify for a diagnosis. Additionally, because of a hierarchical exclusion rule, the GAD diagnosis will not be assigned if its symptoms occur exclusively within the course of MDD (Brown & Barlow 1992; Brown et al. 2001; Clark et al. 1995; Mineka et al. 1998; Watson 2005). Since MDD and GAD are highly comorbid (see, e.g., Brown et al. 2001; 1998; Mineka et al. 1998), such exclusion rules lower the prevalence of GAD artificially. Here, we consider data of all respondents who completed BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Figure 3. The key for the comorbidity networks shown in Figures 4, 5, and 6.

Figure 4. A comorbidity network for major depressive disorder (MDD) and general anxiety disorder (GAD). Larger nodes represent more frequent symptoms, darker circumference represents higher centrality, thicker edges represent higher frequency of cooccurrence, and darker edges represent stronger associations. Only edges with a log odds ratio higher than (þ or -)0.60 are represented. Centrally positioned nodes (mConc, gConc, mSleep, gSleep, mFatig, gFatig, mRest, and gRest) represent overlapping symptoms. Non-overlapping MDD symptoms are displayed on the left of the figure, and non-overlapping GAD symptoms on the right.

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Cramer et al.: Comorbidity: A network perspective the MDD and GAD interview sections, regardless of whether or not they obtained diagnoses. As such, the network demonstrates that, when considering both subthreshold and threshold depression and generalized anxiety, symptoms of generalized anxiety are in fact more prevalent. Second, if MDD and GAD are separate entities, we would have expected the edges to be thickest between symptoms of the same disorder (i.e., high co-occurrence). However, it is apparent that this is not the case in the network: Some of the thickest edges connect MDD with GAD symptoms; for instance, the thick edge between loss of interest (mInt) and reporting more than one event one worries about (gEvent). Also, we would have expected edges to be the darkest blue between symptoms of the same disorder (i.e., high log odds ratios), but that is also not evident when inspecting the figure. In other words, associations between symptoms of one disorder are not stronger than associations between symptoms of different disorders. These findings are in line with an earlier hypothesis that MDD and GAD are hard to distinguish on a genetic level (Mineka et al. 1998) and, as such, raise the question of whether MDD and/or GAD are truly distinct disorders. We will return to this matter in more detail in the paragraph about the non-uniformity of diagnostic criteria. Third, duration (mDur and gDur) is hardly associated with any of the other MDD and GAD symptoms8 (i.e., few edges are incident in those nodes). This may appear surprising since, in clinical practice, duration is key in determining the presence or absence of a mental disorder. However, if we consider medical illnesses as an analogy, the finding is potentially less surprising: Cancer will be diagnosed if a malignant tumor is present, and that diagnosis is independent of how long the tumor has been present. Thus, we could argue that, in a network approach, MDD is present whenever some symptoms are present without considering the duration of those symptoms. This is not to say that duration is not an important factor at all. Consider again medical illnesses where duration is important in determining the course of action and, subsequently, the probability of full recovery: The longer a malignant tumor has had time to grow and possibly spread, the more difficult it will be to treat it. Duration could fulfill the same role in determining the best course of action for treating mental disorders. Finally, the strongest evidence for comorbidity stems from strong associations that involve at least one overlapping symptom (e.g., between depressed mood, mDep, and sleep disturbances, gSleep). This apparent nontrivial role of overlapping symptoms in comorbidity stands in stark contrast to earlier findings regarding MDD, GAD, and other mental disorders (e.g., see Biederman et al. 1995; Bleich et al. 1997; Clark & Watson 1991; Franklin & Zimmerman 2001; Kessler et al. 1999; Seligman & Ollendick 1998; Watson et al. 1995). We will return to this issue in more detail in the paragraph about overlapping symptoms. It is crucial to note that the network is not necessarily complete. That is, this comorbidity network is based on the symptoms of major depression and generalized anxiety, but, naturally, it stands to reason to hypothesize the presence of factors – other nodes – that selectively influence some of the symptoms and are thus part of the

network. For instance, it is well known that major life events, such as the loss of a loved one, can trigger major depression and, more specifically, there is evidence for selective influence of such personal tragedies on the more psychological symptoms of depression (e.g., depressed mood, thoughts of suicide) (David et al. 2008; Kessler 1997; Monroe et al. 2001). Also, there is evidence that traits such as neuroticism (mediated by rumination on sadness) and behavioral inhibition (i.e., shy, fearful, and withdrawn) can trigger the onset of depression and/or anxiety symptoms (e.g., see Hirshfeld et al. 1992; McNiel & Fleeson 2006; Roelofs et al. 2008a; 2008b). Because such and other more “etiological nodes” are missing from this network, they are in a sense latent. However, such latent etiological nodes do not turn the MDD and GAD comorbidity network into a latent variable model: A network with multiple latent nodes that selectively influence some of the symptom nodes is not the same as a latent variable model in which one latent factor influences all symptoms and thus entirely explains relations between symptom nodes. Moreover, an unobserved variable is indeed latent, but not every unobserved variable automatically qualifies as a latent variable in the psychometric sense in which such variables are portrayed in latent variable models commonly used in data analysis. 5. The inequality of symptoms and its consequences for diagnostic cut-offs and the definition of a mental disorder The focus in comorbidity research is on diagnoses, which means that inferences regarding comorbidity rest on summed scores that are obtained by counting symptoms. In latent variable modeling, such an unweighted summed score is either a sufficient statistic for the latent variable (e.g., see Andersen 1973; Masters & Wright 1984) or has a monotone likelihood ratio with that latent variable (Grayson 1988). In both of these cases, inferences based on the summed symptom scores will often generalize to the latent variable. The unweighted summation of symptom scores implies that all symptoms are considered equal. Although thus formally consistent with latent variable modeling (Grayson 1988), this assumption is highly problematic and may be the origin of some significant problems in comorbidity research. In a network approach, symptoms are likely to be actually unequal in terms of their centrality, a property that is not reflected in any latent variable model, and this has consequences for the comparability of equal summed scores. Suppose that Alice displays two MDD symptoms – depressed mood and loss of interest – while Bob displays two other MDD symptoms – psychomotor and weight problems. On an intuitive level, it is plausible that Alice’s symptoms are more likely than Bob’s to eventually result in a full-fledged depression. In other words, some symptoms appear to be more central features of depression than others. The comorbidity network sustains this intuition. When considering the node strengths in Figure 4 (i.e., colors of the nodes), one immediately sees that, indeed, depressed mood (mDep) and loss of interest (mInt) are far more central in the network than are psychomotor (mRest) and weight problems (mWeight). In other words, the same summed score of Alice and Bob BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Cramer et al.: Comorbidity: A network perspective may not adequately capture that the symptoms of Alice result in a higher probability of developing other MDD symptoms – and thus augment the probability of eventually developing depression – compared to Bob’s symptoms. Hence, summed scores appear to be incomparable, at least with respect to elucidating which people with subthreshold depression problems are at more risk of developing MDD. Naturally, such symptom inequalities are widely recognized among psychiatrists and clinical psychologists, and they do occasionally appear in DSM-IV (e.g., depressed mood and loss of interest as central features of major depression); the problem is, however, that the models that underlie current comorbidity research do not naturally allow for them. If our line of reasoning is correct, and there is no latent variable that screens off correlations between symptoms (a latent variable model renders all symptoms equally central and thus exchangeable 9), then the inequality of symptoms in terms of their centrality also renders diagnostic cut-offs open to debate. We are certainly not the first ones to point out that diagnostic cut-offs appear to be arbitrary (e.g., see Gotlib et al. 1995; Lilienfeld & Marino 1999; Maier et al. 1997; Solomon et al. 2001). For instance, there are individuals who do not meet diagnostic criteria for MDD yet appear to be psychosocially as dysfunctional as individuals who are diagnosed with MDD; that is, the consequences of subthreshold MDD problems may not always be distinguishable from those of diagnosed MDD. With the network approach, we offer a potential explanation of such findings. Suppose that Alice displays four MDD symptoms and Bob five. The diagnostic cut-off of criterion B for MDD is five, so Alice would not be diagnosed with MDD while Bob would. So far so good, but now suppose that Alice’s symptoms are all highly central in the MDD network while Bob’s are more peripheral. Is it, in such a scenario, plausible to conclude that Alice is not depressed and Bob is? In other words, based on diagnostic cut-offs, we may fail to disentangle symptomspecific effects, because such cut-offs do not take into account the centrality of symptoms. This brings us to another important point: namely, the definition of a mental disorder, generally conceptualized as “Disorder A is X or more symptoms out of Y possible symptoms.” According to a latent variable perspective, it is not only perfectly defensible to entertain such a definition, but the definition is the same for every single individual; that is why Alice is not depressed and Bob is. However, if symptoms are not exchangeable in terms of their centrality, as we think is plausible, one cannot help but question such a definition of a mental disorder. In other words, if diagnostic cut-offs alone are no longer the demarcation line above which someone suffers from a particular mental disorder, then how do we define a mental disorder? From a network perspective, there are several possibilities to define what constitutes a mental disorder. As a starting point, we propose to define a disorder as a cluster, a set of nodes (symptoms) that are strongly connected. Now, from a graph theoretic perspective, there are multiple ways to define in what sense a set of nodes is strongly connected (see, e.g., Hubert 1974). First, let us call the giant network consisting of all symptoms of all mental disorders (i.e., the entire symptom space) as defined in the DSM-IV, graph G. Then a subgraph H (for instance, consisting of all 144

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MDD symptoms) is a cluster of G if and only if the minimum node strength of H is larger than the minimum node strength of H þ fng, with n any other node adjacent to H (Definition 1). It is also possible to define a subgraph H as a cluster of G if and only if the minimum of the average distance between all nodes in H is strictly smaller than that of H þ fng for any node n in G (i.e., closeness; see, e.g., Boccaletti et al. 2006) (Definition 2). Other definitions are possible, and it is – in our opinion – up to future debate and research to determine which is the most sensible one. Second, now that we have hypothetically defined the cluster of all possible symptoms of a disorder, we need to determine when such a cluster is disordered. One plausible candidate is a modified version of the diagnostic cut-off; for example, in the case of MDD, at least three of the most central symptoms in the entire MDD cluster (with “central” either defined as the nodes with the largest node strengths, or as the smallest average distance within the cluster). In contrast to a latent variable perspective, both definitions acknowledge the centrality differences of symptoms but, at the same time, accept the inevitable fact that some form of a diagnostic cut-off is needed to disentangle people with and without a disorder. A related point concerns the external effects of different symptoms. One readily imagines extending a network with variables that are not part of the disorder itself, but constitute nontrivial consequences of many mental disorders (e.g., losing one’s job, lowered educational achievement, or suicide attempts). It is interesting to note that, under the assumption of a latent variable model, it is the latent variable that has a direct relationship with external effects, and not the symptoms. Due to the absence of a direct relationship between a symptom and an external effect, this means that a symptom can never be statistically independent of such an external effect, given another symptom. Thus, for instance, a suicide attempt by someone with thoughts of suicide and concentration problems (and three other symptoms resulting in a diagnosis of major depression) is entirely attributable to the overarching latent depression and, given the thoughts of suicide, the concentration problems are thus still associated with the suicide attempt. In our view, it would be more logical to hypothesize a direct relationship between thoughts of suicide and a suicide attempt and a weaker or perhaps even nonexistent relationship between concentration problems and a suicide attempt. In the same vein, it appears to make sense to envision a stronger relationship between concentration problems and losing one’s job than between losing weight and losing one’s job. This differential impact of symptoms on external effects is not possible in a latent variable model, whereas it is very easily envisioned within a network perspective. Centrality differences between symptoms imply that there probably will be pathways to comorbidity that are more likely (i.e., strong connections between symptoms that are central in a network) than others. Figure 4 confirms this idea: One likely pathway to comorbidity connects depressed mood (mDep) with sleep problems (gSleep) and anxiety (gAnx). Less likely pathways involve psychomotor problems (mRest) because this symptom has such weak associations with the other symptoms in the network. Naturally, inspecting a graph is not enough to draw any solid conclusions on the pathways to

Cramer et al.: Comorbidity: A network perspective comorbidity between MDD and GAD, but we do think it is evident that the network approach could contribute to finding answers to this question, if only because the visual representation of a network immediately leads to a host of interesting hypotheses. 6. Non-uniformity of mental disorders Quite a few scholars are essentialists in describing the relationship between the two main diagnostic categories “disorder” and “no disorder” that are based on diagnostic criteria and the real world (e.g., see Haslam 2000; Haslam & Ernst 2002; Lilienfeld & Marino 1999): The diagnostic criteria we use result in a distinction between disordered and non-disordered people that also exists in the real world. Seductive as this line of reasoning may seem, in order for it to be true, two conditions must be satisfied. First, a mental disorder must have defining features such that everyone, based on those defining features, could be assigned to the “disorder” category (i.e., defining features are present) or the “no disorder” category (i.e., defining features are absent) provided that these features were known with certainty. Second, as a result, all members of the same category must essentially be the same with respect to those defining features (i.e., uniformity). Down’s syndrome is a good example of a medical disorder that satisfies those two conditions: The syndrome has one defining feature, the presence of all or part of an extra 21st chromosome, and everyone with Down’s syndrome possesses that defining feature while everyone without Down’s syndrome does not possess it. This line of reasoning is unlikely to hold for mental disorders. First, quite a few mental disorders do not have defining features, at least not in an essentialist sense. For example, besides depressed mood or loss of interest, which must always be present for a person to be diagnosed as having MDD, any constellation of five symptoms (i.e., features) will suffice to fulfill criterion B for MDD. When any such constellation of symptoms is present for at least two weeks in an individual, then that individual will be assigned to the “MDD” category, otherwise to the “no MDD” category. This renders the core features of depression non-defining because, for instance, someone with the feature “depressed mood” could end up in the “MDD” category – because he or she suffers from five or more symptoms for more than two weeks – as well as the “no MDD” category if he or she suffers from less than five symptoms or the symptoms are present for less than two weeks. Second, as a result of the lack of truly defining features, the “basket” with depressed people does not contain uniform members: Pete is depressed because he suffers from sleep disturbances, fatigue, concentration problems, depressed mood, and psychomotor problems, while Anne is depressed because she suffers from depressed mood, loss of interest, self-reproach, weight problems, and thoughts of suicide. As such, one must wonder whether the distinction between “disorder” and “no disorder,” as we have defined it in our diagnostic criteria, actually exists in the real world. Latent variable modeling schemes posit the existence of such a categorical system (in a latent class model) or a continuous one (in a factor or item response theory [IRT] model) as a hypothesis. Hence, such

models are consistent with the hypothesis that we may one day find out “what depression really is”; that is, latent variables may “become” observed through a refinement of the conceptual and measurement apparatus used to study them (e.g., Bollen 2002; Borsboom 2008). However, in the absence of such refinements, the acceptance of the latent variable hypothesis depends at least partly on its explanatory virtues (Haig 2005), and in the context of comorbidity research these explanatory virtues are, at present, quite limited. That is, apart from the fact that such a model would explain why correlations between symptoms are positive and that it more or less fits the observed frequency of symptom patterns, there is little that speaks in its favor. When studying comorbidity based on diagnoses, this inevitably leads to the question of what we actually observe when two disorders covary: genuine covariation between two real disorders, or covariation between certain constellations of symptoms we have designated to be disorders, but that are in fact not indicators of the same latent variable? This issue, of course, has generated a heated debate through the history of psychiatry and clinical psychology (Haslam 2000; Haslam & Ernst 2002; Jablensky 2007; Kendell 1975; Klein 1978; Krueger & Markon 2006b; Lilienfeld & Marino 1999; Richters & Hinshaw 1999; Spitzer 1973; 1999; Spitzer & Endicott 1978; Wakefield 1992; 1999a; 1999b; Zachar 2000; Zachar & Kendler 2007). The network approach could contribute to finding an answer to this question in two ways: first, by utilizing techniques to find what is called a community structure, and second, by reconceptualizing the question itself, and thereby the range of possible answers. The community structure of a network refers to the existence of at least two clusters of nodes, such that the nodes within a cluster are highly connected with one another, but only modestly or sparsely with the nodes within another cluster (see Newman 2006; Newman & Girvan 2004). We analyzed the community structure of the MDD and GAD comorbidity network twice with a spinglass algorithm (for technical details, see Reichardt & Bornholdt 2006): one time with co-occurrence between symptoms as edge weights and one time with the log odds ratios between symptoms as edge weights. The results are in line with the notion that there is no essential distinction between MDD and GAD, as has also been found in behavioral genetics and diagnostics research (Mineka et al. 1998; Wadsworth et al. 2001): Our network reveals no community structure whatsoever, regardless of which edge weights were used; that is, the comorbidity network did not differ from a random network in terms of connectivity between nodes. These results suggest that MDD and GAD may not be separate entities. Naturally, this conclusion may be different for other mental disorders. We are by no means pioneers when claiming that boundaries between diagnostic categories are fuzzy, for this phenomenon was noticed quite some time ago (e.g., see Kendell 1975; Klein 1978; Spitzer 1973; Spitzer & Endicott 1978). However, earlier ponderings have not included an account of why the boundaries are fuzzy and, in our view, a network approach offers such an explanation. If we are indeed correct to assume that a mental disorder is best conceptualized as a network of symptoms and – consequently – comorbidity is best viewed as a network of symptoms of two disorders, then boundaries BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Cramer et al.: Comorbidity: A network perspective are fuzzy because they simply do not exist. And the reason that they do not exist lies in the fact that the networks are not isolated from each other. The very fact that there are bridge symptoms precludes such a situation from occurring. As a result, we can draw the line between disorders A and B everywhere in the network. For instance, we could draw a boundary between MDD and GAD such that MDD contains only non-overlapping MDD symptoms while GAD contains its own symptoms and the overlapping MDD symptoms. Or, we could draw a boundary such that MDD only contains non-overlapping MDD symptoms and GAD only its non-overlapping symptoms. In other words, from a network perspective, the DSMIV – defined boundary between MDD and GAD is no more defensible than any other boundary. The network perspective offers an intermediate position between essentialism and conventionalism regarding mental disorders and the comorbidity that exists between them. On the one hand, there is a sense in which the delineations of mental disorders are arbitrary (there is no preferred line that separates the relevant networks). One the other hand, since realizations of common causes for symptom clusters cannot be detected, the actual phenomenon of comorbidity is not a matter of convention, since it depends on causal patterns that exist in the real world, independent of the researcher who studies them. Although mental disorders can be defined as a network in various ways, which may reflect mainly pragmatic concerns, comorbidity will remain regardless of how one draws the lines. In this sense, comorbidity may be more real than the mental disorders on which it is defined. This is consistent with, and may actually offer an explanation of, results typically found in quantitative behavior genetics. Through twin studies and related methodologies, it has been established that a considerable portion of the individual differences in anxiety and depression, as well as many other psychological variables, is determined by genetic factors (Boomsma et al. 2002; Kendler et al. 2001; McGue & Christensen 2003). Much research has focused on determining the genes responsible for this fact, but so far these efforts have been moderately successful at best, with the typical result being that individual polymorphisms do not account for more than a minor portion of the phenotypic variance (e.g., 1% or 2% at best). Thus, such phenotypes are highly polygenetic. The network account explains this naturally: It is likely that the strength of connections between symptoms (e.g., the relation between lack of sleep and irritability) differs over individuals, and it is also likely that these individual differences are at least partly under genetic control. However, a network of k nodes consists of k 2-k relations between distinct nodes (380 possible relations for the network in Fig. 4), and it is rather unlikely that the strength of each of these relations stands under control of the same genes. Thus, the network approach is not only consistent with the fact that most psychological phenotypes are polygenic, but may actually offer an explanation of that fact. In addition, the approach suggests that gene-hunting efforts may be better served by relating polymorphisms to the relations between symptoms, rather than to composites of symptoms such as total scores on questionnaires. The possibility of individual differences in a network structure raises the question of whether a uniform definition of comorbidity exists. For example, is there a 146

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particular sequence in which two comorbid disorders arise that holds for every single individual? At first sight, this appears to be unlikely. However, even though there may be individual differences in qualitative structure and quantitative characteristics of networks, statistical considerations regarding the average strength of connections may suggest pathways that are more or less prevalent in the population. For instance, in contrast to Moffitt et al. (2007), who found that MDD and GAD were equally likely to be the first in the comorbidity sequence, the MDD and GAD comorbidity network (see Fig. 4) does suggest the existence of a general pathway: namely, from MDD to GAD. First, because the non-overlapping MDD symptoms are not highly associated with one another, it does not appear to be very likely that someone with a few non-overlapping MDD symptoms will progress to other non-overlapping MDD symptoms. Second, a pathway from nonoverlapping to overlapping MDD symptoms to GAD symptoms could be more likely because of stronger associations between those types of symptoms. The converse scenario – that is, from GAD to MDD – appears to be less likely in this particular network. In general, associations between non-overlapping GAD symptoms are relatively strong, at least stronger than between the symptoms of MDD, and, most importantly, more or less as strong as associations between non-overlapping and overlapping GAD symptoms. As such, when in the GAD network, to progress quickly from a few non-overlapping GAD symptoms to overlapping GAD symptoms and from there to MDD symptoms, does not appear to be more likely. Instead, it appears to be equally likely that someone stays in the GAD network without progressing to MDD symptoms. Given the structure of this particular MDD – GAD network, we therefore hypothesize that Neale and Kendler (1995) are correct in concluding that the most likely pathway could indeed be from MDD to GAD. Naturally, further research involving the time course and etiology of mental disorders is required to test this hypothesis. It should be noted, however, that the hypothesis follows naturally from a (tentative) causal interpretation of the network: the stronger the association between symptoms, the more likely that one symptom will lead to another. Furthermore, a causal explanation of a network suggests that some symptoms within a disorder put one at greater risk for comorbidity than do others. To the contrary, one does not get these implications from either unidimensional or two-dimensional latent variable models that assume exchangeable symptoms, save for measurement precision (see Bollen [1989] for a good explication of this point). Thus, studying the etiology of symptoms may offer interesting insights with respect to the question of whether symptom development is best conceptualized in terms of a latent variable model, or in terms of a network perspective. We therefore consider the direction of research efforts toward the study of temporal dynamics of symptoms to be essential. 7. Symptom overlap between disorders A final problem with current comorbidity research has to do with the fact that many disorders share a number of symptoms: sleep disturbances, fatigue, restlessness, and

Cramer et al.: Comorbidity: A network perspective concentration problems in the case of MDD and GAD (American Psychiatric Association 1994). The obvious problem of such symptom overlap is that it raises doubt as to whether comorbidity is a real phenomenon: If we would remove overlapping symptoms from our diagnostic system, would comorbidity estimates look more or less the same, or is it that comorbidity is just that, symptom overlap? The latter does not appear to be true. Numerous researchers have approached this problem via different angles and with respect to different disorders, and the majority have reached the same conclusion: Yes, there is considerable symptom overlap between some disorders, but it seems highly unlikely that this overlap explains most systematic covariation between those disorders (e.g., see Biederman et al. 1995; Bleich et al. 1997; Franklin & Zimmerman 2001; Kessler et al. 1999; Seligman & Ollendick 1998). However, there are reasons to argue that some of the methodological approaches to study the effects of symptom overlap are problematic, rendering the conclusions based on such approaches open to debate. For instance, Bleich et al. (1997) removed symptoms that overlapped between post-traumatic stress disorder (PTSD) and MDD and re-diagnosed Israeli combat veterans who were already diagnosed with PTSD and/or MDD. The results showed that, after the removal of the overlapping symptoms, 98% (95%) of the veterans with lifetime (current) MDD were re-diagnosed with MDD, whereas 70% (55%) of the veterans with lifetime (current) PTSD were re-diagnosed with PTSD. Besides the fact that the re-diagnosis percentage of both lifetime and current PTSD is somewhat low, the problem with this approach is that re-diagnosing someone with MDD without overlapping symptoms does not prove that symptom overlap does not play a role in the etiology of comorbidity between MDD and another disorder.

Suppose that someone endorses eight MDD symptoms, three of which overlap with GAD. Two problems arise here. First, the effect of removing the overlapping symptoms depends on the diagnostic cut-off: This person will be re-diagnosed with a cut-off of five while with a cut-off of four, there will be no re-diagnosis. Hence, conclusions about the effects of removing overlapping symptoms depend entirely on diagnostic cut-offs that, as we noted earlier, are at least partially arbitrary. Second, and more important, it is impossible to exclude that a re-diagnosis actually signals the major impact of overlapping symptoms in explaining the etiology of comorbidity: What if overlapping symptoms are relay stations that trigger the onset of symptoms in the entire network, resulting in a comorbid diagnosis? As such, a subsequent re-diagnosis does not have to signal the relative unimportance of overlapping symptoms. To the contrary, it could be justifiably taken to mean that overlapping symptoms have a seminal role. They cause comorbidity with such a profound effect on the network that removing them does not affect the initial diagnosis: the damage has already been done. This is not to say we think that the removal of overlapping symptoms to study its effects is a bad idea per se. We think it is a useful starting point, but (a) the effects of removing overlapping symptoms are perhaps better studied on a symptom level instead of on a diagnosis level, and (b) the matter should be investigated further; for instance, by not removing overlapping symptoms but by separately analyzing a subgroup: people who display one or more overlapping symptom pairs. Thus, we first investigated the impact of removing the six symptoms that overlap between MDD and GAD, as well as their associations with all other symptoms from the comorbidity network in Figure 4, resulting in Figure 5 (see Fig. 3 for the key). This figure confirms our initial suspicions: without the overlapping symptoms, not much comorbidity

Figure 5. The comorbidity network for major depressive disorder (MDD) and general anxiety disorder (GAD) after removal of the overlapping symptoms and their bivariate associations with the other symptoms. This network is based on exactly the same four characteristics as the full network in Figure 4. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Cramer et al.: Comorbidity: A network perspective seems to remain. In fact, only depressed mood (mDep) and loss of interest (mInt) have some relatively strong connections with GAD symptoms such as anxiety (gAnx), loss of control (gContro), and number of events that cause worry (gEvent). Next, we performed the subgroup analysis: We thus computed log odds ratios, co-occurrences, frequencies, and node strengths for only those respondents who displayed at least one pair of overlapping symptoms (e.g., both MDD and GAD concentration problems; N ¼ 1,059).10 Figure 6 presents their comorbidity network without the overlapping symptoms (see Fig. 3 for the key). This figure leaves no room for doubt about the importance of overlapping symptoms: All symptoms are more frequent and co-occur more frequently, and having one symptom increases the odds of having another one substantially (and thus the node strength) compared to the comorbidity network in Figure 5. Taking all results together, it is likely that overlapping symptoms play a more important role in explaining comorbidity than was originally thought. 8. Conclusions and future directions In this article, we have introduced a radically different conceptualization of mental disorders and their symptoms: namely, the network approach. Under the assumption of such an approach, a mental disorder is a network of symptoms that stand in direct, possibly causal, relations to one another. Comorbidity between mental disorders is then conceptualized as direct relations between symptoms of multiple disorders. We have argued that such an approach bears a closer resemblance to the reality of mental disorders and comorbidity between them, as it allows for

(1) multiple etiological processes that interact in causing symptoms, (2) interindividual differences in the manner in which a constellation of symptoms is contracted, (3) direct relations between overlapping symptoms, and (4) inequality of symptoms. Also, we have proposed an integrative method, based on bivariate associations, to visualize comorbidity networks. Based on such an empirical network for major depression and generalized anxiety, we showed that a network approach results in a host of realistic and testable hypotheses that are not naturally accommodated by latent variable models. First, it is likely that there exist pathways to comorbidity through the symptom space that are more likely than others (e.g., via core psychological symptoms such as depressed mood and loss of interest). Second, it is plausible that those pathways generally follow the same direction (e.g., we found that comorbidity from major depression to generalized anxiety appeared to be more likely than the other way around). Finally, overlapping symptoms play a more than trivial role in explaining the roots of comorbidity (i.e., we showed that symptoms of major depression and generalized anxiety were more strongly connected in people who displayed at least one pair of overlapping symptoms). The present work bears interesting relations to that of Van der Maas et al. (2006), who showed that the positive manifold of correlations between various IQ tasks – often thought to result from a single latent variable, general intelligence – may result from a dynamical system in which a network of bidirectionally related cognitive processes beneficially interact with one another during development (i.e., the mutualism model). The mutualism model serves as an excellent starting point for developing a unified theory for mental disorder networks because of their similarities. For instance, the mutualism

Figure 6. A comorbidity network for major depressive disorder (MDD) and general anxiety disorder (GAD) for those respondents (N ¼ 1,059) who displayed at least one pair of overlapping symptoms. This network is based on exactly the same four characteristics as the network in Figure 5.

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Cramer et al.: Comorbidity: A network perspective model is a dynamical system (Alligood et al. 1997) (for examples of dynamical systems in other areas of psychology, see Cervone 2004; Shoda et al. 2002; Van Geert 1998). Such a system consists of a set of possible states with a rule that determines the present state in terms of past states. At any point in time, dynamical systems are in a particular state and that state can be represented as a point in state space. If a dynamical system evolves long enough, then it will encounter one or more attractors in state space: regions in state space that the system will move towards and enter. In state spaces with more than one attractor, some systems tend to move towards one attractor and remain there in a stable state (i.e., monostable systems; see, e.g., Pisarchik & Goswami 2000). The mutualism model is an example of such a monostable system. Like the mutualism model, mental disorders are also dynamical systems that evolve over time. However, unlike the mutualism model, mental disorder networks are probably minimally bistable systems with a “disorder” attractor state and a “no disorder” attractor state between which the system oscillates. For example, in a substantial number of people who suffer from major depression, it is a well-established fact that depressive symptoms come (i.e., the system moves towards a “depressed” attractor state), and go (i.e., the system moves towards a “not depressed” attractor state), either through therapeutic intervention or spontaneous remission (e.g., see Posternak & Miller 2001). Some mental disorders may be multistable systems with the system oscillating between more than two attractor states. It is possible that bipolar II disorder is a system that oscillates between hypomania, major depressive episodes, and, under the influence of therapeutic interventions, remission states. Dynamical systems theory can be used to predict the trajectory of a system in the state space; that is, future states of the system can be predicted from earlier states, a technique that is, for instance, widely employed in weather forecasting (e.g., see Palmer 2001). Analogously, such techniques could in the future be used to predict trajectories of a variety of mental disorders, given the initial state of a network for an individual. If there are individual differences in the precise structure of networks, this may require person-specific network structures to be determined for each individual separately, as is, for instance, possible through the analysis of intra-individual time series (Hamaker et al. 2007; Molenaar 2004). The trajectory of any mental disorder as dynamical system cannot be adequately predicted without taking external variables into account. One important feature of many mental disorders is that all or most symptoms are positively correlated. As such, when modeling the reality of mental disorders from a dynamical systems perspective, if people enter the network by displaying one symptom, this symptom will quickly turn other symptoms “on.” As a result, the trajectory of such a system will be predictable and unrealistic: everyone will “contract” the mental disorder. In reality, there are many external variables that mitigate relationships between symptoms: good news that prevents someone progressing from depressed mood to thoughts of suicide, homeostasis due to which someone with sleep difficulties will not stay fatigued indefinitely, and so on. Such external variables thus play a critical role in determining toward which attractor state the system moves, and, as such, must be included in mental disorder systems.

Also, we should take into account the possibility that the entire symptom space network displays characteristics of a small world (e.g., see Barrat & Weigt 2000; Rubinov et al. 2009; Watts & Strogatz 1998). A small-world network is a highly clustered network with relatively short characteristic path lengths (i.e., it takes relatively few steps to “travel” from one node in the network to another). Networks with such properties are frequently found, ranging from the power grid of the western United States through the neural network of the worm Caenorhabditis elegans. If a general mental disorder system would indeed also display small-world features, it potentially offers a powerful explanation of the generally high comorbidity between mental disorders (i.e., short characteristic path lengths). Also, it would reconfirm the existence of distinct symptom clusters that represent distinct mental disorders (i.e., high clustering). Finally, any adequate general network model for mental disorders must encompass the fact that mental disorders as systems are essentially complex (e.g., see Cilliers 1998): Because of the interplay between the individual components (i.e., symptoms) of the system and the interaction between the system and its environment, the system/disorder as a whole cannot be fully understood by analyzing its individual components. Also, these interactions change over time, and this can result in emerging properties, properties of the system that are not evident from inspecting the individual components. In complexity research, rapid advances are made with respect to modeling emerging properties in complex systems, and the network approach for mental disorders could benefit from those advances (see, e.g., Paik & Kumar 2008; Sole´ et al. 2000). An important additional question is how dynamical properties of complex systems relate statistically and conceptually to interindividual differences as commonly analyzed with latent variable models (Molenaar 2003). As such, multiple insights from various research disciplines may be further developed and combined into a general psychometric theory of mental disorders as networks. Such a theory should, in our view, address the dynamical nature of causal systems (i.e., model that tracks the development of a mental disorder network over time), allow for representing the influence of external variables (e.g., treatment that potentially turns symptoms “off”), and allow for an adequate conceptualization of causal relations between symptoms. Advances in the areas of complexity and dynamical systems may be of considerable help in constructing such a theory. Also, given the relevance of results from various disciplines (e.g., mathematics, physics, and computer science), the construction of a viable psychometric theory based on these ideas is likely to involve the integration of theory and methods from different fields, and we therefore hope to attract the attention of scholars from a wide variety of disciplines. The need for a general theory of this type is, we think, evident: We have been looking at mental disorders through the wrong psychometric glasses, and it is high time for us to craft new ones. ACKNOWLEDGMENT

This work was supported by the Netherlands Organisation for Scientific Research (NWO) innovational research grant no. 451-03-068. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective NOTES 1. The National Comorbidity Survey Replication (NCS-R) is a nationally representative household survey of English speakers 18 years and older in the United States (see Kessler et al. 2004). The NCS-R survey schedule is the version of the World Health Organization (WHO) Composite International Diagnostic Interview that is developed for the WHO World Mental Health Survey Initiative (WMH-CIDI; Kessler & Ustun 2004). The interviews were conducted between February 2001 and April 2003. A total of 9,282 respondents participated in Part 1 of the interview (core diagnostic assessment) that we used for this article. The symptoms that participants reported within one disorder all occurred within the same time frame. 2. We did not collapse the six symptoms that overlap between MDD and GAD into three bridging symptoms because the log odds ratios between each pair of overlapping symptoms were not high enough to warrant such a collapse. A probable explanation for this is that some people, for instance, did report concentration problems in the depression section, but were unable to report those same problems in the generalized anxiety section because that section was skipped (e.g., because the respondent did not experience chronic anxiety). 3. It is prudent to note that feedback loops can create considerable methodological difficulties in model fitting, because they lead to models that cannot be recursively estimated. However, given our present state of ignorance concerning the nature of comorbidity, we think it is more useful to construct a theoretical representation that is likely to be faithful to reality, than it is to construct a model based on a list of desirable computational properties. 4. This network is based on the NCS-R questionnaire that mostly contains dichotomous items. However, some of the items were not (e.g., “How many pounds have you gained?”), and we dichotomized those according to the DSM-IV diagnostic algorithms. Details of the dichotomization process are provided at: http://www.aojcramer.com. 5. The odds ratio is the ratio of the odds of an event (e.g., suffering from loss of interest) occurring in one group (e.g., people who suffer from depressed mood) to the odds of that event occurring in another group (e.g., people not suffering from depressed mood). For cell counts in a 2x2 contingency table, the sample odds ratio equals n11n22/n12n21 (see Agresti 2002). Since the odds ratio scales between zero and infinity, with a value of 1 signifying the absence of association, the odds ratio is not optimal for visualization in our network; therefore, we used the natural logarithm of the odds ratio. A log odds ratio of 0 (i.e., an odds ratio of 1) indicates that the event is equally likely in both groups. Please note that a high co-occurrence (¼ n11) does not necessarily imply a high odds ratio. For example, (1) a high cooccurrence (n11 ¼ 500), (2) almost no people who do not have both symptoms (n22 ¼ 3), and (3) thus, relatively many people who have one or the other symptom (n12 ¼ 15 and n21 ¼ 100) yields an odds ratio of 1 (500 3/100 15), signaling no association between those symptoms. Thus, co-occurrences and odds ratios show different aspects of a data set. 6. In fact, we also computed tetrachoric correlations for the MDD and GAD symptoms with a full information maximum likelihood approach through which we dealt with the missing values that were Missing At Random (MAR). We found that the ordering of the symptoms in terms of their node strength was nearly the same as with log odds ratios. 7. We have checked the stability of the results depicted in this figure by randomly splitting the sample in two and running all analyses for both groups separately. Those separate analyses revealed the same results and, therefore, we consider the components of Figure 4 to be stable. 8. The fact that duration is weakly associated with the other MDD and GAD symptoms cannot be explained by a skip structure that only allowed respondents to progress to the other symptoms’ section if they fulfilled the duration criteria for depressed

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mood/loss of interest (MDD: more than 2 weeks) and chronic anxiety (GAD: more than 6 months): respondents with depressed mood/loss of interest for at least 3 days for more than 1 hour per day (MDD) as well as respondents with chronic anxiety for at least 1 month were allowed into the sections about the other symptoms. 9. It is important to note here that within a latent variable framework, factor loadings cannot be measures of symptom centrality as we view the concept, since those loadings are simply reliability estimates: the higher the factor loading, the more reliably an indicator “represents” the common cause. 10. The contingency tables, as well as the computational script (made in R), are available at: http://www.aojcramer.com. We have checked the stability of the results depicted in Figure 6 by randomly splitting the sample in two and have run all analyses for both groups separately. Those separate analyses revealed the same results, and therefore, we consider the components of Figure 6 to be stable.

Open Peer Commentary Latent variables and the network perspective doi:10.1017/S0140525X10000580 Catherine Belzung,a,b Etienne Billette de Villemeur,c Mael Lemoine,a and Vincent Camusa,b,d a Universite´ Franc¸ois Rabelais de Tours, Faculte´ des Sciences et Techniques, Parc Grandmont, F-37200 Tours, France; bINSERM 930, Centre National de la Recherche Scientifique (CNRS), ERL 3106, F-37200 Tours, France; cToulouse School of Economics (IDEI & GREMAQ), F-31000 Tours, France; dClinique Psychiatrique Universitaire, CHRU de Tours, F-37200 Tours, France. [email protected] [email protected] [email protected] http://lemoine.ovh.org [email protected]

Abstract: We discuss the latent variables construct, particularly in regard to the following: that latent variables are considered as the sole explanatory factor of a disorder; that pragmatic concerns are ignored; and that the relationship of these variables to biological markers is not addressed. Further, we comment on the relationship between bridge symptoms and causality, and discuss the proposal in relationship to other constructs (endophenotypes, connectionistinspired networks).

Since the early stages of the discipline of psychiatry, the construct of psychiatric semiology and nosography has been indissociable from the etiological conceptualization of observed phenomena. Nevertheless, it is widely admitted that psychiatric disorders are multifactorial and etiologically complex, and explanatory models should refer mostly to explanatory pluralism rather than to biological reductionism. Our knowledge about psychiatric disorders remains incomplete, and we can only hope to get “small explanations, from a variety of explanatory perspectives, each addressing part of the complex etiological process leading to disorder,” and try to understand “how these many different small explanations all fit together,” etiological pathways being considered “complex and interacting more like networks than individual pathways” (Kendler 2005, p. 435). Our current categorical classifications of mental disorders in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSMIV; American Psychological Association 1994) and in the World Health Organization’s International Statistical Classification of Diseases, 10th revision (ICD-10) have been conceptualized on assumptions of more global and simple hypothetical explanations.

Commentary/Cramer et al.: Comorbidity: A network perspective In that context, the clinical assessment of psychiatric conditions has been addressed in reference to the “latent trait hypothesis,” which considers each observed symptom or cluster of symptoms to be related to a specific latent cause. Any attempt to go beyond the usual categorical construct of current mental disorders classifications could constitute a valuable epistemological contribution in view of the upcoming new version of mental disorders classifications (DSM-V), as it takes an important step toward a less categorical, and rather dimensional conception of mental disorders. We have the following specific comments to make on Cramer et al.’s discussion of latent variables in the target article. 1. The target article makes a restrictive interpretation of the latent variable models. Along the article’s lines, latent variable models are represented as unidirectional trees, the “latent variable” (the common cause) being the root. In this representation, the authors assume that all links have the same importance. Yet, by definition, a latent variable is only non-observable, and is not necessarily causally central. Cramer et al. are probably right in criticizing the assumption (implicit in psychiatry) that all symptoms should be related to a central latent variable, but they mistakenly underestimate the potential role of accessory latent variables. Getting rid of all latent variables would be tantamount to assuming that everything is known about the observed phenomenon. Moreover, there is no reason why the flexibility they claim for their network approach (multi-directionality, different link strength) should not be allowed within the context of a latent variable model. 2. Besides, a heuristically good reason to suppose the existence of a latent variable is mainly therapeutic rather than methodological. This kind of hidden variable is often seen as a therapeutic target rather than an etiological node; that is, not something to find that would explain everything, but something to act upon that would dissolve everything. If a match is considered the cause of a fire in a building, rather than oxygen in the air, which is no less required to start a fire, it is because the match seems the most appropriate factor to act upon. Mackie (1974), Hesslow (1984), Gannett (1999), and Magnus (1992), among others, have shown the importance of pragmatic concerns in the search for a single target which might be called the cause of a disease (it is called the problem of causal selection). This kind of pragmatic interpretation of a latent variable as “what we have to act upon” may justify the otherwise objectionable assumption that there is actually a latent variable which explains and causes everything. There is, however, a question as to how the network approach is to be translated into the definition of therapeutic targets. For instance, while such a definition is obviously easy on the basis of the target article’s Figure 1, one might ask what could be proposed on the basis of Figure 4. 3. It would also be interesting to discuss this model, as well as the latent variable model, with regard to the biological markers of these diseases. Indeed, particular markers of the disorder could be related to specific biological alterations. For example, anhedonia could be related to a deficit in nucleus accumbens processing, or a defect in stress reactivity to a dysregulated neuroendocrine axis. 4. Beyond that, in the case of two comorbid disorders, do the authors propose that each symptomatic node be related to a specific biological dysfunction that would be common to the two comorbid pathologies? In this case, a given biological marker defect underlying pathology A would also be altered in the comorbid pathology B. If there is no latent variable underlying the different symptomatic features, what is the explanation as to why these symptoms often co-occur? Moreover, if two comorbid disorders have a common epiphenomenal symptom, should this be regarded as a bridge symptom? For example, if decreased eating occurs in an anxiety disorder as well as in depression, but does not induce (or is unrelated to) any of the other symptoms of depression or anxiety, might

it not be considered a bridge symptom underlying comorbidity? How can symptoms be distinguished from “non-symptom causal processes” (sect. 2, para. 9) or from the “external effects” (sect. 5, para. 6) if the boundaries of the disorders are “fuzzy” (sect. 6, para. 6)? 5. It would be interesting to compare the network model described by Cramer et al. with the psychopathological endophenotype approach that has been developed to dissect major depression into different independent entities (see, e.g., Hasler et al. 2004), or with other constructs used in the field of psychiatry, such as connectionist-inspired ones (e.g., Tanti & Belzung 2010).

The rocky road from Axis I to Axis II: Extending the network model of diagnostic comorbidity to personality pathology doi:10.1017/S0140525X10000592 Robert F. Bornstein Derner Institute of Advanced Psychological Studies, Adelphi University, Garden City, NY 11530. [email protected]

Abstract: Although the network model represents a promising new approach to conceptualizing comorbidity in psychiatric diagnosis, the model applies most directly to Axis I symptom disorders; the degree to which the model generalizes to Axis II disorders remains open to question. This commentary addresses that issue, discussing opportunities and challenges in applying the network model to DSMdiagnosed personality pathology.

Cramer et al.’s network model represents a promising new approach for conceptualizing and quantifying comorbidity in psychiatric diagnosis, helping avoid the thorny challenge of operationalizing latent constructs, and shifting the focus of comorbidity research from syndrome to symptom. Scrutiny of Cramer et al.’s analysis reveals that the theoretical underpinnings and empirical evidence bearing on this model apply most directly to Axis I symptom disorders (e.g., major depression, generalized anxiety). Because Axis II personality disorders differ in myriad ways from Axis I symptom disorders, the degree to which the network comorbidity model generalizes to Axis II disorders remains open to question. This commentary addresses that issue, discussing issues that arise in applying the network model to DSM-diagnosed personality pathology (i.e., the personality disorder [PD] diagnoses offered in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition or DSM-IV; American Psychiatric Association 1994). As Cramer et al. have noted, diagnostic comorbidity evidence involving DSM-IV Axis I disorders can yield ambiguous, confusing patterns. Diagnostic comorbidity evidence bearing on DSMIV Axis II is far worse. Consider: The number of differential diagnoses per DSM-IV PD ranges from 3 (dependent, obsessivecompulsive) to 7 (paranoid), with the mean number of differential diagnoses per PD being 4.5. Thus, on average each DSM-IV PD shows substantial overlap with 50% of the remaining PDs. When Ekselius et al. (1994) calculated correlations among interview-derived scores for PDs in a heterogeneous sample of psychiatric patients and nonclinical participants, they obtained a mean interscale correlation (r) of .41, and statistically significant interscale correlations in 41 of 45 comparisons (91%). Subsequent comorbidity studies have confirmed these results (Bornstein 1998; 2005). Given these patterns, extending the network comorbidity model to Axis II presents some unique challenges, but it also involves some unique opportunities to gain new perspective on BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective the model, its advantages, and its limitations. Two issues are germane in this context. 1. PD symptoms differ fundamentally from Axis I symptoms. Axis I disorders are sometimes called “symptom disorders” because they are characterized by prominent, psychologically painful symptoms (e.g., depressed mood, difficulty sleeping, binge eating). Whether or not patients choose to acknowledge them when asked, they are typically aware of experiencing these symptoms (even if not fully aware of the symptoms’ negative impact). The situation is very different for Axis II PDs, which have traditionally been conceptualized as being “ego syntonic” (i.e., consistent with the patient’s experience of self). As a result, personality-disordered patients typically have far less insight into their symptoms than do patients with Axis I disorders, which complicates diagnosis, decreases motivation for treatment, and reduces therapeutic efficacy (Peters 1990; Shedler & Westen 1999). In the context of the network model, these Axis I – Axis II differences have two noteworthy implications. First, although self-report assessment tools (e.g., questionnaires, diagnostic interviews) are the measures of choice for rendering Axis I diagnoses, such measures are of limited value in rendering Axis II diagnoses (see, e.g., Widiger & Samuel 2005). Indirect measures (e.g., free-response tests) and reports from knowledgeable informants must be used in conjunction with self-report instruments to assess PDs reliably (Bornstein 2007). Second, these Axis I – Axis II differences in insight and selfawareness suggest that the definition of latent variable as conceptualized in the network model must be expanded when applied to Axis II. Here it is not only necessary to distinguish observable symptoms from unobservable latent constructs, as the network model suggests, but also to distinguish symptoms that are phenomenologically latent (i.e., ego syntonic) from those that are experienced as problematic by the patient (i.e., ego dystonic). 2. Unlike Axis I criteria, Axis II criteria are revised to minimize comorbidity. In many clinical settings the most common Axis II

diagnosis is “mixed PD,” and epidemiological data indicate that a sizeable proportion of PD-diagnosed patients – more than 50% in some samples – receive two or more PD diagnoses (Bornstein 2003; Widiger & Clark 2000). As a result, symptom revision across successive editions of the DSM entails somewhat different goals on Axis I and Axis II. On Axis I symptoms are revised to increase diagnostic accuracy, but on Axis II symptoms are revised to maximize accuracy while simultaneously reducing comorbidity. As I have noted elsewhere (Bornstein 2003), when Axis II symptoms are reworded or removed merely to limit escalating comorbidity rates, clinicians are choosing to alter reality (i.e., a high level of PD comorbidity) to fit some idealized conceptualization of PDs as distinct and separate syndromes. For example, “frantic efforts to avoid real or imagined abandonment” (see Diagnostic and Statistical Manual of Mental Disorders, 3rd edition, revised [DSM-III-R]; American Psychiatric Association 1987, p. 347) was removed from the dependent PD criteria in DSM-IV because patients with borderline PD also show this symptom, but every extant model of dependent PD would argue for inclusion of this symptom (Bornstein 2005). Removing certain PD symptoms merely to minimize PD overlap is akin to arguing that labored breathing should no longer be considered a symptom of pneumonia because patients with emphysema also show this symptom. Clearly, the contrasting strategies used to revise symptoms on Axis I and Axis II present a challenge when the network comorbidity model is extended from symptom disorders to personality pathology. Without question, Cramer et al.’s network model represents a promising new approach to conceptualizing and quantifying comorbidity in psychiatric diagnosis. This perspective not only captures dynamic features of psychopathology that traditional latent variable models cannot capture, but has the additional advantages of shifting the focus from surface behavior to underlying process, and the level of analysis from syndrome to

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symptom. Extending the network model to Axis II will be challenging, but likely to benefit the model over the long term by compelling researchers to confront conceptual and empirical challenges that do not arise when the model is applied to Axis I. Questions regarding the generalizability of symptom clusters, nodes, and bridge symptoms across culture, age, and gender are almost certain to emerge, and as research on the network model advances, it will not only be useful to extend this model from Axis I to Axis II, but to begin to address aspects of crossaxis comorbidity as well.

Aligning psychological assessment with psychological science doi:10.1017/S0140525X10000737 Daniel Cervone Department of Psychology (mc 285), University of Illinois at Chicago, Chicago, IL 60607-7137. [email protected] http://www.psch.uic.edu/dcervone.asp

Abstract: Network analysis is a promising step forward in efforts to align psychological assessment with explanatory theory in psychological science. The implications of Cramer et al.’s analysis are quite general. Networks analysis may illuminate functional relations not only among observable behaviors that comprise psychological disorders, but among cognitive and affective processes that causally contribute to everyday experience and action.

One of contemporary psychology’s more curious features, long noted (Cervone 1991; Mischel 1973), is that the strategies through which investigators pursue two of the fields’ primary goals are discordant. One goal is to understand the workings of the mind. Although there may be disagreement on the details – the precise nature of mental mechanisms and abilities, and the degree to which they are innately specified or developed through interaction with the physical and social world – there is consensus at a broad strategic level. The mind is a complex system of distinct yet functionally related parts (e.g., Barsalou 1999; McClelland & Rogers 2003). Multiple interacting subsystems, both cognitive and affective, contribute to experience and action (Cervone 2004; Mischel & Shoda 1995; Sander et al. 2005). “Observable behavior is an interaction effect par excellence” (Fodor 1983, p. 1). The other goal is to assess qualities of the individual. Assessment efforts commonly describe people with respect to constructs identified in latent variable analyses. In clinical diagnosis, the constructs are diagnostic categories. In personality trait psychology, they are dimensions (Costa & McCrae 1992). In either case, as Cramer et al. highlight, the common cause hypothesis of latent variable modeling dictates that the observable behaviors indicative of a given category or dimension are not functionally related to one another. Local independence among the indicators is assumed. At the level of substantive theory, few psychologists are likely to embrace the common cause hypothesis and its consequences. Clinicians commonly reject essentialist views in which a diagnostic category corresponds to a singular cause (Ahn et al. 2006). Investigators who employ latent variable modeling in their research may, when pressed, abandon its common cause assumptions on theoretical grounds (see Cervone et al. 2006). “Problem and method,” then, “pass one another by” (Wittgenstein 1953/ 2001, p. 197). Even investigators who recognize that actions, affects, and cognitions interact adopt latent variable methods that obscure these interactions from view. The resulting conceptual contrast is just as sharp as Cramer et al. suggest. Consider a standard psychological science

Commentary/Cramer et al.: Comorbidity: A network perspective account of a person’s tendencies to experience emotions such as anxiety and fear (“symptoms,” in the case of disorders). The affective scientist might explain them by reference to interactions among physiology, cognitive appraisals processes, and enduring beliefs (e.g., Sander et al. 2005). A developmentalist would add that temperament, which itself develops partly through environmental interaction, contributes to this interacting system of cognitive and affective elements (Schmidt & Fox 2002). Importantly, in any such account, emotional tendencies such as anxiety and fear are the explananda, the phenomena to be explained. The interacting mental systems are the explanans. The latent variable approach turns this world upside down (Cervone 1999). In comorbidity analyses, the high-level psychological tendency “internalizing” (Krueger 1999) does explanatory work: It explains correlations among lower-level locally independent tendencies such as anxiety and fear. The tendencies to experience anxiety and fear, in turn, do more explanatory work: They explain correlations among yet lower-level locally independent tendencies such as the experience of panic and social anxiety. And so it goes; it is locally independent tendencies all the way down. Not only are there no interactions among indicators of a given construct; there also is no explanation of experience and action by reference to well-defined systems of mind or brain. This might be palatable if investigators were careful to describe latent variables merely as clusters of interrelated behaviors. But, instead, they commonly discuss them in terms normally reserved for structural entities with causal power (Cervone 2005). For example, internalizing, a construct that summarizes between-person correlations among indices of psychological distress, is said to be a “substrate” (Krueger 1999, p. 926) of mental disorders. This is not unlike a geologist positing a substratum of “destructiveness forcefulness” to explain a region’s tendency to experience both volcanoes and earthquakes. Explanations that reference abstract tendencies of the sort identified in latent variable analyses are seductive (Kagan 1998) – so much so that, once, even Cramer et al. are enticed. Did they really mean to say that “neuroticism” – a latent variable that reflects intercorrelations among dispositional tendencies to experience anxiety, hostility, self-consciousness, impulsiveness, vulnerability, and depression (Costa & McCrae 1992) – “can trigger the onset of depression” (sect. 4, para. 5, emphasis added)? Neuroticism is ripe for network analysis. One would not claim that it “triggers” depression, since depression is part of neuroticism, and neuroticism is conceptualized as a constant (see Borsboom et al. 2003). The great virtue of Cramer et al.’s article is that they not only articulate a problem, but provide a solution: network analysis. Future work might expand their current scope. In principle, networks could include functional relations not only among observable behaviors, but among cognitive and affective components as well; empirical evidence documents numerous functional relations, such as the influence of selfconsciousness on emotion (Mor & Winquist 2002), self-efficacy perceptions on motivation (Bandura 1997), knowledge structures on self-appraisals (Cervone et al. 2008), and mood on self-evaluations (Cervone et al. 1994). One might account for personality traits in the manner Cramer et al. account for diagnostic categories and comorbidities. Functional relations among perceived self-efficacy, personal goal-setting, and disciplined, persistent behavior (e.g., Bandura & Cervone 1986), for example, might enable one to view conscientiousness (whose components include competence, achievement striving, self-discipline, and dutifulness; Costa & McCrae 1992) as a cluster of functionally interrelated cognitive – affect elements and their behavioral effects. Such an effort requires an assessment method that taps these cognitive and affective elements. A social-cognitive approach to

assessment (Cervone et al. 2001) is apt in that it addresses “functional relations among affect and physiological arousal, cognition, and action” (Cervone et al. 2001, p. 41) rather than latent variables measured by independent indicators. Social-cognitive methods, and recent clinical assessment efforts (Haynes et al. 2009), are sensitive to individual idiosyncrasy, thus addressing Cramer et al.’s recognition of possible individual-level variability in network structure. For more than four decades, psychologists have called for assessment and measurement strategies that align with the body of knowledge available in psychological science (Mischel 1968). Cramer et al.’s contribution is a most valuable step in this direction. ACKNOWLEDGMENTS I thank Tracy L. Caldwell and Yuichi Shoda for their comments on a draft of this commentary.

Comorbid science?1 doi:10.1017/S0140525X10000609 David Danks,a,b Stephen Fancsali,a Clark Glymour,a,b and Richard Scheinesa a

Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, and bInstitute for Human and Machine Cognition, Pittsburgh, PA 15213. [email protected] http://www.hss.cmu.edu/philosophy/faculty-danks.php [email protected] [email protected] http://www.hss.cmu.edu/philosophy/faculty-glymour.php [email protected] http://www.hss.cmu.edu/philosophy/faculty-scheines.php

Abstract: We agree with Cramer et al.’s goal of the discovery of causal relationships, but we argue that the authors’ characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.

We agree with the view that Cramer et al. develop in the target article: that naı¨ve latent variable models often fall woefully short of ideal. Unfortunately, their proposed solution and accompanying test case suffer from a number of flaws. Cramer et al. begin with a straw man: They assume that, in a latent variable model, symptoms cannot also influence one another. Unless we define “latent variable model” to exclude such effects, there is no reason to impose such a constraint on our models. Mathematically, it is straightforward for latent variable models to have both latent common causes of measured variables and direct influences of measured variables on other measured variables. This is often the case for actual causal structures; for example, when there is confounding in observational or quasi-experimental studies. Cramer et al. further claim that a “latent variable model renders all symptoms equally central and thus exchangeable” (sect. 5, para. 3). This claim is difficult to understand. “Central” is neither a causal nor a statistical notion; “exchangeable” is a statistical notion that, if meant, would be quite inappropriate in this usage. Cramer et al. might mean that in latent variable models all symptoms have the same variance, or the same dependence on any latent variables, or in their probability distributions conditional on values of latent variables, or in their probabilities conditional on one another. Each of these claims is violated in many latent variable models in the social sciences and elsewhere, and all of these claims are false unless “latent variable model” is arbitrarily defined so as to satisfy them. But that would be to focus on BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective a model class that no one ought to accept a priori in the first place. Cramer et al. focus on a stiff but appropriate standard for (their version of) latent variable models: The models should get the causal relations right. Unfortunately, they do not apply that same standard to their favored alternative. Instead, they resort to representing simple associations, with occasional suggestions that the relationships they so specify could be causal (e.g., sleep deprivation causes tiredness). Simple associations cannot, in general, be used reliably to estimate causal relations: they ignore possible screening off (conditional independence) relations, measurement errors, and latent confounding, and they give no direction to causal relations when they exist. Cramer et al. focus on two extreme model classes and ignore the enormous space of (learnable and estimable) models that lie between these poles; those models can include all of latent variables, direct causal connections, and feedback cycles (of varying speeds). Consider instead searching for graphical causal models from their data. Absent latent variables, graphical causal models – both cyclic and acyclic – specify conditional independence relations. These relations can be used to search for cyclic and acyclic causal models. The theory of cyclic graphical models is difficult and underdeveloped, and for binary variables no adequate search procedure is available, but the target article does not engage what is known (e.g., Lacerda et al. 2008; Pearl & Dechter 1996; Richardson 1996). For acyclic graphs, there are many correct search algorithms (e.g., PC, Spirtes & Glymour 1991; FCI, Spirtes et al. 1993; Conservative PC or CPC, Ramsey et al. 2006; Greedy Equivalence Search [GES], Meek 1997). The PC algorithm, for example, is an asymptotically pointwise consistent search procedure under independent and identically distributed (i.i.d.) sampling when there are no latent variables and the structure is acyclic. The PC algorithm will sometimes return doubleheaded arrows; asymptotically, the appearance of such structures in the PC output indicates latent common causes of the connected variables. We can at least begin to explore the possibilities with PC. The data that Cramer et al. use to illustrate their approach are missing more than 70% of the possible values, and there is no

Figure 1 (Danks et al.).

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explanation in their article of how those missing values are treated. There are several possibilities: A search can be conducted using only cases with no missing values, but that would include only about 10% of the cases; missing values can be replaced at random according to some prior distribution; missing values for a variable can be replaced using a probability distribution equal to the frequency distribution of that variable in the available data; or, as in the PC algorithm, relevant statistics can be computed using available data and ignoring missing values. For the data Cramer et al. provide, and a .05 alpha value for conditional independence decisions, the PC algorithm yields the graph in Figure 1: The variables form three distinct clusters: The larger two correspond to the two focal diagnostic categories, connected only by the two measures of sleep. The MDD (major depressive disorders network) measures form two disconnected components. Four variables (gSleep, gConc, gFatig, gIrri) form a chain of double-headed arrows, suggesting that there may be an unobserved common cause. The BPC (Build Pure Clusters) search algorithm (Silva et al. 2006) estimates whether a set of variables shares a latent common cause; BPC is asymptotically correct for binary variables whose values are two-valued projections of a Gaussian distribution. BPC finds that gSleep, gConc, gFatig, and gIrri do have a latent common cause, as do a separate cluster of MDD measures (mRep, mRest, mSuic). Similar results are found if missing values are replaced using the base frequency of each variable value. This analysis is scarcely complete. For example, were the data good enough to warrant it, one could apply the FCI (Fast Causal Inference) algorithm (Spirtes et al. 1993), which is correct when there are both latent common causes and direct influences of measured variables on one another. In our view, Cramer et al. unnecessarily restrict the modeling options, do not offer a plausible, reliable method for causal inference, and fail to explore what the data from their own example might reveal. ACKNOWLEDGMENTS David Danks is partially supported by a James S. McDonnell Foundation Scholar Award. Clark Glymour is partially supported by a grant from the James S. McDonnell Foundation.

PC output for Cramer et al.’s data

Commentary/Cramer et al.: Comorbidity: A network perspective NOTE 1. Authors are listed in alphabetical order; all contributed equally to this commentary.

Visualizing genetic similarity at the symptom level: The example of learning disabilities doi:10.1017/S0140525X10000749 Oliver S. P. Davis and Robert Plomin MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London SE5 8AF, United Kingdom. [email protected] http://www.iop.kcl.ac.uk/staff/profile/?go¼10880 [email protected] http://www.iop.kcl.ac.uk/staff/profile/?go¼10628

Abstract: Psychological traits and disorders are often interrelated through shared genetic influences. A combination of maximumlikelihood structural equation modelling and multidimensional scaling enables us to open a window onto the genetic architecture at the symptom level, rather than at the level of latent genetic factors. We illustrate this approach using a study of cognitive abilities involving over 5,000 pairs of twins.

A surprising finding emerging from genetic studies across diverse learning disabilities is that most genetic influences are shared:

They are “generalist” rather than “specialist” (Plomin & Kovas 2005). We know this because multivariate genetic analysis of twins yields genetic and environmental correlations among traits; high genetic correlations point to a shared genetic etiology and frame a “generalist genes” hypothesis. Although recent advances in molecular genetics, such as genome-wide association, are revealing the genetic variants that are responsible for these common influences (Wellcome Trust Case Control Consortium 2007), we are beginning to realize that the genetic and environmental architecture of psychological traits is far more complex than previously imagined. Just as Cramer et al. highlight the difficulties of psychiatric diagnosis at a phenotypic level, we have argued that, at an etiological level, such common disorders are quantitative traits reflecting multiple underlying dimensions of genetic (and environmental) risk (Plomin et al. 2009). To maximize our chances of identifying particular genetic variants, it is essential that we understand the genetic relationships among these traits by estimating and comparing the genetic correlations derived from genetically sensitive study designs (Plomin et al. 2008). In common with Cramer et al., we have found that one of the most effective ways to present and reason about such high-dimensional information is through graphical representation (Tufte 2001). Accurate estimation of multivariate statistics such as genetic and environmental correlations requires large samples. We recently exploited widespread access to inexpensive and fast Internet connections in the United Kingdom to assess over 5,000 pairs of 12-year-old twins from the Twins Early Development Study

Figure 1 (Davis and Plomin). Latent factor twin model with genetic correlations highlighted: A, additive genetic effects; C, shared (common) environmental effects; and E, nonshared environmental effects. Squares represent measured traits, and circles represent latent factors. The lower tier of arrows represents factor loadings, and the second tier represents genetic and environmental path coefficients. The curved arrows at the top represent correlations between genetic (solid lines) and environmental (dotted lines) latent factors. Adapted from Davis et al. (2009). BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective (TEDS; Oliver & Plomin 2007) on four batteries: reading, mathematics, general cognitive ability (g), and, for the first time, language (Haworth et al. 2007). A multivariate structural equation model using latent factors showed that, as expected, genetic correlations among reading, mathematics, and g are high in late childhood and early adolescence (0.75–0.91), with language as highly correlated genetically with g as reading and mathematics (see our Fig. 1 here) (Davis et al. 2009). However, as Cramer et al. demonstrate, there is another level of detail that cannot be investigated through analysis of latent factors. The batteries that index the latent constructs of reading, mathematics, g, and language can be broken down into their constituent tests, our “symptoms,” to better understand the complex relationships among cognitive components that result in high correlations at the level of latent factors. Our own approach to exploring these relationships used multidimensional scaling of genetic correlation matrices to produce interactive graphical representations of the underlying genetic architecture. As shown in Figure 1, each latent construct was characterised by three or four subscales that assessed different aspects of the trait: 14 tests in total. These measures are described in detail in Davis et al. (2009). Multidimensional scaling can be used to reduce the high-dimensional relationships among the tests to two or three spatial dimensions. Classical (metric) multidimensional scaling (Gower 1966; Young & Householder 1938) requires a matrix representing the pair-wise “distance” between every pair of traits. With a high-performance computing cluster we calculated the pair-wise genetic correlations among all the tests in the battery using maximumlikelihood structural equation model-fitting in Mx (Neale et al. 2006) to make a genetic correlation matrix. The genetic correlation matrix represents the genetic similarity among the tests. To represent the genetic dissimilarity, or distance, we subtracted the correlations in the matrix from 1. We performed

multidimensional scaling on the resulting matrix using the R function cmdscale (R version 2.10.1; R Development Core Team, 2009) and checked whether three dimensions allowed an adequate representation of the true distance matrix using the criterion suggested by Mardia et al. (1979) and inspection of a Shepard diagram, which plots the distances obtained from multidimensional scaling against the values in the original distance matrix. Figure 2 represents the well-fitting three-dimensional solution using the graphics library OpenGL, available in R through the rgl package. The screenshot shows genetically similar traits clustering together and genetically dissimilar traits more distant from one another in space. For a sense of scale, the closest relationship is between two measures of reading comprehension, GOAL and PIAT in the centre of the figure, with a genetic correlation of almost 1; the most distant relationship (a genetic correlation of 0.12) is between TOWRE on the far left, a measure of reading fluency, and Picture Completion on the far right, a measure of nonverbal ability. The image highlights subtle patterns of genesharing among the tests. For example, the mathematics tests cluster close together, while the comprehension and fluency components of reading ability are relatively separate in the centre and far left. Likewise, the g battery falls naturally into verbal (near the top) and nonverbal (far right) components. Meanwhile, reading comprehension, the verbal components of g, and language cluster at the top of the figure. Although most correlations are strong, the heterogeneity tells a more nuanced version of the generalist genes story than we saw at the level of latent factors. This approach to visualizing the genetic relationship among traits at the symptom level complements Cramer et al.’s network approach to phenotypic comorbidity. When they call for scholars from a wide variety of disciplines to join together to fashion a new approach to psychometrics, they may certainly count geneticists among their allies.

Figure 2 (Davis and Plomin). Screenshot of a three-dimensional representation of genetic similarities among the tests that form the latent factors in Figure 1. Each sphere represents a test, and tests are colored by corresponding latent factor from Figure 1: green for reading, blue for mathematics, red for g, and yellow for language. Tests with similar genetic influences are closer together in space.

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Commentary/Cramer et al.: Comorbidity: A network perspective ACKNOWLEDGMENTS Oliver Davis is supported by a Sir Henry Wellcome Postdoctoral Fellowship from the Wellcome Trust (WT088984). The Twins Early Development Study is supported by the U.K. Medical Research Council (G0500079), the Wellcome Trust (WT084728), and the U.S. National Institute of Child Health and Human Development (HD44454 and HD46167).

An agenda for symptom-based research doi:10.1017/S0140525X10000750 William Fleeson, R. Michael Furr, and Elizabeth Mayfield Arnold Department of Psychology, Wake Forest University, Winston-Salem, NC 27109. [email protected] [email protected] [email protected] http://www.wfu.edu/psychology/faculty/fleeson.html http://psych.wfu.edu/furr/ http://www2.wfubmc.edu/psychiatry/Faculty/ physician_info.htm?PhysicianID¼653

Abstract: The network approach proposed by Cramer et al. suggests fascinating new directions of research on mental disorders. Research is needed to find evidence for the causal power of symptoms, to examine symptoms thoroughly, to investigate individual differences in edge strength, to discover etiological processes for each symptom, and to determine whether and why symptoms cohere into distinct mental disorders.

The network approach proposed by Cramer et al. suggests fascinating new directions of research on mental disorders. This commentary highlights the advantages of the target article’s approach and proposes directions for research supporting it. In particular, the proposition that symptoms causally affect one another, rather than being mere signs or outcomes of an underlying latent disorder, is exciting. Although symptoms would be powerful even as mere outcomes (e.g., by being intensely aversive for self or others), ascribing causal power to symptoms raises them to an even greater level of importance as the very heart of psychological disorders. 1. Find evidence for the causal power of symptoms. An urgent line of research is to test the proposal that symptoms have causal power for other symptoms. Although the network approach presented by the target article is plausible, we believe that this proposal has yet to be supported by sufficient empirical evidence. The most directly relevant evidence would be a demonstration of the causal power of symptoms. Evidence for the causal power of symptoms could be obtained from cross-lagged effects, either long-term effects from longitudinal designs or short-term effects from experience-sampling studies (Conner et al. 2009). Evidence for causality might even be obtained from experimental manipulation of symptoms (if ethical and minimally distressing). Recent research on “normal” personality has taken a similar line. For example, extraversion and positive affect are robustly correlated (Lucas et al. 2000), which is often interpreted as resulting from a latent extraversion trait factor causing a latent positive affect trait factor. One of us has conducted experiments showing instead that state manifestations of extraversion (becoming extraverted for a moment, analogous to a disorder’s symptom) cause the state of positive affect (McNiel & Fleeson 2006). That is, the states of normal traits have causal power, suggesting that the symptoms of mental disorders may also have causal power, and that manipulating them in experiments or tracking their lagged effects in experience-sampling studies may be fruitful. 2. Examine symptoms thoroughly. The network approach suggests a more thorough assessment of symptoms. If symptoms are more than indicators, then their patterns and frequencies of occurrence should be assessed. For example, experiencesampling studies, in which participants report symptoms every

few hours for a few weeks, would provide rich information about the frequencies, patterns, and co-occurrences of symptoms (i.e., evidence of symptom co-occurrences for a given person across time or situations). This information is key for determining node strength and edge strength in the network. For example, the frequencies and co-occurrences of emotional volatility (Trull et al. 2008), idealization, suicidal ideation, and emptiness on a daily basis could help determine the node strengths and edge strengths in Borderline Personality Disorder (BPD) 3. Investigate individual differences in edge strength. The incorporation of edges or connections between symptoms opens up new possibilities for the conceptual definitions of disorders and of diagnosis. Rather than individual differences in the levels of the symptoms being the primary component of a disorder, individual differences in the interconnections among symptoms may become an additional or central component to the disorder. If symptoms affect one another, then different people might have different strengths of causation linking symptoms. For example, some people might have a strong connection between perceived feelings of abandonment and self-injury, such that abandonment is usually followed quickly by selfinjury, whereas others may have a weak connection between these symptoms. Such individual differences in strengths of connections between symptoms might determine whether a given individual experiences only one symptom or descends into a cascade of mutually activated symptoms; since the difference between these two outcomes comes from the strength of the connections, it might mean that having the disorder or not is a matter of having or not having strong connections. That is, everyone might be vulnerable to occasional symptoms (e.g., anger, perceptions of rejection), but only some may show an entire collection of symptoms. Carried still further, it is possible that different individuals, all with relatively strong edges among some symptoms of a disorder, nonetheless differ in which edges are strongest. Thus, they all suffer a cascade of symptoms when any one symptom is activated, but they suffer different cascades from each other. This kind of heterogeneity in symptoms would be identified by withinperson analyses of experience-sampling data, which would reveal individual differences in co-occurrences of symptoms, and aid in risk assessment. 4. Discover etiological processes for each symptom. If there were only one causal factor for a mental disorder, then it would be more plausible that there is only one etiological process for that mental disorder; namely, the one that produces the latent factor within the individual. However, if each symptom has causal power to set up a chain of consequences, then each symptom likely has its own etiological history. Research that identifies the triggers and processes resulting in each symptom would be useful. Again, experience-sampling data would reveal co-occurrences between triggers and symptoms. 5. Determine whether and why symptoms cohere into distinct mental disorders. A concern about the network approach is that,

without single latent causes, disorders may lose coherence and become replaced by countless individual symptoms, creating a situation too chaotic for research or for treatment. Cramer et al. have proposed some plausible technical rules for identifying coherent disorders, but they have not provided evidence that such rules produced distinct clusters or disorders. Thus, it is necessary to determine whether those rules produce coherence, whether other rules are needed, or whether distinct disorders are hard to identify. Distinct disorders may also arise from multiple, small latent causes, which affect each other causally. We are embarking on a 5-year project on the symptoms of BPD that undertakes some of these lines of research. The evidence for or against the network approach is still to be produced, but we are convinced that the approach supports lines of research that are likely to produce new important insights into disorders. In turn, this should lead to new treatments focused on the causal power of symptoms. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Extending the network perspective on comorbidity doi:10.1017/S0140525X10000762 Brian D. Haig and Frances M. Vertue Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand. [email protected] [email protected] http://www.psyc.canterbury.ac.nz

Abstract: Cramer et al. make a good case for reconceptualizing comorbid psychopathologies in terms of complex network theory. We suggest the need for an extension of their network model to include reference to latent causes. We also draw attention to a neglected approach to theory appraisal that might usefully be incorporated into the methodology of network theory.

Despite its prominence in clinical research, the concept of comorbidity is heavily contested (e.g., Lilienfeld et al. 1994). In the target article, Cramer et al. contend that the major problem with comorbidity research stems from its widespread adoption of standard latent variable theory, which holds that latent variables are common causes that explain the correlations between the manifest variables to which they give rise. On this view, comorbidity is a bidirectional relation between latent variables that causally produce correlated indicator variables or symptoms. The problem arises from the model’s acceptance of the statistical assumption of local independence, which says that the manifest indicators are solely due to the influences of the common causes; they cannot relate to one another causally. We are unsure how widespread in comorbidity research this latent variable theory actually is, but we agree with Cramer et al. that its inability to allow manifest variables or symptoms to be causally related is a major limitation of its conceptualization of comorbidity. Cramer et al.’s alternative conceptualization of comorbidity as complex networks of causally related symptoms holds considerable promise for resolving some troubling issues in the field, and the authors point to several advantages that this perspective holds. A major achievement of the target article is its demonstration that network theory has the resources to help resolve several syndromal questions about comorbidity (see also Meehl 2001). Cramer et al.’s network depiction of comorbidity has no place for latent variables as common causes. In fact, as it is currently formulated, their model has no place for latent variables at all. The result is a “flat” model of comorbidity that embraces only manifest symptoms in causal relation to one another. However, this particular feature of the model should be seen as a contingent feature of its initial formulation, not as an expression of an unyielding empiricist commitment to focus on the phenomenology of psychopathology only. The authors make clear that their network characterization of comorbidity is necessarily incomplete. They explicitly acknowledge that relations between symptoms are likely to be mediated by other unobservable (i.e., latent) factors in causal chains. Of course, to embrace latent factors in an extended network model would not be to admit common cause latent variable theory back into the fold. But it is perhaps worth noting here that there are alternative nonstandard models within latent variable theory that permit the specification of different relations between latent and/or manifest variables. For example, Keith Markus has suggested to us in personal communication that there are no good theoretical and statistical reasons why one could not insert direct causal paths between indicator variables in a structural equation model. We think that further research on network formulations of psychopathologies is justified on strategic methodological grounds, and should now be extended to explicitly incorporate reference to etiological factors at whatever level of specificity can be achieved. Despite the use of latent variable models in research on psychopathology, there is a general distrust of theorizing about latent causes by researchers in the field. The largely

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atheoretical nature of the DSMs III and IV (Diagnostic and Statistical Manual of Mental Disorders, 3rd and 4th editions; American Psychiatric Association 1987; 1994), for example, came about because their task forces believed that good causal theories of psychopathology were seldom to be had. We think the construction of explanatory theories of comorbid conditions should be pursued with vigour. Good theories are to be valued because they are our primary vehicles for understanding the relevant syndromal facts. However, this will require the implementations of sound theory construction strategies. Psychology, with its penchant for the weak testing of austere theories via tests of statistical significance, and an overreliance on goodness-of-fit measures of empirical adequacy, has been somewhat remiss in this regard. Relatedly, there has been a general failure to acknowledge that good theories in science are often generated by abductive or explanatory means in order to explain established phenomena, developed through a strategy of analogical modeling, and evaluated on multiple criteria, some of which have to do with the explanatory worth of theories (Haig 2005; for an application of abductive theory construction methods to clinical reasoning and case formulation, see Vertue & Haig 2008). In a preliminary test of their hypothesis about the importance of bridge principles in explaining comorbidity, Cramer et al. use the Akaike information criterion to judge the fit of competing models. This can be seen as one approach to the widespread practice in science of testing hypotheses about models for their predictive accuracy. However, the inclusion of latent factors in a network model of comorbidity raises the question of how one should evaluate those models when they contain explanatory hypotheses. Cramer et al. rightly point out that common cause latent variable theories have modest explanatory power. However, network theories that appeal to latent causes more generally have the potential to offer more powerful explanations. Their evaluation will have to combine information about postulated causes (preferably in the form of causal mechanisms), correlations, and competing causal accounts. An approach to theory evaluation that can do this is known as inference to the best explanation (see Haig 2009). With inference to the best explanation, the ideas of explanation and evidence come together, and explanatory reasoning becomes the basis for evaluating theories: The explanatory goodness of theories counts in their favour. Conversely, the explanatory failings of theories detract from their credibility. According to Thagard (1992), inference to the best explanation is essentially a matter of establishing relations of explanatory coherence between propositions within a theory. To infer that a theory is the best explanation is to judge it as more explanatorily coherent than its rivals. Theories depicted as networks of propositions, including network models of comorbidity, lend themselves naturally to evaluation in terms of considerations of explanatory coherence. In conclusion, we think the long-term prospects of network theory in psychopathological research are good precisely because it can exploit the considerable conceptual and investigative resources of dynamical systems theory. Cramer et al.’s dynamical systems conceptualization of comorbidity can study change in individuals over time. It therefore promises to feature in a future clinical psychology that has moved beyond the strictures of the present DSM model to embrace, among other things, an idiographic, process-oriented approach to scientific research on comorbidity and other psychopathologies.

Symptom networks and psychiatric categories doi:10.1017/S0140525X10000774 Nick Haslam University of Melbourne, Department of Psychology, Parkville VIC 3010, Australia. [email protected] http://www.psych.unimelb.edu.au/people/staff/HaslamN.html

Commentary/Cramer et al.: Comorbidity: A network perspective Abstract: The network approach to psychiatric phenomena has the potential to clarify and enhance psychiatric diagnosis and classification. However, its generally well-justified anti-essentialism views psychiatric disorders as invariably fuzzy and arbitrary, and overlooks the likelihood that the domain includes some latent categories. Network models misrepresent these categories, and fail to recognize that some comorbidity may represent valid co-occurrence of discrete conditions.

Cramer et al.’s network perspective is a welcome innovation in a field whose problems and practices are equally stubborn. As a methodological tool, network analysis could enhance psychiatric diagnosis by clarifying symptom centrality and improve classification by mapping the “symptom space” in ways that may reduce comorbidity. The network perspective also provides an appealing rationale for paying attention to symptoms as phenomena that matter in themselves rather than merely being superficial and causally impotent markers of reified “disease entities” (Hyman 2010). This ontological upgrading of symptoms would be popular with behavioral clinicians (Persons 1986), whose treatments directly target symptoms and who tend to find talk of “latent variables” a little beside the point. Indeed, I suspect that many clinicians would agree with the authors in comprehending mental disorders as constituted by their symptoms rather than revealed in them. Diagnostic labels are often treated as useful shorthands, rather than as natural kind terms. Although focusing attention on symptoms and their interrelations has scientific and clinical advantages, it is questionable to go further and rule out in principle the existence of latent psychopathologies. Cramer et al.’s elimination of latent variables goes too far, and as a result the authors’ account overreaches. There is no intrinsic incompatibility between network modelling and recognition of latent categories (Schweinberger & Snijders 2003), nor should there be any incompatibility between believing that some symptom clusters reflect latent classes and that symptoms within those clusters may have causal interrelationships. Measurement models may demand local independence (i.e., no association among symptoms after the latent variable is statistically controlled) and equal centrality of all symptoms, but these are limitations in their formalism, not a reason for dismissing the existence of latent variables in the domain of psychopathology. The authors present a starkly dichotomous view of disorders. On the one hand, there are medical disorders such as Down syndrome that have discrete boundaries, essence-like etiologies, defining features, and isolated symptom clusters. The authors recognize that such disorders are latent categories, and symptom networks will therefore represent them incompletely. On the other hand are psychiatric disorders, which have “necessarily fuzzy” boundaries, diffuse etiologies, and no defining features. Their symptom clusters form densely interconnected webs that can be separated into distinct disorders only by arbitrary division. This view can be challenged because the distinction between essentialized natural kinds and arbitrary symptom clusters is too polarized, and because psychiatric disorders are not uniformly of the latter type. Cramer et al. present psychiatric conditions as all alike in lacking sharp, non-arbitrary category boundaries. No doubt this is true of many disorders, which simply represent quantitative extremes on continuous dimensions. However, as I have argued elsewhere (Haslam 2002), mental disorders are structurally diverse. Many are differentiated from normality by an imposed and convention-based decision rule. Others have boundaries that are intrinsic rather than conventional, but those boundaries are themselves indistinct or fuzzy, representing gradations of abnormality. Still others are latent classes with discontinuous boundaries. The parameters of the category (e.g., its prevalence) are empirical matters that do not simply reflect where a Diagnostic and Statistical Manual of Mental Disorders (DSM) committee chose to set a diagnostic threshold. The evidence that some mental disorders are latent categories comes largely from research using taxometric methods, which Paul Meehl and colleagues (Meehl 1995) developed to distinguish between (latently) categorical and dimensional models

of psychiatric phenomena. The preponderance of taxometric findings support dimensional models of mental disorders, consistent with Cramer et al.’s view, but several latent categories have been found (e.g., autism, schizotypal personality; for a review, see Haslam 2003). It is implausible that these latent categories represent natural kinds in the essentialist sense – they may arise from threshold effects, complex interactions among multiple causal factors, and so on, rather than from a single underlying causal factor, or “specific etiology” – but they have non-arbitrary category boundaries and some kind of underlying causal process or mechanism that makes them coherent. The philosophical concept of “homeostatic property clusters” (Kornblith 1993), intended to strike a middle path between essentialism and conventionalism, is somewhat related. These clusters are “real divisions in the structure of the world” (Craver 2009, p. 577) that cohere not because they share an essence but through the operation of a similarity-generating mechanism. Demonstrating that some psychiatric disorders are taxonic in Meehl’s sense does not commit one to essentialism, but it does invalidate any claim that all disorders are arbitrary and all boundaries fuzzy. Taxometric research has direct relevance to questions of comorbidity (Meehl 2001; Waldman & Lilienfeld 2001). Comorbidity is meaningful as a concept only if the supposedly comorbid conditions are latent categories. Two disorders cannot truly cooccur in a person unless both are discrete and separable. If disorders are not taxa, then comorbidity merely represents overlap of symptoms from different diagnostic lists. Cramer et al. hold to this view of comorbidity, according to which it is primarily a nuisance to be eliminated by nosological revision. By excluding on principle the existence of latent psychiatric categories, they fail to acknowledge that some comorbidity may be real and meaningful. I share Cramer et al.’s belief that most current psychiatric diagnoses do not pick out natural categories and that essentialist and reifying accounts of them should be vigorously challenged. For this reason, and because network analysis is a promising tool for mapping symptom space and reining in the promiscuous comorbidity of current diagnostic practice, I applaud their work. However, I hold a more pluralistic view of psychiatric classification than they do, believing that some psychiatric conditions approximate real categories with non-arbitrary and non-fuzzy boundaries. For this reason, I question the ontological position that they adopt, according to which all psychiatric boundaries are fuzzy, all distinctions arbitrary, and all comorbidity spurious. It should be possible to reap the benefits of network analysis without committing to a position that the symptom level is the only one that is real, and that latent categories could not occur within psychiatry.

Network models of psychopathology and comorbidity: Philosophical and pragmatic considerations doi:10.1017/S0140525X10000610 S. Brian Hooda and Benjamin J. Lovettb a

Department of Philosophy and Religious Studies, University of West Florida, Pensacola, FL 32514; bDepartment of Psychology, Elmira College, Elmira, NY 14901. [email protected] [email protected] http://sites.google.com/site/sbrianhood http://faculty.elmira.edu/blovett

Abstract: Cramer et al.’s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective Cramer et al.’s account of comorbidity is a thought-provoking contribution to the methodological foundations of psychopathology research. While we agree in spirit with the authors’ motivations (e.g., addressing problems with latent variable approaches to comorbidity), we will point out several prima facie challenges for the network approach to comorbidity. Consider the following philosophical views concerning psychological disorders. Realism, the view suggested by latent variable models, represents psychological disorders as the unobservable causes of symptoms. On this view, as Cramer et al. note, disorders exist independently of our measurement and diagnostic practices. They are detected through measurement. Conventionalist approaches, on the other hand, represent psychological disorders as artifacts of our measurement practices. For example, operationism collapses the distinction between disorders and their symptoms: the symptoms are constitutive of the disorders. Operationists define a disorder such as generalized anxiety disorder (GAD) as nothing more than the satisfying of a certain set of diagnostic criteria. As Cramer et al. note, the differences between these positions can have practical consequences such as different prevalence rates for a disorder. Cramer et al. have sought to carve out a third position with respect to comorbidity and psychological disorders. On their view, disorders are “clusters” of symptoms that systematically covary. Hence, it seems that on the network view, disorders do not exist independently of their symptoms. Disorders are aggregations of symptoms; this is very close to operationism. One logical consequence of this account is that psychological disorders cannot meaningfully be appealed to in causal explanations of pathological behavior. If GAD is just a specific cluster of symptoms, explaining why a patient manifests those symptoms by citing GAD would be circular. Cramer et al. note that the network view agrees with many clinicians’ conceptualizations of psychological disorders; however, there seem to be noteworthy ways in which the network view conflicts with clinical practice. Specifically, this view seems not to leave room for special cases, where clinicians would say that the psychiatric disorder is “latent” or “silent” (Lovett & Hood, in press). For instance, say that someone with schizophrenia experiences a lengthy period (several months) without symptoms. Does this person not have schizophrenia during that time? It is consistent with the latent variable approach to say that, for a time, the schizophrenia was not producing symptoms, but the network model does not seem to allow a representation of such a statement. To take a similar case, say that a child diagnosed with attention deficit hyperactivity disorder (ADHD) is placed in an environment where the behavioral symptoms are not permitted to be expressed; on the network view, has the ADHD been cured? There are additional concerns about the practice of diagnosis itself under the network approach. Individuals are diagnosed with psychological disorders; however, symptom clusters in Cramer et al.’s models are determined by population-level data. It is well established that structures implied by covariation across individuals may not correctly represent cognitive structures in individuals (Borsboom 2005; Borsboom et al. 2009b; Hamaker et al. 2007; Molenaar 2004; Molenaar et al. 2003). This is the problem of local homogeneity, and it is a serious conceptual obstacle for realism with respect to latent variable models generally and psychometric models of intelligence, personality, and psychopathology in particular. As Cramer et al. note, this is a challenge for the network approach as well. They propose addressing it through the analysis of intra-individual time-series data (target article, sect. 8, para. 5), but there are conceptual and practical limitations to this approach. First, if intra-individual structures of covariation in symptoms vary across people, then each patient may have his or her own kinds of disorders, even if multiple patients share the same symptoms. In other words, in such a case, it would not be clear what the basis would be for saying two individuals have the same disorder. Second, suppose that this problem can be overcome and we have some

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criteria for issuing a diagnosis of, for example, delusional disorder. Cramer et al. would suggest that we diagnose a person as delusional on the basis of intra-individual time-series data. But such a diagnosis is then made relative to the patient’s prior level of symptoms, and a patient who has long been consistently delusional would seem to be asymptomatic. Indeed, if a person with psychiatric symptoms seeks mental health services, and the mental health professional wishes to make a diagnosis, how long must the person’s case be followed to note covariation in his or her different symptoms over time before a person-specific network structure can be developed and a disorder (defined as a cluster of symptoms) can be diagnosed? These problems should be judged relative to the benefits of adopting a network approach. Certainly, the network approach can account for the relationships between symptoms that are not a product of common causes (i.e., violations of local independence), and it leads to an intuitive conception of disorders as having fuzzy boundaries. These virtues of the network approach are important conceptual benefits. However, the practical benefits claimed by Cramer et al. are less certain. For instance, Cramer et al. claim that symptom-symptom causal relationships are often the focus of clinical intervention. However, their examples on this point are not actually symptom-symptom relationships (unless “seeing a feared object is itself” a psychiatric symptom, as is “believing that one has not finished a list of tasks”); and in clinical practice, the symptoms themselves (and the consequent impairment in everyday life) are the focus of intervention rather than symptom-symptom relationships. The challenges we raise here notwithstanding, Cramer et al.’s contribution is ambitious, and their proposal certainly warrants further consideration. We especially like how the authors endeavor to engage a wide audience of methodologists, clinical psychologists, and philosophers of science. In this they show the interdisciplinary relevance of comorbidity and the need to enlist the efforts of diverse specialists in addressing the nature of psychopathology and comorbidity.

Is there a contradiction between the network and latent variable perspectives? doi:10.1017/S0140525X10000786 Stephen M. Humphry and Joshua A. McGrane Graduate School of Education, University of Western Australia, M428, Crawley, WA 6009, Australia. [email protected] [email protected] http://www.education.uwa.edu.au/ppl

Abstract: First, we question whether Cramer et al.’s proposed network model can provide a viable scientific foundation for investigating comorbidity without invoking latent variables in some form. Second, the authors’ claim that the network perspective is radically different from a latent variable perspective rests upon an undemonstrated premise. Without being demonstrated, we think the premise is potentially misleading.

Cramer et al. argue that we have been looking at mental disorders through the wrong psychometric lenses, and that we need a general psychometric theory of disorders as networks. Their article is valuable in as much as it raises the possibility of connecting psychometric theory to network models. However, we focus on two points of contention. Before doing so, we stress agreement on an overarching point: There is insufficient evidence that any mental disorder is a single continuous latent variable. As stated by Borsboom et al. (2004, p. 1063), the “ontological position that the attribute being measured exists and affects the outcome of the measurement procedure” is a strong scientific claim that is difficult, but necessary, to establish (see Michell 1999).

Commentary/Cramer et al.: Comorbidity: A network perspective Having said that, our first point of contention is this: We do not think the authors convincingly argue that the network perspective “nullifies the need to invoke latent variables as an explanation of the covariance between symptoms” (sect. 1, para. 5). The authors recognize that “non-symptom causal processes” like homeostasis may mediate and “partly explain” relations between symptoms (sect. 2, para. 9). However, a network model needs to explicitly incorporate such causal mechanisms to form the basis of a scientific theory, and this would make causal mechanisms fundamental, not incidental. Thus, Holland (1998, p. 132) refers to mechanisms and their defining transition functions as the primitives of a network. In Kauffman’s (1993) reaction network model of autocatalytic sets, for example, nodes may represent molecules, and edges may represent reactions: chemical reactions are the causal relations in the network. Without causal mechanisms, it is not clear how the network presented in the target article is anything more than a “method to visualize” sets of symptoms defined by the statistical associations in the empirical data (sect. 3). It would seem that, as soon as one invokes causal mechanisms, one must invoke latent variables like psychological and physiological states. Thus, we question whether the network perspective nullifies the need to invoke latent variables, in some form, to explain covariation among symptoms. Our second point of contention is that Cramer et al. have not formally demonstrated the technical premise for claiming their network perspective is a radically different conceptualization to the latent variable perspective. The premise is that “the presence of direct causal relations between symptoms contradicts the essential assumptions that underlie psychology’s main class of measurement models” (sect. 1, para. 5). This premise is asserted in the target article and references, but not formally demonstrated. Our concern is not whether the conceptualization is radically different. We are concerned that, without proper demonstration, the premise is potentially misleading. Briefly, on a more basic issue, we think it worth making more explicit that latent variable models in Item Response Theory (IRT) require that inter-individual covariation between levels of a trait and item scores is “entirely attributable to the [latent variable]” (sect. 2, para. 3). We assume, then, that the authors’ premise pertains specifically to intra-individual processes, given that in a source cited in the article, Borsboom (2008, p. 1101) questions whether symptoms are effects of a common cause “at the level of the individual person.” In asserting their premise, the authors focus specifically on the assumption of local independence. Borsboom (2008) uses the analogy of temperature to explain local independence, likening the reading of thermometers to indicators of latent variables. For a fixed temperature, there is no covariation among the readings. Borsboom (2008, p. 1099) says “the same implication exists in latent variable models, where this property is called local independence (‘local’ in the sense that one position on the attribute is considered at a time, and ‘independence’ because the indicators are statistically independent in the subpopulation of people who occupy this position).” As Borsboom explicitly states, this is an implication of local independence: See Lord and Novick (1968, p. 361) for a formal definition. Expressed in these terms, the implication is that for a subpopulation at a single position there should be no inter-individual covariation among item responses. However, to argue that this lack of covariation precludes causality would be to risk arguing by selective observation. The reason is that IRT models require that an individual’s responses to items are correlated with the locations of the items on the latent variable. One of the pioneers of IRT, Rasch, explained this implication of his dichotomous IRT model by showing there were positive correlations between item estimates and the log-odds of correct responses to the items, for groups of respondents with similar raw scores (see Rasch 1960/ 1980, Figure 7, p. 89). He treated the raw score groups as relatively homogeneous with respect to the latent variable. Thus,

even at the level of the individual, local independence does not preclude covariation, attributable to a latent variable, among responses. One cannot, therefore, argue that local independence precludes causation at the level of the individual on the basis that no such covariation may exist. Whether there is a plausible scientific basis for proposing that one or more latent variables cause the relations among indicators is a separate matter, which is justifiably raised in the target article and references. However, it would be unfortunate if readers are led to believe that local independence necessarily means one cannot apply existing psychometric theory if it is posited that intra-individual relations among symptoms are due to a latent variable. Moreover, a body of work in IRT focuses on polytomous models for items with explicitly dependent categories, such as categories that form a rating scale. These models accommodate direct dependence between the response categories that would otherwise violate the assumption of local independence (Andrich 1985; 2005; Verhelst & Verstralen 2008). It may be possible to develop methods to apply these models where there are direct causal relations among symptoms. In summary, first, we contest that the network perspective proposed by Cramer et al. nullifies the need to invoke latent variables. Second, we argue for the need to thoroughly examine whether existing models can be applied before calling for a new “general psychometric theory” (sect. 8, para. 9).

Network origins of anxiety and depression doi:10.1017/S0140525X10000622 Michael E. Hyland School of Psychology, University of Plymouth, Plymouth PL4 8AA, United Kingdom. [email protected] http://www.plymouth.ac.uk/pages/ dynamic.asp?page¼staffdetails&id¼mhyland

Abstract: Cramer et al. contrast two possible explanations for psychological symptoms: latent variables (i.e., specific cause) versus a network of causality between symptoms. There is a third explanation: The reason for comorbidity and the reported network structure of psychological symptoms is that the underlying biological cause is a psychoneuroimmunoendocrine information network which, when dysregulated, leads to several maladaptive psychological and somatic symptoms.

It is entirely plausible that symptoms should have a direct causal relationship with one another and this relationship has a network structure. It is implausible that symptoms arise only from mutual causality. They cannot arise out of thin air. This commentary addresses the origins of anxiety and depression. Modern Western medicine is based on an untested assumption of specificity: each disease has its own unique pathophysiology. The biological explanation of depression and anxiety is consistent with this assumption. A multi-billion dollar pharmaceutical industry stems from the hypothesis that depression results from low levels of serotonin, and anxiety from low levels of gammaaminobutyric acid (GABA). According to this hypothesis, the “latent variables” referred to by Cramer et al. are not really latent (i.e., hidden) – they are known neurotransmitters. If only life were that simple. Not only is the serotonin hypothesis widely disputed (Lacasse & Leo 2005), but drugs which enhance serotonin have a marginal effect on depression as the effectiveness of these drugs is largely placebo mediated (Kirsch 2009). If the unique neurotransmitter explanations for depression and anxiety are an over-simplification, what is causing these symptoms? According to infornet theory (Hyland, in press), the body is organised in two ways: (a) as a sequential processing system BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective that leads to specific pathologies and the diseases associated with those specific pathologies, and (b) as a parallel processing system. The parallel processing system extends through the neural, immune, and endocrine systems; it is a psychoneuroimmunoendocrine information network system, or infornet for short. The infornet carries out a number of functions, including managing the reference criteria of homeodynamic control systems. Reference criteria of homeostatic control systems remain fixed; those of homeodynamic systems vary and are responsive to both internal and external events. The infornet integrates information from external or psychological inputs with information from internal or biological inputs. Outputs from the infornet to the cortex generate mental states that modify behaviour and hence modify the external environment. For example, pain is a signal that leads to adaptive behaviour – such as, withdrawal of one’s hand from a flame. According to infornet theory, anxiety and depression evolved as signals to modify behaviour. Anxiety is a signal of behavioural alarm and prepares the body for external danger. Depression is a signal of behavioural inhibition and is the consequence of behavioural patterns that fail to achieve a person’s important goals (Hyland 1985). Depression helps disengage behaviour from unattainable or inappropriate goals (Carver & Scheier 1990). Anxiety and depression are therefore caused by outputs from a parallel processing system involving many different biochemicals, rather than being directly caused by a single biochemical. Because it encodes meaning, the infornet can be described in terms of the meaning it contains – by analogy, a computer programme can be described in terms of what it does rather than a binary magnetic code. The meaning of the infornet is represented by infornet beliefs. Infornet beliefs create the instructions that alter the reference criteria of homeodynamic control loops and which provide the mental signals that alter behaviour. The infornet beliefs that lead to anxiety can be characterised as “the external environment is dangerous.” The infornet beliefs that lead to depression can be characterised as “I am not achieving the goals I want to achieve” (Carver & Scheier 1990; Hyland 1987). Beliefs tend to be interconnected and mutually supporting. The infornet beliefs leading to depression and anxiety are interconnected via a network structure of linked beliefs. Both of these mood-altering infornet beliefs are part of a more general belief that “the external environment is unsatisfactory,” and this more general belief is part of a top-level belief that “the general situation is bad.” If the general situation is bad, then not only is there threat from the external environment but also threat to the internal environment (e.g., threat of infection or damage). Consequently, the top-level belief of “the general situation is bad” is also associated with beliefs that drive the inflammatory response system (Rosenkranz 2007; Segerstrom & Miller 2004). Infornet dysregulation occurs when, through the application of network learning rules that normally create better self-regulation, the infornet develops maladaptive beliefs – for example, that the general situation is always very bad when it is not. Chronic depression and anxiety (i.e., mental states which cannot be attributed to the immediate situation) are signals of a dysregulated infornet; that is, an inappropriate response to the current situation. Correlations between depression and anxiety, on the one hand, and between depression, anxiety, and inflammatory mediators, on the other, arise because all these variables are outputs of an information system whose beliefs are interconnected and where maladaptive beliefs tend to spread. Why do Cramer et al. observe a network structure in the relation to psychological symptomatology? My guess is that causality between symptoms plays a minor role. I suggest that the main the reason for the network structure of symptom comorbidity is because of an underlying biological network structure. That is, anxiety and depression are outputs from a dysregulated infornet that has multiple physiological and psychological outputs.

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Infornet theory predicts that fuzzy boundaries between disease states will not be limited to psychological symptoms but will include somatic symptoms. Diseases without a specific pathology, such as chronic fatigue syndrome (CFS) and irritable bowel syndrome (IBS), have a variety of psychological and somatic symptoms: diagnosis is not based on a unique pattern of symptoms. Comorbid relationships between fatigue, the somatic symptoms associated with CFS and IBS, along with depression and anxiety, should all form part of a network structure. In particular, inflammatory somatic symptoms should be related to psychological symptomatology (Whalley 2007). Dysregulated people have multiple and varied symptoms. Dysregulation varies continuously across the infornet’s meaning space. If diseases have a specific cause, then comorbidity is an inconvenience. Comorbidity is often a reason for exclusion in clinical trials. From the perspective of infornet theory, however, the study of comorbidity has the potential to provide unique insights into a hypothesised network system whose outputs are psychological symptoms along with physiological changes (typically, pro-inflammatory) that cause somatic symptoms.

The network perspective will help, but is comorbidity the question? doi:10.1017/S0140525X10000634 Wendy Johnsona,b and Lars Penkea a Centre for Cognitive Ageing and Cognitive Epidemiology, and Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom; b Department of Psychology, University of Minnesota –Twin Cities, Minneapolis, MN 55455-0213. [email protected] [email protected]

Abstract: Latent variable modeling has revealed important conundrums in the DSM classification system. We agree that the network perspective has potential to inspire new insights and resolve some of these conundrums. We note, however, that alone it cannot really help us understand etiology. Etiology, not comorbidity, is the fundamental question.

It has become popular in the last few years to model large sets of inter-correlated variables as networks. There are probably two reasons for this. First, and perhaps trivially, as was the case about 25 years ago with latent variable modeling, the statistical techniques and tools to do this network modeling tractably and readily are relatively recently available; that is, researchers now can. This is, however, not a priori a reason to take whatever it offers us as the best answer to our scientific questions (Gigerenzer 1991). But second, again as did latent variable modeling about 25 years ago, the network perspective offers exciting prospects for fresh understanding of dynamic systems ranging from power grids, to epidemic spread through a population, to the development of chronic disease such as diabetes in individuals. Psychopathology, with its common occurrence and multifaceted manifestations seems a particularly apt target for the network perspective, and we are pleased to see Cramer et al. take some concrete steps towards applying it. At this point, problems with the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) system of psychopathological diagnostic system are widely acknowledged. Despite Cramer et al.’s criticism of it, much of the credit for revealing those problems, perhaps especially comorbidity, should rest with latent variable modeling (e.g., Krueger et al. 2007). Latent variable modeling might also be credited with something else: revealing the tension between trying to use the same diagnostic system for purposes of systems of administration

Commentary/Cramer et al.: Comorbidity: A network perspective and treatment and for purposes of understanding development and etiology. The former takes place and requires description at the level of the population, whereas the latter takes place and requires description at the level of the individual. Researchers can count on latent variable modeling to tell us something about the former, but whatever it says may not apply to the latter (e.g., Cervone 2005; Molenaar 2004). As Cramer et al. note, the reason latent variable modeling may not tell us about development and etiology involves the assumption of local independence underlying it. From the latent variable perspective, pervasive comorbidity is the chronic symptom of this trouble. If two disorders share the same symptom, how can there be local independence of symptoms? And if two disorders share the same symptom, it should be no surprise to find comorbidity. But it is the strong causal attributions of latent variable models that carry with them the need for assumptions like local independence, not the structural equations that define the parameters to be estimated themselves. The appropriateness of latent variable models is assessed by how well those structural equations can replicate the data, not by any direct test of the appropriateness of the causal attributions. Completely different patterns of causal attribution can be described by the same sets of structural equations, and these different causal models will fit the data equivalently. For example, it is probably just as likely that depression emerges from a constellation of symptoms as it is that depression is the underlying latent cause of those symptoms, and the two models would fit the data identically well (Borsboom et al. 2003). If depression does emerge in this way from a constellation of symptoms, the co-emergence of some other disorder that shares those symptoms is no problem at all, as, for example, when obesity contributes to the emergence of both heart disease and diabetes. Thus, comorbidity is a problem not because of the structural models that have been used but because of the causal attributions associated with latent variables. Administration systems rely on accurate description and need not rely on causal understanding at all. In fact, many of the most effective treatment protocols to date have not relied on causal understanding. But ultimately understanding psychopathology will rely critically on understanding development and etiology. For that, researchers only get in trouble when they assume what they should be trying to test. Thus, the critical problem with latent variable models is not really the comorbidity they have helped to identify, but the causal attributions they entail. Cramer et al. have demonstrated that the network perspective offers potential to develop important insights into the patterns of association among DSM-IV symptoms, particularly through the possibilities it offers to include estimates of parameters that express the relative frequencies of nodes and the extent to which they are interconnected with other nodes. More importantly, even when the causal assumptions in latent variable modeling are relaxed, their structural models can be accurate descriptions at the level of the population, but may not be accurate at the level of the individual, which is of necessity also the level at which function must be understood. Network models cannot guarantee consistency between the intra- and inter-individual levels either, but they may be more likely to show it, though this remains to be tested. It might also turn out that a combination of transactional processes (as can be modeled in networks) and latent causal factors provides the best description of the development of some traits (Fraley & Roberts 2005). Perhaps most important of all, however, the use of new statistical tools such as network modeling frees us to think about etiology in new ways. Network models cannot tell us directly about causation either, especially if we are not even sure that we have the optimal symptom designations for the disorders, as Cramer et al. hint and we would emphasize. Nor can they help us fix the DSM if we do not have the optimal symptom designations. But they can open our minds to new ideas about etiology that can be tested in

other ways, especially if we go beyond the basic cross-sectional data Cramer et al. have used for illustration here. And that’s what we need to be thinking about.

Toward scientifically useful quantitative models of psychopathology: The importance of a comparative approach doi:10.1017/S0140525X10000646 Robert F. Krueger,a Colin G. DeYoung,b and Kristian E. Markonc a

Department of Psychology, Washington University, St. Louis, MO 631304899; bDepartment of Psychology, University of Minnesota –Twin Cities, Minneapolis, MN 55455-0344; cDepartment of Psychology, University of Iowa, Iowa City, IA 52242. [email protected] [email protected] [email protected]

Abstract: Cramer et al. articulate a novel perspective on comorbidity. However, their network models must be compared with more parsimonious latent variable models before conclusions can be drawn about network models as plausible accounts of comorbidity. Latent variable models have proven generative in studying psychopathology and its external correlates, and we doubt network models will prove as useful for psychopathology research.

In the target article, Cramer et al. offer a novel psychometric perspective on symptoms of major depression, generalized anxiety disorder, and patterns of co-occurrence among these symptoms. The study of human individual differences has always benefited from its close relationship with psychometric theory and models, and the application of psychometric models in the study of psychopathology provides a much-needed remedy to assuming that the constructs delineated in the DSM-IV-TR (Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text Revision; American Psychiatric Association 2000) are valid by fiat (Krueger & Markon 2006a). The ultimate utility of the work of Cramer et al. in understanding and ameliorating psychopathology, however, hinges on evaluating the comparative construct validity of their approach. Network models are only one type of model, and Cramer et al. do not compare the fit of their models with the fit of other plausible models. Importantly, Cramer et al. do not directly compare network and latent variable models. Indeed, these comparisons may be moot in some cases, as network and latent variable models may be more similar than Cramer et al. suggest. We expand on these points here. Comparative construct validity of quantitative models of psychopathology. A fundamental limitation of the work

presented by Cramer et al. pertains to their selective approach to model fitting. They present the results of fitting network models, but not the results of fitting other psychometric models. Their strategy is instead to argue on an a priori basis that other models (e.g., latent variable models) should not be considered as potential models. This strategy is obviously problematic. The selection of a quantitative model is an empirical matter, not a matter that can be decided a priori. As Cramer et al. point out (e.g., in their use of the Akaike Information Criterion [AIC] as an index of fit; sect. 2, para. 8), a desirable model can be thought of as balancing at least two properties: (1) the ability to reproduce the observed data and (2) the efficiency or parsimony with which the model can achieve the first property, more efficient models having greater scientific utility if they are equivalent in their ability to reproduce the observed data (Markon & Krueger 2004). With regard to model fit, the network models proposed by Cramer et al. are unlikely to emerge as optimal models when BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective compared with latent variable models. This is because the network models fit by Cramer et al. contain a multitude of parameters; they are lacking in parsimony when compared with latent variable models. Models lacking in parsimony often provide a poor relative fit to data and are thereby lower in scientific utility because they amount to little more than re-expressions of observed data (Barron & Cover 1991). Because they capitalize on chance, they also tend to generalize poorly to new samples of measures or persons. Heavily parameterized models lack the ability to articulate organizing scientific constructs that have proven indispensable in building theories and evaluating the correspondence between theories and data. Consider, for example, the model shown in Cramer et al.’s Figure 4, portraying network-model derived connections among the symptoms of major depression and generalized anxiety disorder. Cramer et al. attempt to argue that the multiplicity of parameters portrayed in Figure 4 (the size of the nodes, the darkness of the circumferences, the thickness of the edges, and the darkness of the edges) provides valuable information in focusing scientific inquiry aimed at understanding and ameliorating depression and anxiety. However, the fit of this highly complex model is never compared with other models that articulate organizing scientific constructs (i.e., latent variable models). By virtue of their greater parsimony and articulation of organizing constructs (e.g., the latent construct of neuroticism as the nexus of anxiety and depression; Griffith et al., in press), latent variable models point us toward key targets for scientific inquiry. Such guidance is obviously critical in pursuing research aimed at reducing the public health burden of mental disorder (Lahey 2009). Put simply, how would one use the information in Figure 4 to explain to a policy maker how we might go about spending public funds wisely in the service of working to ameliorate the burden of depression and anxiety? By funding hundreds of separate projects focused on understanding each line in the figure? We doubt such a conversation would prove generative, or that scientific inquiry framed by Figure 4 would prove enlightening. We would also encourage the reader to contemplate how complicated Figure 4 would look if a more comprehensive set of psychopathological symptoms (e.g., symptoms of other mood and anxiety disorders) were modeled along with the specific symptoms that were the focus of Cramer et al.’s efforts. Although comparative models of internal structure are an important step in model development, a next step is to evaluate the ability of a model to explain external constructs. Space considerations prevent us from describing the extensive literature on the construct validity of latent variable models of psychopathology at length. We can provide only a few examples but encourage the reader to consult the citations we give for more details. As examples, latent variable models of psychopathology can account for phenomena such as gender differences in the prevalence of specific syndromes (Kramer et al. 2008), the genetic and environmental effects that both connect and distinguish specific syndromes (Kendler et al. 2003), and the generality and specificity of biobehavioral correlates of psychopathology (Patrick et al. 2006). Ultimately, some comparisons between network models and latent variable models may be moot, because the two frameworks are more similar than Cramer et al. suggest and may be identical in many cases. Importantly, network models of the sort espoused by Cramer et al. would be latent variable models if they included multiple measures of each symptom, rendering each of their nodes a latent variable. Thus, latent variable models are not only more parsimonious, but also more comprehensive than the models discussed by Cramer et al. Moreover, some sophisticated network models have proposed constructs very similar to latent variables (Kemp & Tenenbaum 2008). Theoretical discussions about the relative merits of latent variable versus network models are fundamentally misleading. The important questions are what models to compare empirically, how to make those comparisons, and what those empirical comparisons reveal.

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Questions about networks, measurement, and causation doi:10.1017/S0140525X10000658 Keith A. Markus Psychology Department, John Jay College of Criminal Justice, City University of New York (CUNY), New York, NY 10019. [email protected] http://web.jjay.cuny.edu/kmarkus

Abstract: Cramer et al. present a thoughtful application of network analysis to symptoms, but certain questions remain open. These questions involve the intended causal interpretation, the critique of latent variables, individual variation in causal networks, Borsboom’s idea of networks as measurement models, and how well the data support the stability of the network results.

I wish to congratulate Cramer et al. for writing an audacious article that offers much food for thought. Among other things, the target article provides a welcome expansion on ideas presented by Borsboom (2008). I focus on areas that could use further fleshing out and on psychometric and methodological issues, leaving clinical issues for others with expertise in that area. The causal interpretation of the network models puzzles me most. Cramer et al. state a tentative causal interpretation as: “the stronger the association between symptoms, the more likely that one symptom will lead to another” (sect. 6, para. 11); but the article does not develop the causal interpretation and instead focuses on modeling symmetric associations. If the authors intend a symmetric notion of causation, this seems like a very strong restriction on the models that rules out cases such as Borsboom’s (2008) chain model in which panic attacks cause concern, but not vice versa. Asymmetric causation brings us back to something akin to a path diagram, although the positing of nonlinear causal connections by using a threshold parameter would add an interesting additional element to the causal model. However, even asymmetric bidirectional causal models face conceptual hurdles (Rozeboom 2009). Certainly one needs to conceptually distinguish causal parameters from associations (McDonald 2002), even if one then intends to argue in favor of equating them. A good deal of work remains to clarify various possible causal interpretations and evaluate their relative merits. A second aspect of the article that I found difficult involved the critique of latent variable models. Cramer et al. make a reasonable case against a single common factor for all symptoms listed for a disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM). However, a critical latent variable modeler would inspect the symptom correlation matrix showing the same patterns of association described in the network, warning against a single common factor, or catch the problems when assessing model fit. As a result, much of the critique of latent variable models seems tilted in favor of networks by making a mismatched comparison of a proper network model against an improper latent variable model. Perhaps improper use of latent variable models occurs in the diagnostic literature, but then the criticism applies to the misuse of such models, not the models themselves. The advocacy against latent variable models risks becoming an advocacy in favor of analyzing causal relationships between observed variables rather than latent variables, and this holds several potential pitfalls. One motivation for using latent variable models involves the ability to remove the attenuating effects of measurement error from the estimates of causal relationships. Replacing latent variable models with causal networks of observed variables negates this advantage. As a thought experiment, imagine modeling a network of variables measured without any error. Now imagine a knob that allows you to gradually turn up the amount of random measurement error mixed into the observed variables. As you turn the knob, you can expect the estimated causal connections between the nodes to decrease, eventually to the point that alternative networks become increasingly indistinguishable based on the observed data.

Commentary/Cramer et al.: Comorbidity: A network perspective An alternative would involve developing multiple measures of each symptom. One could then treat each symptom as a latent variable with its own common factor model, and model the causal relationships between symptoms as relationships between these latent variables (Bollen 1989). This would allow the research to both control for measurement error and model direct causal relationships between the symptoms. As such, it remains unclear that shifting the focus to causal relationships between symptoms requires giving up measurement models and their advantages, but these two issues seem conflated in Cramer et al.’s article. I see no motivation for encouraging a return to observed regularities as the primary object of scientific explanation. Cramer et al.’s comments regarding different causal networks for different individuals seem to cut against the presented network analyses and in favor of a return to some kind of latent variable model. Latent class analysis, or latent mixture modeling, provides a means of identifying stable subclasses of individuals sharing the same causal structure. Cramer et al. sensibly suggest modeling individual differences on the network parameters, allowing each individual his or her own causal structure. However, if clusters of individuals share the same causal structure, the latent mixture would provide a more parsimonious model than one that states that everybody differs from everybody else. Borsboom (2008) suggested understanding symptoms as parts of disorders but took this as an alternative measurement model. Cramer et al. definitively reject the idea that symptoms measure disorders. I did not find the motivation for this shift clear from Cramer et al.’s presentation and would like to see further work fleshing out Borsboom’s original idea that symptoms can both constitute and measure a disorder, or at least that symptom severity can measure disorder severity despite this constitutive part-whole relationship. A final minor point involves the use of random split-sample cross-validation to support conclusions regarding the stability of the results of the network analyses. It seems unsurprising that a random split of such a large sample would produce the same results in each half, but it remains unclear why this provides support for the stability of the results. Resolving the issue requires greater clarity regarding the intended notion of stability. However, a more informative approach might instead make use of the methods outlined by Shadish et al. (2002) for systematically examining the consistency of the results across various possible moderating variables available in the data set. Such non-random stratification of the sample might provide a much more informative and stringent assessment of the stability of the results. Again, I wish to congratulate Cramer et al. for having written such a far-reaching article. I hope that my comments can play a constructive role in moving the research program forward and look forward to further developments.

Symptoms as latent variables doi:10.1017/S0140525X1000066X Dennis J. McFarlanda and Loretta S. Maltab a Laboratory of Neural Injury and Repair, Wadsworth Center, New York State Department of Health, Albany, NY 12201; bStratton VA Medical Center, Albany, NY 12208. [email protected] [email protected]

Abstract: In the target article, Cramer et al. suggest that diagnostic classification is improved by modeling the relationship between manifest variables (i.e., symptoms) rather than modeling unobservable latent variables (i.e., diagnostic categories such as Generalized Anxiety Disorder). This commentary discusses whether symptoms represent manifest or latent variables and the implications of this distinction for diagnosis and treatment.

Cramer et al. model behavioral disorders purportedly using manifest variables (symptoms) rather than latent constructs (diagnostic categories). We challenge the assumption that a symptom is a manifest variable, and use the symptom of sleep disturbance as an example because the target article authors have used this as an exemplar of a symptom shared by several disorders (cf. Diagnostic and Statistical Manual of Mental Disorders (DSM-IV); American Psychiatric Association [APA] 1994). It might seem that a symptom such as sleep disturbance is an empirical observation rather than a theoretical abstraction like depression; but sleep disturbance is itself an abstraction that encompasses multiple, specific instances of sleep loss: delayed sleep onset, mid-sleep and early morning awakening, restless sleep, and disturbed circadian rhythms and sleep stages (APA 1994). To say that someone has a sleep disturbance is to assert that they are disposed to have multiple instances of sleep loss in one or more of these domains. Conceptualizing symptoms as manifest or latent variables has implications for assessment. The nodes in Cramer et al.’s network were categorically defined symptoms assessed with the Composite International Diagnostic Interview (CIDI) (Kessler & Uslum 2004; Kessler et al. 2005b), a structured clinical interview in which symptoms are assessed in terms of presence/ absence rather than with a dimensional score. Clinical interview is one among several methods of assessing sleep disturbance, including questionnaires, diaries, polysomnography (PSG), and laboratory observations, each of which differs in their reliability and validity (Crocker & Algina 1986). Reliability considers the similarity between observed and true scores. It is necessary to make this distinction if one assumes that the same phenomenon is being measured during multiple observations. In the case of validity, a distinction is made between what is being measured and the instrument that is used to measure it. For example, we might assume that an interview and PSG measure the construct of sleep disturbance, though they might differ in their validity (i.e., the extent to which they are influenced by other factors). Moreover, there is an implicit understanding that the true sleep disturbance score represents a latent variable composed of the observations (manifest variables) of each testing occasion. Hence, the categorical sleep disturbance symptom assessed by the CIDI is assumed to be an abstraction of manifest (observed) instances of sleep loss. Conceptualizing symptoms as latent constructs has implications for diagnostic validity. What is the nature of the categorically defined symptom of sleep disturbance as a unit of analysis in diagnostic classification and differential diagnoses of mood and anxiety disorders? The determination that sleep disturbance is a symptom of a mood or anxiety disorder assumes the following: the sleep loss is frequent, substantial, persistent, unintentional; not due to the presence of a medical condition or a substance; and not due to extrinsic (environmental) factors (APA 1994). Insomnia complaints are common in the population, with a one-year prevalence of 30 – 40% (APA 1994). Hence, ascertaining the frequency, intensity, and persistence of the sleep disturbance is necessary to assess whether it is severe enough to be considered abnormal. Intentional sleep loss (e.g., a student who remains awake all night studying for exams), or sleep loss due to a medical condition, use of substances, disrupted circadian rhythms (e.g., jet lag), or due to environmental factors (loud neighbors), would not be diagnosed as a symptom of a mood or anxiety disorder (APA 1994). We therefore contend that sleep disturbance as a symptom of a mood or anxiety disorder is a latent construct that encompasses manifest observations of unintentional, frequent, substantial, and persistent sleep loss not due to the aforementioned extrinsic factors, and that these manifest variables must be assessed in order to determine whether the sleep disturbance does in fact qualify as a bona fide symptom of a mood or anxiety disorder. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective Consider the symptom of sleep disturbance in posttraumatic stress disorder (PTSD). Though this PTSD symptom has many features in common with primary insomnia (e.g., Inman et al. 1990), sleep disturbance in PTSD can be discriminated from that in primary insomnia according to differences in REM-sleep abnormalities (e.g., Inman et al. 1990; Mellman et al. 2002) and frequency of sleep disturbance due to nightmares (Mellman & Pigeon 2005), due to fear of sleep and of the dark (Inman et al. 1990), or due to nocturnal hypervigilance (e.g., lying awake listening for strange sounds) (Deviva et al. 2005). Hence, although the construct of sleep disturbance is found in several behavioral disorders (APA 1994), the observed manifestations of sleep disturbance in PTSD can be discriminated from those associated with other behavioral health conditions. Analysis of sleep disturbance as a latent construct reflecting manifest observations such as, for example, frequency of delayed onset due to fear of nightmares could reduce diagnostic overlap and enhance diagnostic validity. Clinical scientists have also recognized the distinction between sleep disturbances in PTSD and those found in other disorders by developing interventions for PTSD patients that specifically target nightmares and nocturnal hypervigilance (Deviva et al. 2005). Hence, recognizing that a symptom reflects a latent construct is not an abstract, semantic issue. Regardless of whether one adopts network or latent construct models of behavioral disorders, a clear understanding of how we define and measure behavioral phenomena, and of the nature of the units of analyses we employ to characterize disorders, has nontrivial implications for determining the best course of treatment, which is the ultimate goal of developing accurate diagnostic methods. Treating symptoms as proxies for manifest observations leads one to think of them as empirical observations rather than as theoretical constructs. However, assessment, diagnosis, and treatment of behavioral disorders are better served by careful consideration of the basis on which symptoms are identified and measured.

Latent variable models are network models doi:10.1017/S0140525X10000798 Peter C. M. Molenaar Department of Human Development and Family Studies, College of Health and Human Development, Pennsylvania State University, University Park, PA 16802. [email protected] http://www.hhdev.psu.edu/hdfs/faculty/molenaar.html

Abstract: Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

All models with common latent variables (factors) can be transformed into equivalent network models without common latent variables. This was proven in Molenaar (2003); Molenaar et al. (2007) apply this transformation to the 1-factor model. The networks that are equivalent to latent variable models are directed graphs connecting observed variables and residuals. In contrast, the networks in the target article are undirected graphs connecting observed variables (symptoms). Notwithstanding these differences in detail, the fact that any latent variable model can be transformed into an equivalent network model (where equivalent means the same number of free parameters and goodness of fit to the data) implies that latent variable models can be conceived of as constituting a subset of the set of network models. This, of course, qualifies any presumed contrast between interpretations in terms of networks and latent variables.

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The transformation of models with common latent variables into equivalent network models without common latent variables is akin to the transformation of linear state space models into equivalent transfer function models, which is standard practice in computational engineering and signal analysis. A typical state space model is composed of two sub-models: a measurement model linking the observed process to the latent state process, and a dynamic model describing the evolution of the latent state process. In contrast, a transfer function model consists of time-lagged relationships involving only the observed process and residuals. Again, it holds that state space models constitute a subset of the set of transfer function models. State space models are very popular in applied dynamic systems analysis. Dynamic systems theory is discussed in the concluding section of the target article. Linear state space models are formally equivalent to longitudinal factor models, the latter being typical instances of psychometric latent variable models. Hence, in so far as the authors see an important role for dynamic systems theory within a network perspective, there also should be a role for state space models and longitudinal factor models. The model equivalences sketched here are suggestive of a rather gradual relationship between network models and latent variable models. The networks presented in the target article are undirected graphs in which the edges reflect associations between observed variables. The analogue for multivariate Gaussian variables would be a covariance matrix depicted as a graph. Such graphs, often augmented by colorings conveying additional information, certainly constitute important tools to display observed relationships among a large number of observed variables. But displays of relationships among observed variables, whether in graphical network form or as arrays of covariances, constitute a low level of modeling. An intermediate level of modeling would include path analysis or transfer function modeling; that is, models involving networks of directed connections among observed variables and residuals. At this level the effects of measurement errors can be taken into account, which appear to be quite important in social-scientific measurements. An example is the generalization of the simplex model to the quasisimplex model. The highest level includes state space models and psychometric latent variable models. These models explain observed relationships in terms of common causes and measurement error and therefore are ideal for scientific theory formation. They are not as restricted as mentioned in the target article. For instance, measurement errors in state space models can be sequentially dependent and/or can depend upon the latent state process. But obviously they put the strongest demands on the data. Cramer et al. are correct in stating that not every set of observed variables can be explained in terms of a limited number of common causes, despite the heuristic and scientific value of such an explanation. For instance, with respect to the networks presented in the target article this would explain what the symptoms are symptoms of. Therefore, one would like to have available a set of adequate inductive tools to determine whether or not each symptom (node in a network) has its own etiology. Or, stated more generally, one would like to have adequate inductive tools to progress from the lowest level of modeling (displaying networks of relationships among observed variables) to higher levels.

Some mental disorders are based on networks, others on latent variables doi:10.1017/S0140525X10000671 Don Ross School of Economics, University of Cape Town, Private bag, Rondebosch 7701, Cape Town, South Africa. [email protected] http://uct.academia.edu/DonRoss

Commentary/Cramer et al.: Comorbidity: A network perspective Abstract: Cramer et al. persuasively conceptualize major depressive disorder (MDD) and generalized anxiety disorder (GAD) as network disorders, rejecting latent variable accounts. But how does their radical picture generalize across the suite of mental and personality disorders? Addictions are Axis I disorders that may be better characterized by latent variables. Their comorbidity relationships could be captured by inserting them as nodes in a super-network of Axis I conditions.

The network perspective on major depressive disorder (MDD) and generalized anxiety disorder (GAD) articulated by Cramer et al. in the target article captures a good deal of the scientific data on, and clinical experience with, those most ubiquitous and protean of Axis I disorders (cf. Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) for definitions of Axis I; American Psychiatric Association 1994). MDD and GAD are not only strongly comorbid with one another, but with a wide range of psychiatric afflictions. It is highly plausible that they designate the most common clusters in the network of co-occurring symptoms of serious mental distress generally. Equally worthy of further study and development is the authors’ suggestion that the causal relationships among these symptoms are typically directly mediated, in part, by external factors (perhaps including the communication of psychiatric diagnoses to patients, a potential “etiological node” [sect. 4, para. 5] that Cramer et al. don’t mention). It is this aspect of the network perspective that renders it a radical and welcome challenge to standard latent variable models. The authors are also importantly right in identifying the implicit commitments of such models with the psychometric methods used in their application, rather than with associated philosophical rhetoric. Cramer et al. are not explicit about the way in which they expect their radical picture to generalize across the suite of mental and personality disorders. We may distinguish two possible interpretations that are compatible with their remarks. On the one hand, it might be that the network model applies to most disorders directly, just as it does to MDD and GAD. On this interpretation, most disorders are clusters of nodes in a single super-network of psychiatric conditions. Alternatively, the network structure of MDD and GAD, along with the ubiquity of their constitutive symptoms, might explain comorbidities involving other Axis I and Axis II disorders which are themselves based on latent variables. Evidently, the second “hybrid” picture would be more complicated and inelegant. However, I believe that the limited current evidence runs more strongly in its favor. A major class of Axis I disorders that seems most likely to demand this hybrid conception is that of the addictions. As is distinctly not true of MDD or GAD, recent progress in understanding addiction has consisted mainly in progressive isolation and refinement of a neural pathways model of its etiology and maintenance (Everitt & Robbins 2005; Everitt et al. 2001; Goldstein & Volkow 2002; Koob 2006; Koob & LeMoal 2000). Summarizing very broadly, it seems to have been discovered that addiction arises through the dopamine reward circuit’s learning a rich but entrenched set of cues that activate representation of highly valued and strongly salient addictive targets and that prepare motor response to consume them, which, when frustrated, are experienced as cravings. A crucial step in the etiology of addiction appears to be adaptation of frontal and prefrontal serotonin and gamma-aminobutyric acid (GABA) circuits that weakens the strength of opponent processes to impulsive consumption. If learned reward system hysteresis and neuroadaptation that weakens cortical control are taken to be jointly necessary and sufficient conditions for addiction, then many people who consume addictive substances, or gamble, to problematic levels are probably not addicted. “Problem drinking” and “problem gambling” are very plausibly behavioral syndromes with network structures. However, what would precisely distinguish true addicts, according to the perspective being suggested, are specific ranges of values for neural processing variables in the anterior cingulate,

ventral tegmental area (VTA), the ventral striatum including nucleus accumbens, and the orbitofrontal cortex (OFC) and dorsolateral prefrontal cortex (PFC). Preliminary evidence for this view comes from the first taxometric analyses of mixed populations of substance-abusing and substance-dependent people. Goedeker and Tiffany (2008) find strong convergence among three taxometric methods in identifying a taxon of heavy daily cigarette smokers, who also meet traditional clinical criteria for dependence, that excludes the large group of less regular nicotine users. That is, nicotine dependence does not appear to have a dimensional structure, in strong contrast to the findings of the same taxometric approaches as applied to analogue depression (Ruscio & Ruscio 2002) and attention-deficit hyperactivity disorder (ADHD) (Haslam et al. 2006). A disorder’s being characterized by a network model does not entail that, under the guise of a latent variable perspective, its structure should emerge as dimensional. Nor does the inverse dependence hold: from the fact that a disorder’s latent structure suggests a taxon one cannot infer rejection of a network model. However, Cramer et al. identify the view opposed to the network perspective as “essentialism” (sect. 6, para. 7), which in the case of mental disorders is the idea that people can be separated into disorder and no disorder classes on the basis of presence or absence of specific “defining features” (sect. 6, para. 1). In the case of addiction, such defining features which are not symptoms have been proposed on the basis of neuroscientific evidence; in this context, confirmation of taxonic structure buttresses the hypothesis that the features in question are indeed defining – that is, that addiction resembles Down’s syndrome, Cramer et al.’s example of a medical condition that is not based on a network. It might thus be suggested that Cramer et al. overstate what the evidence will bear when they say baldly that “this line of reasoning [applied to Down’s syndrome] is unlikely to hold for mental disorders” (sect. 6, para. 2). On the other hand, the issue might be thought to turn partly on semantics: someone might want to maintain that if addiction is a distinctive, identifiable pathology of the VTA-to-PFC/OFC circuit, then it is not really a mental disorder after all. Addictions are highly comorbid with other Axis I and Axis II disorders, including MDD, ADHD, bipolar spectrum disorder, and antisocial personality disorder. A possible way of representing this is to conceive of addictions, themselves modeled using latent variable structures, as nodes in the overarching network of mental disorders. On this representation, however, comorbidities among addictions would not be best modeled by network analysis. Comorbidity among addictions might be grounded etiologically in a genetic predisposition, and manifestly in the common attenuation of cortical control circuits in addicts. Cramer et al. have supplied a promising and liberating way of thinking about psychiatric comorbidity. They likely would not want to be taken as suggesting that it is the whole story.

Comorbidity: The case of developmental psychopathology doi:10.1017/S0140525X10000683 Aribert Rothenberger,a Tobias Banaschewski,b Andreas Becker,a and Veit Roessnerc a

Child and Adolescent Psychiatry, University of Go¨ttingen, 37075 Go¨ttingen, Germany; bChild and Adolescent Psychiatry, Mannheim Central Institute of Mental Health, J5, 68159 Mannheim, Germany; cChild and Adolescent Psychiatry, University of Dresden, 01187 Dresden, Germany. [email protected] [email protected] [email protected] [email protected]

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Commentary/Cramer et al.: Comorbidity: A network perspective Abstract: In developmental psychopathology, differentiating between the coexistence and the clinical entity of two problem areas is of utmost importance. So far, logistic regression analysis has already provided helpful answers, as shown in studies on comorbidity of tic disorders. While the concept of bridging symptoms may be investigated adequately by both logistic regression and the network approach, the former (latent variable) seems to be of advantage with regard to the problems of multiple comorbidities and development.

In this commentary, we use comorbidity to refer to the co-occurrence of two diagnoses at the same time for a single patient, independently of etiological and/or pathway considerations (see Banaschewski et al. 2007). In children’s mental disorders, comorbidity plays an even greater role than in adults: about 80% of children develop at least one comorbid condition compared with 45% in adults (Cramer et al., target article; Freeeman et al. 2007; Gillberg et al. 2004). However, parents and physicians prefer to restrict the necessary multimodal treatment regime to only one disorder (i.e., a clinical entity). Therefore, in children it is highly important to determine whether or not the coexistence of two mental disorders represents a separate clinical entity (i.e., true comorbidity) with the possibility of a specific treatment (Banaschewski et al. 2007; Cramer et al., target article). To approach this issue, the concept of “overlapping” or “bridging” psychopathology between two problem areas comes into play. For example, the coexistence of tic disorders (TDs) and attention-deficit hyperactivity disorder (ADHD) presents an important practical problem for diagnosis and treatment in child psychiatry (Rothenberger et al. 2007). First, some direct overlap exists between symptoms of hyperactivity, impulsivity, and inattention, and, second, there is some indirect overlap because TD and ADHD are both associated with the same disorders (e.g., Asperger Syndrome, anxiety, depression, obsessivecompulsive disorder [OCD]). Thus, it is of practical importance to disentangle which symptom dimensions are actually overlapping and which ones are distinct between TD and ADHD. And does an overlap of symptoms indicate that TD þ ADHD represents a separate clinical entity? In order to answer these questions, we think that a 22 factorial design, analyzing “pure” groups and their combination (i.e., without further comorbid conditions like anxiety or depression) is more appropriate and practically relevant than a network approach, because the factorial design creates a clearer and clinically adaptable picture for guiding treatment. So far, several studies using this approach have shown that TD þ ADHD is not a clinical entity and that its comorbidity needs to be explained within the framework of an additive model (Rothenberger et al. 2007). Using the Child Behavior Checklist as a psychopathological screener, both TD and ADHD showed similar scores for the anxiety/depression, schizoid/obsessive, and social withdrawal scales, whereas only ADHD reached statistical main effects for aggression, delinquent behavior, and attention and social problems (Roessner et al. 2007b). These results also clarify that internalizing problems should not be neglected in TD þ ADHD, nor in primarily externalizing disorders like ADHD (see also, Sobanski et al., in press). Further, the results underline that boundaries between both mental disorders are partly fuzzy while also displaying distinct features, a conclusion which, methodologically, might also be reached by using the network approach. Applying both approaches to the same data set could be helpful in order to detect their scientific strengths and weaknesses. At least, both statistical approaches stress that bridging symptoms, the correlation of latent variables and interacting networks, must be carefully controlled for in clinical trials since they are possible confounders of mental disorders. Further, relations between symptoms may vary according to a patient’s age, his/her stage of development, gender, and changes in other symptoms. To provide a long-term dynamic view of comorbid psychopathology, it would be of great interest to evaluate the advantages of a network approach in developmental psychopathology research when investigating the stability and

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change of certain disorders, symptom clusters, and/or behavioral dimensions along the life-span. For example, there is a high stability of ADHD symptoms from ages 9 –14 (Larsson et al. 2004). Subsequent follow-up at ages 16 – 17 has indicated that hyperactivity-impulsivity decreases, while inattention remains the same (Larsson et al. 2006). Changes in ADHD subtypes and comorbidity have also been reported (Lahey et al. 2005; Steinhausen et al. 2010). So far, logistic regression analyses have mainly been used and have already provided some practically useful answers in regard to the development of comorbidity (Roessner et al. 2007a; Wanderer et al., submitted). In their introduction (target article, sect. 1), Cramer et al. ask whether there is a general order in which people develop a particular disorder first and then another next. We would add here that there might be a third disorder, as can be seen in daily clinical practice in child psychiatry when faced with psychosocial impairment caused by comorbidity. For example, TD has a childhood onset and follows a remitting course into adulthood. Comorbid ADHD commonly antecedes TD and remains after tic remission, whereas comorbid OCD starts later than tics and often remains longer than ADHD. Unfortunately, developmental psychopathology with more than one comorbidity has received minor attention to date, and it would be a challenge for a network approach to disentangle this complex issue. Using logistic regression, Roessner et al. (2007a) analyzed, in children and adolescents with TD, the impact of comorbid ADHD diagnosis on the frequency of additional (third) comorbidities during development. The main finding was that ADHD generally increased comorbidities in all age groups (especially the externalizing problems), with the exception of adolescent OCD and anxiety disorders. In addition, there was a higher annual rate of change in emotional problems compared to externalizing disorders. This indicates the importance of an increase in internalizing problems with age for comorbidity research in youngsters and underlines that multiple comorbidity is a relevant issue that needs to be dealt with. It is hard to imagine how a network approach could solve this problem in a scientifically better and practically more useful way, since too many interactions over time may reduce clarity. On the other hand, these manifold interactions between symptoms, changing over time, reflect reality. Hence, a network approach can be successful in this matter only if a statistical way is found to reduce complexity without loosing validity. In sum, the network approach should be evaluated in developmental psychopathology mainly for theoretical reasons while its practical value for a better understanding and treatment of young patients has yet to be proven. Such a research perspective should include not only the level of clinical symptoms, but also the area of neuropsychological and neurobiological parameters. This may allow us to define new endophenotypes, which might in turn help to further elucidate the comorbidity pathway from genes to behavior.

Comorbidity: Cognition and biology count! doi:10.1017/S0140525X10000695 Orly Rubinstena and Avishai Henikb a Edmond J. Safra Brain Research Center for the Study of Learning Disabilities, Department of Learning Disabilities, University of Haifa, Mount Carmel, Haifa 31905, Israel; bDepartment of Psychology and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel. [email protected] [email protected] http://www.bgu.ac.il/henik/index.html

Abstract: We agree with Cramer et al. that pure cases of behavioral disorders with no symptom overlaps are rare. However, we argue that

Commentary/Cramer et al.: Comorbidity: A network perspective

Figure 1 (Rubinsten and Henik). Three alternative frameworks for the origins of comorbidities. IPS, intraparietal sulcus. (a) A unique pathophysiology resulting in a network of behavioral symptoms. (b) Multiple brain dysfunctions resulting in a network of behavioral symptoms and (c) A unique pathophysiology resulting in a behavioral disorder that is a risk mechanism for another disorder.

disorders do exist and the network idea is limited and limiting. Networks of symptoms are observed mainly at behavioral levels. The core deficit is commonly at the cognitive or brain levels, and there the story is completely different.

We argue that latent variables, argued against by Cramer et al. in the target article, exist at the cognitive and biological levels. Interestingly, when we searched Cramer et al.’s article, the word brain was not mentioned even once, the words gene and cognitive were mentioned twice, and the word cognition was mentioned only once. If only the behavioral level is taken into account, the suggested network model may be correct. However, core features of mental disorders are best understood in terms of deficits at the cognitive and the biological levels (e.g., Frith 2001). Specifically: (1) Core (“common cause”) deficits at the cognitive or brain level may show up as a network of symptoms similar to that suggested by Cramer et al., even when there is a single deficit. (2) A single deficit at the behavioral or cognitive level may produce, through development, a cascade of difficulties (Rutter & Sroufe 2000) that may end up as comorbidity (i.e., look like a network of symptoms at the behavioral level). (3) Revealing the core deficit will lead to a more exact diagnosis, which would encourage specific intervention programs. We discuss developmental learning and behavioral disorders. A most remarkable finding is the specificity of these disorders: highly

intelligent children who excel in many different ways, have a specific cognitive disability. This one specific cognitive gap may hamper, through development, many types of behaviors, including those that are relevant to other abilities, resulting in comorbidity or in “a network of symptoms.” Here we give examples of mathematics disorder, reading disorder, and attention-deficit hyperactivity disorder (ADHD) to further strengthen our argument. Mathematics and reading disorders. Five to seven percent of children experience difficulties in learning mathematics and/or reading though they are not of low intelligence and do not suffer from educational deprivation (von Aster & Shalev 2007; Wilson & Dehaene 2007). Current research suggests that these learning disabilities, known as developmental dyscalculia (DD) and dyslexia (for reading), are due to underlying brain dysfunctions (see, e.g., Cohen Kadosh et al. 2007; Kucian et al. 2006; Shaywitz & Shaywitz 2008). Similar to depression and generalized anxiety, DD and dyslexia, recognized psychiatric disorders (under different terms such as mathematics disorders or reading disorders; American Psychiatric Association 1994) are appropriate to test Cramer et al.’s main arguments. It has been suggested that DD reflects deficiency mainly (but not only) in brain regions of the parietal cortex, along the intraparietal sulcus (IPS). IPS deficiencies can be found at the structural level (Isaacs et al. 2001; Rotzer et al. 2008) and the functional level (e.g., DD in adults [Cohen Kadosh et al. 2007; BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective Holloway & Ansari, in press]; DD in children [Kaufmann et al., in press; Kucian et al. 2006; Mussolin et al., 2010; Price et al. 2007]). The IPS is considered to be involved with an abstract, amodal representation of numbers (Cantlon et al. 2009; Dehaene 2009). The IPS is also activated by numbers presented in symbolic notations such as Arabic numerals and spoken number words (Eger et al. 2003). Despite indecisiveness in existent developmental imaging studies (e.g., Kucian et al. 2006; Price et al. 2007), deficiency in the IPS functioning is the best-validated core deficit or, in Cramer et al.’s terminology, the common cause of DD (Wilson & Dehaene 2007). This in itself does not fit the description of multiple behavioral symptoms organized in a connected network with no latent variable. Importantly, the unique pathophysiology of DD is frequently accompanied by heterogeneous behavioral deficits. Moreover, this is the case in many other developmental disorders (Karmiloff-Smith 2006) – multiple problems are the rule, and pure disorders apply only to a minority of cases. Twenty to sixty percent of children with DD have associated learning problems such as dyslexia (von Aster & Shalev 2007). What is the reason? We (Rubinsten & Henik 2009) have suggested two main alternative hypotheses for the origin of comorbidity of DD and dyslexia: (1) A single brain injury may cause DD and include a risk mechanism for dyslexia (see our Fig. 1, panel a). For example, a deficient ability to automatically associate written symbols with mental representations such as quantities or phonemes may lead to math and reading difficulties. In this case, a specific brain lesion produces a cognitive difficulty, and a network of symptoms may appear at the behavioral level but not necessarily at the cognitive or biological levels. (2) DD þ dyslexia could be due to several brain dysfunctions – for example, one in the IPS (Price et al. 2007) resulting in DD, and the second in the left peri-sylvian brain areas (McCandliss & Noble 2003) resulting in dyslexia (see Fig. 1, panel b). Landerl et al. (2009) have suggested that dyslexia and dyscalculia have separable cognitive profiles (i.e., a phonological deficit in dyslexia and a deficient number module in DD) that simply appear together. This does not support Cramer et al.’s network theory. Attention-deficit hyperactivity disorder (ADHD). One developmental disorder may be a risk mechanism for another; an example follows. ADHD is a neuropsychiatric disorder that is characterized by inattention, impulsivity, and motor restlessness (American Psychiatric Association 1994; Bush 2010). Individuals with ADHD manifest unexpected problems in mathematics that cause impairments in academic achievement and daily functioning, with estimates ranging from 10% to 60% (Mayes et al. 2000). Some attribute the significant mathematical delays in children with ADHD to attention-based impairments (Lindsay et al. 2001) or working memory (Rosselli et al. 2006). These general cognitive impairments (i.e., not specific to mathematics) are considered to be integral features of the ADHD syndrome and, hence, may cause mathematical difficulties in some of these children (i.e., DD þ ADHD) (Barkley 1997; Castellanos et al. 2006) (see Fig. 1, panel c). To summarize, DD, dyslexia, and ADHD are specific neurodevelopmental psychiatric disorders, but they are rooted at the biological and cognitive levels, and are only indicated by behavioral signs. Therefore, even if at present research regarding such biological and cognitive deficits is not always conclusive, it can better serve as a basis for testable predictions. ACKNOWLEDGMENTS Work by Orly Rubinsten and Avishai Henik was conducted under the auspices of the Center for the Study of the Neurocognitive Basis of Numerical Cognition, supported by the Israel Science Foundation (Grant No. 1664/08) in the framework of their Centers of Excellence.

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Looking at comorbidity through the glasses of neuroscientific memory research: A brainnetwork perspective doi:10.1017/S0140525X10000804 Angelica Staniloiua and Hans J. Markowitscha,b a Department of Physiological Psychology, University of Bielefeld, D-33501 Bielefeld, Germany; bInstitute for Advanced Study, Alfried-Krupp Wissenschaftskolleg, D-17487 Greifswald, Germany. [email protected] [email protected] http://www.uni-bielefeld.de/psychologie/personen/ae14/ staniloiu.html http://www.uni-bielefeld.de/psychologie/personen/ae14/ markowitsch.html

Abstract: As psychiatric illnesses have correlates in the brain, it is surprising that Cramer et al. make almost no reference to the brain’s network character when proposing a network approach to comorbidity of psychiatric diseases. We illustrate how data from combined neuropsychological and functional and structural brain-imaging investigations could inform theoretical models about the role played by overlapping symptoms in the etiology of psychiatric comorbidity and the pathways from one disorder to another.

Comorbidity with substantial overlap of symptoms can be found in a number of disease conditions, apart from the generalized anxiety disorder (GAD) and major depressive disorder (MDD) analyzed by Cramer et al. in the target article – for example, between bipolar disorder, attentiondeficit hyperactivity disorder (ADHD), and severe mood dysregulation. Furthermore, ADHD is often comorbid with bipolar disorder. “Severe mood dysregulation” is non– DSMIV-TR terminology (Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text Revision; American Psychiatric Association 2000) that describes a condition in children which comprises a constellation of symptoms of ADHD, oppositional defiant disorder, and MDD (Hudziak et al. 2007). Deficits of face-emotion processing were reported in major depressive disorder, bipolar disorder, severe mood dysregulation, and ADHD. The investigation of face-emotion processing with functional magnetic resonance imaging (fMRI) provided evidence of distinct neural correlates among youth (children and adolescents) with bipolar disorder, ADHD, and severe mood dysregulation (Brotman et al. 2010). The neural (amygdala) activation during face-emotion processing in youths with severe mood dysregulation resembled the neural pattern reported in youths with MDD. This is of particular interest given longitudinal studies indicating that severe mood dysregulation in youths leads to subsequent depressive episodes (Brotman et al. 2010). These results therefore provide an example of how neuropsychological investigations combined with brain imaging may scientifically test the significance of overlapping symptoms for the etiology of comorbidity and may even aid in predicting the progression from one disease to another. Memory research data could also inform a network conceptualization of comorbidity by offering a perspective that is grounded in evidence about brain organization and development and takes into account variables such as age, gender, and developmental phase. Though not listed under the DSMIV-TR (APA 2000) diagnostic symptom criteria, impairments of episodic-autobiographical memory are frequently described in MDD (Beblo & Herrmann 2000; Williams & Scott 1988) or bipolar disorder. Furthermore, several psychiatric disorders (e.g., dissociative amnesia) and neuropsychiatric disorders (e.g., mild cognitive impairment [MCI], Alzheimer’s dementia) characterized by alterations of episodic-autobiographical memory can be comorbid with MDD. Also, in stroke conditions, temporal lobe epilepsy, and multiple sclerosis, both

Commentary/Cramer et al.: Comorbidity: A network perspective MDD and episodic-autobiographical memory deficits could be present. From a neuroscience perspective, an analysis of the nature of the co-occurrence of memory and emotional disorders includes references not only to genetic polymorphisms, but also to brain mechanisms such as extent of insult (lesion penumbra), shared vasculature, axonal innervations (branching, bifurcation) or brain metabolism (neurotransmitters, enzymatic pathways), and a desynchronization or disconnection of otherwise integrated brain networks. The latter may underlie several psychiatric symptoms, including the co-occurrence of emotional processing and episodic-autobiographical memory impairments, as illustrated further on. It is accepted that specialization and integration characterize the human brain and that cognition and emotion are integrated through structures with a high degree of connectivity (hubs; Pessoa 2008). Functional neuroimaging has provided evidence for abnormalities of functional connectivity between spatially distanced brain areas, underlying several psychiatric symptoms. Recently, the combination of functional and newer structural imaging techniques (diffusion tensor imaging) began to unearth (micro-)structural correlates for various functional connectivity dysfunctions, in particular white matter (long-range fiber tracts) abnormalities (Catani 2007; Paus et al. 2008). Evidence for white matter changes in several psychiatric disorders comes also from genetic and anatomo-pathological research. Postmortem studies of patients with MDD revealed glial cell loss (Rajkowska et al. 1999). Dysfunctions of oligodendrocytes or genes involved in myelination have been reported both in patients with MDD and in those with bipolar disorder (Lee & Fields 2009). Apart from genes, sex hormones are involved in a gender-differentiated modulation of white matter reorganization in adolescence (Paus et al. 2008). Environment and experience could also exert influences on white matter development, partly via epigenetic regulation of gene expression in myelinating cells (Casaccia-Bonnefil et al. 2008). In children with a history of early deprivation, an overgeneral memory effect (Valentino et al. 2009), as well as changes in fiber tracts (including the uncinate fascicle [UF]), have been described (Govindan et al. 2010). The anatomy and function of UF suggest that it may be one pathway of co-occurrence of emotional and memory disorders. UF integrates memory with emotion and links portions of the frontal and temporal lobes. Its ventromedial part connects the amygdala and uncus with the gyrus rectus and the subcallosal area (Ebeling & von Cramon 1992) (Fig. 1). The UF partly intermingles with the anterior commissure and the inferior occipitofrontal fascicle. It matures later than other connections and may continue its development beyond 30 years (Lebel et al. 2008). The ventral right UF is involved in the retrieval of episodicautobiographical memories, in particular in ecphorizing affectladen personal events. The UF also belongs to an emotional processing circuitry that connects the amygdala with the orbitofrontal cortex and the anterior cingulate cortex. The role played by the right UF in the retrieval of episodicautobiographical memories has been underlined by several studies of memory performance in patients with neurological insults as well as in normal people (Fink et al. 1996; Levine et al. 1998). Furthermore, the relevance of the UF for declarative memory in general has received support from many researchers who described a connection between UF structural alteration and memory performance in temporal lobe epilepsy (Diehl et al. 2008), multiple sclerosis (Sepulcre et al. 2008), and Alzheimer’s disease (Yasmin et al. 2008). In their study of amnestic MCI, Fujie et al. (2008) reported both memory and emotional recognition impairment together with abnormalities of the UF. Consistent with the proposed role of UF in emotional processing, recent evidence suggests that disruptions in functional fronto-limbic connectivity described in MDD,

bipolar disorder, and anxiety disorders may have UF microstructural changes as substrates (Phan et al. 2009; Taylor et al. 2007; Wang et al. 2009). Altered connectivity between brain areas involved in memory and emotional processing may also underlie the commonly observed comorbidity between dissociative amnesia and MDD (Maldonado & Spiegel 2008). Evidence for functional disconnections between these areas in dissociative amnesia was provided in a study by Brand et al. (2009). In this study functional brain imaging performed in a resting state in 14 patients with dissociative amnesia evinced malfunctions of the right temporofrontal regions with a common significant hypometabolic zone in the right inferolateral prefrontal cortex. As Tramoni et al. (2009) have found, these malfunctions may reflect subtle structural right-hemispheric prefrontal white matter abnormalities. Regarding the directionality of the links between emotional disorders and episodic-autobiographical memory impairments, it seems perhaps intuitive that one pathway flows from emotional disorders to episodic-autobiographical memory impairments via a defect in emotional processing. Memory research, however, provides grounds for an additional, less intuitive pathway, which may run from episodic-autobiographical memory impairments to the emotional disorders. In this regard, recent data emphasize that episodic-autobiographical memory has a significant proscopic function. Neuroimaging studies have revealed that similar networks, which serve episodic-autobiographical memory, are engaged in self-projection and construction of future events (Schacter & Addis 2009). A main characteristic of patients with MDD is the inability to imagine an optimistic future (Sharot et al. 2007), which may lead them to attempt suicide. It is plausible that this inability may reflect a disruption of the balance between the neural networks that subserve the encoding and retrieval of positive versus negative episodic-autobiographical memories (Markowitsch et al. 2003). In conclusion, we agree that shortcomings characterize current psychometric conceptualizations of psychiatric diseases, warranting a more in-depth psychometric thinking and the advent of new conceptualizations. We suggest, however, that the development of a theoretical network approach to psychiatric diseases should rely on an iterative relationship between psychometric analysis and neuroscience, which would enable refinements of the network approach.

Figure 1 (Staniloiu and Markowitsch). Lateral view of the frontal cortex showing the course of the fasciculus uncinatus. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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The importance of modeling comorbidity using an intra-individual, time-series approach doi:10.1017/S0140525X10000816 Dana Tzur-Bitan,a Nachshon Meiran,a and Golan Shaharb a Department of Psychology, Ben Gurion University of the Negev, Beer-Sheva 84105 Israel; bDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510. [email protected] [email protected] [email protected] http://www.bgu.ac.il/nmeiran http://w3.bgu.ac.il/riskreslab

Abstract: We suggest that the network approach to comorbidity (Cramer et al.) is best examined by using longitudinal, multi-measurement, intraindividual data. Employment of time-series analysis to the examination of the generalized anxiety disorder and major depressive disorder comorbidity enables a detailed appreciation of fluctuations and causal trajectories in terms of both symptoms and cognitive vulnerability.

We are fascinated with the compelling challenge posed by Cramer et al. to the latent variable approach, and with their alternative, network approach to the understanding of psychiatric comorbidity. Whereas the determination of the empirical status of both approaches – latent and network – awaits future research, herein we argue that such research would greatly benefit from an intensive, intra-individual, longitudinal prospective study design, preferably using time-series analysis (TSA). The preponderance of cross-sectional data pertaining to the anxiety and depression comorbidity often yields perplexing results. Most cross-sectional studies investigating comorbidity are in essence based on the assessment of prevalence, aimed at examining the likelihood of a secondary syndrome in the presence of a primary one. Studies applying such an approach

generally tend to conclude that anxiety leads to depression (Breslau et al. 1995; Hettema et al. 2003; Kessler et al. 1996; Lewinsohn et al. 1997). As cited by Cramer et al., longitudinal, inter-individual studies focusing on point prevalence, suggest differently. Namely, these studies show that both disorders are equally likely to be the first in a comorbidity sequence (Moffitt et al. 2007). Moreover, longitudinal studies focusing on subthreshold symptomatology indicate that mixed presentation of anxiety and depression tends to culminate in either full recovery, or in pure presentation (Barkow et al. 2004). While employing inter-individual, longitudinal research on comorbidity advances the field, herein we argue that the logic behind such studies should be taken further to examine intraindividual unfolding of symptomatology. In an ongoing project examining intra-individual trajectories, and associations, involving anxiety and depression, we find, first and foremost, intense fluctuations in symptoms, cognitions, and affective variants of both disorders. These frequent fluctuations across a 2- to 6month period were unique to participants reporting elevated levels of both anxiety and depression. Such a pattern is sharply contrasted with the relatively stable manifestation of anxiety and depression in participants exhibiting, to begin with, elevated levels of either, but not both, anxiety and depression. To illustrate, in Figure 1 we present the variability of fear, as representing the affective component of anxiety, in four participants initially assessed using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI). Participants were also assessed as to their affect by using the PANAS-X (Positive Affect Negative Affect Schedule, expanded version; Watson & Clark 1994), for a period of 2 months, three times daily. Two of the four participants had elevated scores on both the BDI and BAI, and the remaining two had elevated scores on either. As shown in Figure 1 (top), participants with a mixed presentation exhibited marked fear fluctuations.

Figure 1 (Tzur-Bitan et al.). Plot of fear (PANAS-X; Watson & Clark 1994) across study assessment period. A and B: Variability in mixed anxiety and depression participants. C: Variability in anxiety participant. D: Variability in the depression participant.

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Commentary/Cramer et al.: Comorbidity: A network perspective Aimed originally for forecasting and control of economical and political trends, TSA involves describing and predicting the pattern of behavior of a variable based on its own past values, while considering the effects of slowly adjusted, gradual accumulation (namely, auto-regressive processes) versus local influences (moving-average processes). The most common and widely used technique for analyzing and forecasting time series is based on the Box and Jenkins (1976) methodology, which includes a three-step strategy for selecting the best forecasting model from a general class of regression-based models. Having selected a model, it then becomes possible to estimate parameters, check the goodness of fit to the data, and then use the fitted model to enhance understanding of the dynamic laws governing the investigated phenomenon. Box and Jenkins also offer a strategy for assessing causality by using transfer-noise function modeling, aimed at assessing the trajectory from an input series (such as an anxiety symptom) into a dynamic system and on to the output series (such as a depression symptom). Implementation of this approach enables the evaluation of the duration, direction, and intensity of influence of one construct on the other. In another project, we examined three participants suffering from generalized anxiety Disorder (GAD) and major depressive disorder (MDD) comorbidity. These participants were followed up daily for an extensive period of 6 months, and TSA was utilized to determine fluctuations and trajectories. We found cognitive vulnerability to emerge as a key component shaping the causal network between symptoms of anxiety and depression. Specifically, in two of the three participants, the looming maladaptive cognitive style (LMCS; Riskind et al. 2000), pertaining to the tendency to generate mental scenarios of potentially threatening situations as rapidly rising in risk, was causally related to subsequent increase in depressive symptoms. As well, in two of the three, LMCS was causally related to a subsequent increase in hopelessness, itself a cognitive vulnerability dimension to depression. In none of the participants did symptoms of depression, or depressive vulnerabilities, cause subsequent increases in anxiety or its vulnerabilities. These findings are particularly intriguing because they suggest that cognitive vulnerability to anxiety might cause both depression and its specific cognitive vulnerability, but not vice versa. Put in the context of Cramer et al.’s argument, our findings suggest that anxiety and depression are multidimensional, and that specific dimensions of anxiety (but not depression) are causally related to specific dimensions of depression. On the face of it, this is consistent with the network approach, and we hope that the use of TSA will shed further light on distinct causal configurations involving anxiety and depression.

Consequences of a network view for genetic association studies doi:10.1017/S0140525X10000701 Sophie van der Sluis,a Kees-Jan Kan,b and Conor V. Dolanb a Department of Functional Genomics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije University Amsterdam, 1081 HV Amsterdam, The Netherlands; bDepartment of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, 1018 WB Amsterdam, The Netherlands. [email protected] [email protected] http://home.uva.nl/kees-jan.kan/ [email protected] http://users.fmg.uva.nl/cdolan/

Abstract: Cramer et al’s proposal to view mental disorders as the outcome of network dynamics among symptoms obviates the need to invoke latent traits to explain co-occurrence of symptoms and syndromes. This commentary considers the consequences of such a network view for genetic association studies.

Genetic association studies (henceforth GAS, used in the plural) aim to identify genes or genetic variants (GVs) that are systematically associated with variation on the behavioural level (Balding 2006; Hirschhorn & Daly 2005). In its exploratory form, GAS test the associations between 300.000-500.000 GVs typed across the entire genome and a phenotype of interest, which can be continuous or dichotomous, such as affection-status. For instance, consider a GV with two alleles: A and B. If allele A is more frequently observed in cases than in controls, then this allele A may be associated with an increased risk for the disease under study. Where successful, GAS are a first step towards revealing functional relations between the genome and behaviour. GAS have successfully localized GVs for medical conditions such as Crohn’s disease (Zhang et al. 2009) and type-I diabetes (Barrett et al. 2009), but, despite high family-based heritability estimates, have been less successful for psychiatric traits. Disappointing results have been attributed to statistical problems (e.g., low power), genetic complexity (e.g., gene-gene interaction), and genetic heterogeneity (e.g., the genetic etiology of disease may vary across families). Incorrect characterisation of the phenotypic model, however, may also render GAS inefficient. In the search for GVs associated with psychiatric disorders, symptoms are usually considered manifestations of an underlying latent trait, which causes the variance in the symptoms, and the observed relations between the symptoms. The ultimate aim of GAS is to identify the GVs that cause individual differences in the latent trait, as under this model the relations between GVs, on the one hand, and individual symptoms, on the other, are mediated by the latent trait. A common operationalization of a latent trait in GAS is the sum score calculated across all items or symptoms of a diagnostic test. The sum score can subsequently be subjected to dichotomization following diagnostic cut-off criteria. This score then features as the dependent variable in GAS, and GVs are used to predict the (dichotomized) sum score. Although the (dichotomized) sum score may be a crude approximation of the latent trait, the operationalization is consistent with the latent trait model. In the network model proposed by Cramer et al., however, the observed relations between symptoms are not attributable to a common latent cause but result from the direct causal relations among the symptoms themselves. In this model, all symptoms could in principle have a unique genetic etiology, so the sum score operationalization could potentially be counterproductive in the search for GVs. One may ask, however, whether the high twin-based and family-based heritability estimates (h 2) for psychiatric disorders are consistent with Cramer et al.’s network view. Under the network model, considerable h 2 estimates remain feasible for two reasons. First, calculations of h 2 are based on within-family comparisons. Researchers test, for example, whether monozygotic twins, who share 100% of their genetic material, are phenotypically more alike than dizygotic twins, who share on average 50% of their genetic material (Falconer 1989). If variance in symptoms is genetic in origin, then the genetic basis of the disorder is likely to be shared by members of the same family. Even if all symptoms have a different genetic etiology, and this etiology differs across families (i.e., genetic heterogeneity), family-based estimations of h 2 can be considerable because they are based on comparisons within families. Second, the direct causal interrelations between symptoms, such as described in the network model, will over time induce genetic correlations between symptoms that are initially genetically unrelated, that is, do not functionally share any genetic basis (as shown by van der Maas et al. [2006] in the context of intelligence). That is, with the passing of time, a common genetic factor may evolve from the phenotypic interactions between genetically unrelated symptoms, and the h 2 of the sum score may increase. The fact that the h 2 of a sum score calculated across genetically unrelated symptoms can be substantial has important implications for GAS. Specifically, the high h 2 of sum scores does BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective not mean that this operationalization is useful in the search for the actual GVs of interest. The distinction between the network model and the latent trait model is thus essential in GAS: If the network model is the true model, then the common way geneticists operationalize their phenotypic information may be counterproductive. Under the network model, GAS on the individual symptoms, or even on the relations between the symptoms, as Cramer et al. suggest, make more sense. Of course, such an approach comes with its own challenges, such as the exacerbation of the problem of multiple testing (e.g., 10 symptoms may produce 1 sum score, but 10 symptom-level ones, and 45 relational tests). At present, the network perspective of major depressive disorder (MDD) and generalized anxiety disorder (GAD) is quite speculative. A recent study by Lux and Kendler (in press), however, lends some credence to the network view of MDD. These authors showed that nine criteria for MDD (e.g., depressed mood, fatigue) displayed markedly different relations to factors such as risk for future episodes, risk of depression in the cotwin, and patterns of comorbidity. These results suggest considerable heterogeneity within the MDD syndrome, and are inconsistent with the idea that, within disorders, all symptoms are interchangeable indicators of a single causal latent trait. Although these results do not prove the network model, Lux and Kendler’s findings are hard to reconcile with the latent trait model. While GAS may benefit from a network approach to phenotypes, genetic studies may help to validate the network approach. Network models and latent trait models may yield quite similar phenotypic covariance structures (van der Maas et al. 2006). Behaviour genetics provides several research designs that are informative about causality, such as direction of causation models, the co-twin control design, the children-of-twins design, and Mendelian randomisation. In additional, possible genetic influence on the interrelationship among symptoms can be addressed using moderated genetic covariance structure modeling (Purcell 2002). Like the latent trait model, the network perspective is a theoretical model, which still requires validation. If true, however, it constitutes an interesting alternative viewpoint and could have far-reaching consequences for GAS.

Networks as complex dynamic systems: Applications to clinical and developmental psychology and psychopathology doi:10.1017/S0140525X10000828 Paul L. C. van Geerta and Henderien W. Steenbeekb a Department of Developmental Psychology, The Heymans Institute, University of Groningen, 9712 TS Groningen, The Netherlands; bDepartment of Developmental Psychology, University of Groningen, 9712 TS Groningen, The Netherlands. [email protected] http://www.paulvangeert.nl [email protected] http://www.gmw.rug.nl/model/

Abstract: Cramer et al.’s article is an example of the fruitful application of complex dynamic systems theory. We extend their approach with examples from our own work on development and developmental psychopathology and address three issues: (1) the level of aggregation of the network, (2) the required research methodology, and (3) the clinical and educational application of dynamic network thinking.

Cramer et al.’s target article shows a fruitful application of a dynamic network perspective – or complex dynamic systems (CDS) perspective – to clinical psychology, in particular the comorbidity of depression and anxiety. This commentary addresses three issues that can place the article in a broader framework of CDS-oriented theory and research in developmental psychology and psychopathology.

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A complex dynamic system (CDS) is as a collection of interconnected components – the network – which change one another’s properties through the interconnections and out of which emerge collective properties, such as patterns or correlations (Van Geert 2009). These patterns correspond with macroscopic phenomena (e.g., depression, attention-deficit hyperactivity disorder [ADHD], developmental levels). We believe that CDS theory provides a coherent conceptual and methodological tool-kit for understanding virtually all interesting phenomena of emergence in the behavioral sciences. Cramer et al.’s network of causal influences is based on actual phenomenal appearances – symptoms – and not on underlying entities, such as depression, that correspond with latent variables. Assigning the causality to the actual phenomena instead of underlying entities is typical of the dynamic systems approach (e.g., Thelen & Smith 1994; van der Maas et al. 2006). Our own work provides examples of a CDS approach to long-term cognitive and language development, based on the interaction of components in a network of variables (e.g., Bassano & van Geert 2007; Fischer & Bidell 2006; Steenbeek & Van Geert 2007; 2008; Van Geert 1991; 1994; 1998; Van Geert & Steenbeek 2005). In an ongoing project, we have formulated a dynamic work model for explaining science and technology talents in young children (see Van Geert & Steenbeek, submitted). The first question we address concerns the chosen level of aggregation for defining the dynamic network. Cramer et al.’s network is a structure of intra-personal symptoms, leading to comorbidity of depression and generalized anxiety. Our own approach to problematic learning trajectories in children with ADHD and PDDNOS (pervasive developmental disorder, not otherwise specified) is based on the concept of a distributed network; that is, variables distributed over a child, an educator, and a material context. We view disorders such as ADHD as attractor states, consisting of fuzzy and variable ensembles of symptoms, actions, emotions, contexts, actions of the educators, and so on. These components affect one another in that they call forth one another’s appearance in particular contexts. The interactions between components result in the consolidation of a particular pattern – for instance, one that educators or psychologists identify as the underlying condition ADHD, which they see as the “deep” causal mechanism of the symptoms. Our view is highly comparable to Cramer et al.’s view on clinical symptoms as attractor states; that is, self-sustaining patterns of mutually attracting symptoms. Our main goal is to use the shortterm dynamics of relationships between variables (e.g., what happens during a concrete child-educator interaction) to explain the long-term trajectories through the state space of variables, which leads to the kind of self-sustaining states diagnosed as ADHD and the like. Our distributed network shows a nested structure, with the child, the educator, and the educational materials as the main nodes, each comprising lower-level networks of variables specific to each of the main nodes (for an example of such a nested dynamic system, see our work on dyadic play interaction in children; Steenbeek & Van Geert 2007; 2008). The distributed network can be described from the perspective of each of its main nodes. For instance, from the perspective of the child, the educator and the educational materials constitute what is commonly called the context of the child’s actions. Such contexts are not to be treated as independent variables. They are dynamically co-determined as part of the network (see Steenbeek & Van Geert 2007; 2008). They can change abruptly (e.g., if the child changes classes or activities), which may lead to relatively abrupt changes in the temporal attractor state (e.g., the actual expression of the child’s ADHD), thus helping to explain the characteristic intrapersonal variability of a particular child’s ADHD condition (see, e.g., Van Geert & van Dijk 2002). Cramer et al. emphasize the variations in connection strength between nodes, defining the nodes’ degree of centrality, that is, importance in a particular network. Superficially similar constellations of symptoms under the umbrella term of ADHD, for

Commentary/Cramer et al.: Comorbidity: A network perspective instance, can be based on networks of different composition; that is, with different variables being more or less dynamically central, dependent on the particular individual in a particular context. This brings us to our second issue, which is that of empirical design and methodology. In line with Cramer et al., we argue that networks emerge in the form of individual trajectories, and should therefore be studied with a time-serial case methodology. We study dynamic networks by directly observing the child’s problematic learning in the class. We combine the observation of the short-term dynamics in a particular child – what happens during a single math lesson – with an account of the child’s long-term dynamics, that is, changes in the patterning of the relationship between the components over the course of months or years (Steenbeek & Van Geert, submitted). This approach is ideographic, that is, it requires individual case studies. Generalization pertains primarily to the relationship between individual cases and the underlying CDS theory and only secondarily to sample generalization (see also Cramer et al’s reference to Molenaar 2004; cf. Van Geert, in press). Our third issue concerns the clinical or educational application of complex dynamic network models. We believe that a practitioner’s qualitative insight into a dynamic network explanation of clinical phenomena – for example, child psychopathologies – will lead to a different practice than one based on a theory of “deep” causes (e.g., the latent variable ADHD as a causal explanation of observable ADHD symptoms). An approach based on an understanding of the fuzzy and dynamic boundaries between disorder and normality may help practitioners to view their actions as part of an interconnected dynamic network, and may help them to redefine the kind of control problem they have to solve in order to help children overcome their clinical and behavioral problems.

The missing developmental dimension in the network perspective doi:10.1017/S0140525X10000713 Sam Wass and Annette Karmiloff-Smith Centre for Brain and Cognitive Development, Birkbeck College, University of London, London WC1E 7HX, United Kingdom. [email protected] [email protected] http://www.cbcd.bbk.ac.uk/people/students/sam http://www.psyc.bbk.ac.uk/research/DNL/personalpages/annette.html

Abstract: We welcome network theory as a tool for modelling the multidirectional interactions that characterise disease. However, we feel that Cramer et al. have neglected one important aspect: how diseases change over developmental time. We discuss principles such as fan in, fan out, bottlenecks, and common pathways, and argue that modelling these developmental aspects can be vital, particularly in deriving properly targeted treatments.

We welcome the central distinction offered in the target article between a latent-variable (i.e., disorder based) and a network theoretical (i.e., symptom based) approach to epidemiology. We have also argued (e.g., Karmiloff-Smith 1992; 1997; 1998; 2007; 2009; Thomas & Karmiloff-Smith 2002) in favour of approaches to typical and atypical development that emphasise multi-directional interactions between genes, brain, cognition, behaviour, and environment. Network theory offers precisely such an approach. We also agree with the authors that it is valuable as a way of offering potentially “truer” descriptions according to which diseases are defined as patterns of covariance of symptoms. In their conclusion, Cramer et al. briefly discuss the potential usefulness of dynamic approaches to network modelling as a way of describing features such as bistable depressive states (Van der Maas et al. 2006; see also Rolls et al. 2008) It is disappointing,

however, that the authors do not discuss another equally vital aspect: namely, the actual growth of networks, how patterns of symptoms evolve over developmental time. In attempting to understand diseases, it is not sufficient merely to examine what their symptoms look like in an adult state. Particularly for deriving treatments and interventions, it is vitally important to track the progress of diseases throughout ontogeny to maturity. In doing so, a number of vital features can become apparent: First, symptoms can fan out over time: that is, a small, basiclevel deficit can, during development, lead to impairments in a variety of domains. So, for instance, an impairment in a hub cognitive domain such as executive attention (Cornish et al. 2007; Scerif et al. 2005) can, if present during particular sensitive periods, impede development across a variety domains such as number, language, and other aspects of social development. Thus, one symptom can lead over developmental time to a range of other symptoms, a causal relationship that may be revealed only if the developmental perspective is considered. Second, particular symptoms can fan in: that is, conditions can converge on a common pathway. One example here may be autism, where various authors (e.g., Anderson et al. 2008; Chauhan et al. 2010; Herbert 2005) have hypothesised that a variety of different genetic vulnerabilities (i.e., discrete etiologies) may all converge on causing over-zealous neuroinflammatory responses early in neural development. These inflammatory and oxidative stressors early in development might, in turn, fan out (i.e., disrupt subsequent development in a range of ways), leading to many of the behavioural features that we recognise as the autistic spectrum behavioural phenotype. Again, it is only by adopting a developmental approach that such common pathways (or bottlenecks) may become evident; and yet they may offer potentially vital targets when developing treatment. Similar principles of fan in and fan out can also go some way to explaining comorbidity, and to patterns of covariance of symptoms between different conditions. Thus, for example, any condition associated with impaired executive attention early in development (including, in various forms, Williams syndrome, Down syndrome, fragile X syndrome, and autism) may lead to partially similar patterns of impaired performance, which may then diverge again later in development (Cornish et al. 2007). Another example of how developmental cascades operate can be gleaned from the neurodevelopmental disorder, Williams syndrome (WS; Donnai & Karmiloff-Smith 2000). Infants and toddlers with WS are very impaired early on in planning saccadic eye movements (Brown et al. 2003). This affects their subsequent ability to follow pointing (Laing et al. 2002), which in turn is detrimental to their ability to use parental referential pointing to learn vocabulary. So, although their language becomes proficient (not “intact”; Karmiloff-Smith 1998 much later in development, initially language in toddlers with WS is extremely delayed and follows a deviant developmental trajectory (Annaz et al. 2008; Paterson et al. 1999). Thus, an early problem within the visual system, together with other contributing factors (Masataka 2001; Nazzi et al. 2003), dynamically influences the way in which auditory stimuli are acquired and, because of the need for a critical vocabulary mass before syntax can take off, it is a visual deficit that is at the root of serious delays in grammatical development. Moreover, the failure to plan efficient saccadic eye movements doesn’t only affect the learning of language. Individuals with WS also turn out to be predominantly featural processors, obvious from both brain and behavioural studies (Karmiloff-Smith et al. 2004; Grice et al. 2001; 2003; Mills et al. 2000). A possible explanation for this is that, in the typical case, rapid configural processing emerges from rapid scanning of stimuli via rapid eye movement planning whereas, in the atypical WS case, remaining fixated on a stimulus (e.g., a face) leads to a focus on featural detail. Individuals with autism are also featural analysers, yet the developmental pathway that leads to this end product may be different from the pathway BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Cramer et al.: Comorbidity: A network perspective that leads to featural processing in WS. Again, only a developmental approach, particularly using cross-syndrome comparisons, can reveal the dynamics of the differing routes from infancy to the mature state in adulthood. And associations across syndromes might be more informative than the search for dissociations (Karmiloff-Smith 2009; Karmiloff-Smith et al. 2003). There is an increasing awareness that it is important to study not simply a “snapshot” of symptoms in the mature state, but also the process by which they were reached. In terms of network theory, this poses considerable but not (we think) insuperable demands on the modelling techniques that are used. Useful cross-discipline approaches may come from work on the development and evolution of social networking (e.g., Backstrom et al. 2006; Kumar et al. 2006), where plentiful data are available for all stages of network development, throughout ontogeny. Data from the very early phases of diseases (when the critical building blocks are laid, but before a clinical diagnosis can be made, as is often the case in autism, dyslexia, and dyscalculia) are often harder to come by, which is why conceptual insights from other areas of network science may be so useful.

Comorbidity in the context of neural network properties doi:10.1017/S0140525X1000083X Juliana Yordanova,a Vasil Kolev,a Roumen Kirov,a and Aribert Rothenbergerb a Institute of Neurobiology, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria; bChild and Adolescent Psychiatry, University of Go¨ttingen, D-37075 Go¨ttingen, Germany. [email protected] [email protected] [email protected] [email protected] http://www.bio.bas.bg/neurobiology/ http://www.user.gwdg.de/ukyk/index.html

Abstract: Cramer et al.’s network approach reconceptualizes mental comorbidity on the basis of symptom space originating from psychometric signatures. We argue that the advantages of this approach need to be regarded in the context of the multi-level functional organization of the neural substrate, ranging from neurogenetic to psychometric. Neuroelectric oscillations are proposed as a levelintegrating principle.

A network perspective on psychopathologic comorbidity is proposed by Cramer et al. as an alternative to the latent variable theory. The authors state that the network approach offers a radically different conceptualization wherein comorbidity is hypothesized to arise from direct relations of symptoms of multiple disorders, rather than from the relationship between sources of these disorders. Here, we argue that the advantages of the proposed perspective need to be viewed not only in the context of psychometry-based latent variable theory, but also in the context of existing neurophysiologic models for comorbidity. Based on a series of neurophysiologic studies of comorbid child psychiatric disorders (attention-deficit hyperactivity disorder [ADHD] and multiple tic disorder [TD]; Yordanova et al. 1996; 1997; 2006), we have proposed a multi-level scheme of comorbidity. According to this model (Yordanova et al. 2006), the TD þ ADHD comorbidity can be specified at several different levels, ranging from neurogenetic and neurobiological to neurophysiologic and psychometric. The following argumentation is used. Transcranial magnetic stimulation (TMS) findings have substantiated the additive model for TD þ ADHD (Moll et al. 2001), according to which the comorbid condition is an expression of the combination of independent nosologies. Since TMS measures reflect the background state of motor system

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Figure 1 (Yordanova et al.). A theoretical extension of Cramer et al.’s Figure 4 demonstrating the multilevel formation of phenotype space.

excitability, basic subcortical-cortical (striato-thalamo-cortical) loops controlling the output from the cortical motor system (e.g., Leckman 2002) appear to be differentially impaired in TD and ADHD, and these impairments co-contribute separately in TD þ ADHD comorbidity. Similarly, other basic neurophysiologic processes, such as sleep and its regulation, have been found to be altered differentially in TD and in ADHD, and, also consistent with the additive model, a combination of independent TDand ADHD-related sleep disturbances was present in TD þ ADHD (Kirov et al. 2007a; 2007b). Likewise, the spontaneous theta EEG activity reflecting background neuroelectric brain states has been found to differentiate the TD and ADHD conditions, but not to distinguish the comorbid group, which further supports the additive model. However, event-related potential studies demonstrate that when active processing demands are imposed, TD þ ADHD can be classified either as a phenotype expression of TD (Yordanova et al. 1996) or ADHD (Rothenberger et al. 2000), or as a unique nosology consistent with the interactive model for TD þ ADHD (Yordanova et al. 1997; 2006). Together, these results show that the specification of TD þ ADHD comorbidity depends on the level at which psychopathological conditions are evaluated. Basic cerebral functions in TD þ ADHD coexistence, such as the sleep-wake or cortical excitability and inhibition, appear to be guided by independent TD- and ADHD-related pathogenic sources. Yet, any cognitive activation involving these basic functions, such as focused expectation, uncertainty control, or early selective attention (Yordanova et al. 1996; 1997; 2006), may lead to a complex interplay of the separate pathogenic sources, which may result in highly specific neurocognitive modes of information processing in comorbid patients. At the psychometric level, behavioral parameters during neuropsychological assessment and symptom expression during clinical assessment represent the most complex stage of integration where new markers of comorbidity may emerge as either isolated or bridging characteristics. This multi-level scheme of comorbidity interpretation (Yordanova et al. 2006) shows that the definition of symptom space may not be limited to the psychometric domain (Cramer et al., target article), as it strongly depends on the level of organization at which the functioning of the neural substrate is quantified. Accordingly, the psychometric domain represents just one: the most fused and integral plane across a “vertical” scale where multiple “horizontal” planes of neurostructural organization exist to produce level-specific quantifiers, from molecular and neurogenetic to neurocognitive and psychometric (Fig. 1).

Commentary/Cramer et al.: Comorbidity: A network perspective With this account, the network perspective proposed by Cramer et al. can be extended in the following directions: 1. The network approach may encompass phenotypes at separate levels of neurofunctional organization. Overlapping and non-overlapping signatures can be extracted at each level. Disclosing level-related direct associations may not only refine comorbidity specification, but also provide important information about underlying neural mechanisms. 2. As a further extension, a broader phenotype space can be constructed to expand the definition of symptom space. Multilevel quantifiers, rather than single-level (e.g., psychometric) signatures, can be included in statistical network evaluation. The multidisciplinary perspective stressed by Cramer et al. can certainly consider signatures derived from genetic, neurofunctional, neuroimaging, morphometric, neurotransmission, and such like, domains. 3. Constructing an integrated multi-level and multipledomain phenotype space of signatures may enable the assessment of causality and relationships among different, yet interdependent, levels. Enlarged space entities can be analyzed in the framework of linear system concepts and methods. Importantly, nonlinear dynamic methods would extract cross-level interactions as integral descriptors of the behavior of a complex dynamic system (Rosso & Masoller 2009; Rosso et al. 2001). A unique level-integrating principle for creating a multi-level phenotype space is provided by the concept of neuroelectric oscillations: Oscillatory signals are recorded in various brain structures (Bas¸ar et al. 2001; Buzsa´ki & Draguhn 2004). The temporal and spatial synchronization of frequency-specific oscillatory networks subserves information-processing mechanisms (Varela et al. 2001). Slow-frequency oscillations from the delta, theta, and alpha frequency bands have been associated with largescale networks of executive and cognitive processing, and fastfrequency oscillations from the beta and gamma frequency bands have been related to local processes resulting also from neuronal firing (Gray & Singer 1989; Kirov et al. 2009; Sarnthein et al. 1998; von Stein & Sarnthein 2000). Oscillatory networks thus reflect neural functioning at different levels of organization of the neural substrate. Importantly, the synchronization of frequency-specific networks may be interdependent (Tort et al. 2009) and is also strongly modulated by the spontaneous multiple second-state variations of the default mode networks (Raichle et al. 2001). Neuroelectric oscillations have provided important markers of different mental disorders (e.g., Herrmann & Demiralp 2005) as well as comorbid conditions (Yordanova et al. 2001; 2006) and have promoted relevant models of child psychiatric disorders (Rothenberger 2009; Sonuga-Barke & Castellanos 2007). The relationships and dynamics of neuroelectric oscillations can therefore provide an integrating principle for the formation of multi-level space phenotype signatures, which may have a direct practical application for the identification and treatment of psychiatric disorders.

The abandonment of latent variables: Philosophical considerations doi:10.1017/S0140525X10000841 Peter Zachar Department of Psychology, Auburn University, Montgomery, AL 36124. [email protected]

Abstract: Cramer et al.’s critique of latent variables implicitly advocates a type of scientific anti-realism which can be extended to many dispositional constructs in scientific psychology. However, generalizing Cramer et al.’s network model in this way raises concerns about its applicability to psychopathology. The model could be improved by articulating why a given cluster of symptoms should be considered disordered.

From the force fields and potential energy of physicists to the traits, temperaments, and abilities of psychologists, making dispositions into something real is deeply entrenched in science. What is radical about re-thinking the scientific validity of latent variables is that they are contemporary proxies for dispositions. Latent variables are omnipresent in contemporary psychology, and dispositions even more so. I agree with Cramer et al. that psychiatric comorbidity refers to a real, important phenomenon that should not be dismissed as an artifact of the classification system (Zachar 2009). For example, epidemiological research in the United States indicates that in any given year a majority of psychiatric disorders occur in only 14% of the people (Kessler et al. 1994). This 14% likely represents a vulnerable population. How should we model this vulnerability? One current view is that neuroticism, a historically important latent variable in scientific psychology, is a primary risk factor for psychopathology (Clark 2005; Kahn et al. 2005; Mineka et al. 1998; Rothbart & Ahadi 1994). According to Cramer et al., however, the reality of a symptom pattern is not found hidden behind the symptoms, but in the symptoms themselves. They argue that symptoms are not effects of disorders, but rather, that relations between symptoms constitute disorders. This claim resembles the classical empiricists’ contention that we have no knowledge of an underlying substance called matter, that is, what we know are observable properties only (color, hardness, etc.). Given that latent variables are proxies for unobservable dispositions, the conceptual model described by Cramer et al. can be usefully applied to many hypothetical constructs in psychology, whether or not they are confirmable as latent variables. Exploring this a bit further can shed some light on what is being claimed about psychiatric disorders. Let me, therefore, briefly consider what the model might say about basic emotions theory, specifically, the non-essentialist model of James Russell (2003; 2008). Russell is an anti-realist about affect programs in the same way that Cramer et al. appear to be anti-realists about latent variables. According to Russell, an emotion such as fear represents a family of states that have varying degrees of similarity to one another. Russell claims that different tokens of fear share overlapping components, but there is no set of components that all episodes of fear must share. Two or more episodes classified as fear could potentially have very few components in common. In philosophical terms, fear components such as raised eyebrows and elevated blood pressure are not manifestations of a natural kind; rather, they are individual events that happen to co-occur in ways that we have learned to notice. If scientists could account for all the mechanisms that explain the different parts of an emotional episode, says Russell, there would be no need to posit an additional mechanism called the affect program to explain the emotional episode itself. Affect programs, like latent variables, are constructs that are attributed ontological significance. Why? Because it is important to account for the patterning that occurs, and affect programs fill that role. They are similar to what Medin and Ortony (1989) call essence placeholders. The key point for Russell is that the affect program model commits us to a false ontology. If we continue to use this ontology, we are not going to discover adequate scientific explanations of emotional phenomena. We need a better ontology. Similar to Cramer et al.’s claims about latent variables, Russell would prefer to eliminate the construct of affect programs from our scientific ontology, and with it the notion of basic emotions as legitimate scientific kinds. One of the difficulties readers have with accepting Russell’s model is that they expect the familiar categories of emotion to have some validity. They are uncomfortable with the possibility there is nothing more substantial about the patterns we do notice than those we do not notice. As a type of causal non-essentialism, Cramer et al.’s network model would allow that the patterning of emotion components occurs in a non-arbitrary way, but the patterning is best explained with reference to direct BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Cramer et al.: Comorbidity: A network perspective causal connections between those components rather than with reference to unobserved affect programs. It could support the elimination of affect programs, but unlike for Russell, Occam’s razor would not be applied to basic emotions as well. Perhaps basic emotions can refer to clusters that have a greater probability of spontaneously emerging from the dynamic interaction of causally connected components – in the same way that amino acids are reliably folded into proteins without benefit of a latent protein-folding mechanism that makes it occur. The difference between emotions and psychiatric disorders is that we do not typically think that psychiatric disorders emerge spontaneously like everyday psychological states emerge – disorders entail a failure of a normal mechanism (Wakefield 1992). This is an important point because Cramer et al.’s argument is broader than the claim that shared symptoms, not latent variables, are the important causal factors in comorbidity; they also use relations among symptoms to explain psychiatric syndromes in general. Perhaps the most important development in psychiatric classification in the last 20 years is the effort by many clinical psychologists and some psychiatrists to replace Robins and Guze’s (1970) categorical approach to the medical model with a psychometrically based dimensional model (Livesley 2003; McCrae 1994; Watson 2005). What are the implications for the bold claim that latent variables (categorical or dimensional) are an inadequate basis for a science of psychopathology? For better or worse, success would mean that the network model might be a candidate for a new general model of psychopathology. How does such a model fare as a general model of psychopathology? As currently formulated, the network model demarcates psychopathology as the collection of symptoms that are manifest in whatever psychiatrists decide to treat. Cases are defined when enough symptoms of the right type are present. This may be a bit too nominalistic. For example, what if psychiatrists start conceptualizing liberalism as a mental disorder – as some have suggested (Rossiter 2008; Savage 2005)? What resources would exist for not including the symptoms of liberalism in the total psychiatric symptom space? In the latent variable model, the pathology is supposedly located in the hidden reality behind the symptom patterns. If there is no reality behind the symptom patterns, would “pathologies” be relocated in the symptoms themselves? Could psychopathology be an emergent property? Such a radical relocation project raises a list of interesting complications. Latent variables can be taken as proxies for the underlying pathological processes of the medical model, but justifying the reality of emergent pathologies would require a different kind of thinking.

Authors’ Response Complex realities require complex theories: Refining and extending the network approach to mental disorders doi:10.1017/S0140525X10000920 Ange´lique O. J. Cramer, Lourens J. Waldorp, Han L. J. van der Maas, and Denny Borsboom Department of Psychology, University of Amsterdam, 1018 WB Amsterdam, The Netherlands. [email protected] www.aojcramer.com [email protected] http://users.fmg.uva.nl/lwaldorp/ [email protected] http://sites.google.com/site/borsboomdenny/dennyborsboom [email protected] http://users.fmg.uva.nl/hvandermaas/

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Abstract: The majority of commentators agree on one thing: Our network approach might be the prime candidate for offering a new perspective on the origins of mental disorders. In our response, we elaborate on refinements (e.g., cognitive and genetic levels) and extensions (e.g., to Axis II disorders) of the network model, as well as discuss ways to test its validity.

R1. Introduction In our target article, we have proposed a network view of mental disorders, in which systematic covariation between symptoms is explained by direct relations between the symptoms themselves. The approach breaks radically with the dominant doctrine, in which disorders are considered to be common causes of their symptoms (i.e., the latent variable perspective). We were pleased to see that many commentators view the network approach as a potential substantive theory of mental disorders. Given the varied set of responses, many of which proposed worthwhile empirical research suggestions and theoretical extensions of the approach, we have fortunately succeeded in bringing together researchers from different fields to reconsider what disorders are and how we should investigate them. One of the most surprising and noteworthy facts about the present set of commentaries concerns what they do not contain: Very few commentators attempt to defend the received view that underlies many current approaches to psychopathology: that is, the latent variable perspective. We take this to imply that the time is ripe for a change of perspective. In addition, the comments have strengthened our conviction that, with the necessary refinements and extensions, “‘inference to the best explanation” could ultimately lead us to the network approach as the substantive theory of mental disorders (Haig 2009). Certainly, Rothenberger, Banaschewski, Becker, & Roessner (Rothenberger et al.) argue that the network approach is complex with its “manifold interactions between symptoms,” but we agree with them even more that this reflects reality. And as we will argue here, complex realities require complex theories. In this response, we discuss the most important extensions, refinements, investigative tools, and objections voiced by the commentators according to the following themes. First, several commentators argued that network models can and necessarily must include latent variables (e.g., Haig & Vertue; McFarland & Malta). In section R2, we explain why some relations qualify for such a measurement model – and are thus likely to be incorporated into a network model – while others do not (e.g., depression as common cause of a cluster of symptoms). Other commentators provided excellent suggestions for refinement of the network model in order to include genetic, neurological, and cognitive levels of explanation (e.g., Rubinsten & Henik; Yordanova, Kolev, Kirov, & Rothenberger [Yordanova et al.]), which we discuss in section R3. Additionally, in section R4, we discuss ways to test the network model, as suggested by several commentators (e.g., Davis & Plomin; Fleeson, Furr, & Arnold [Fleeson et al.]; Van der Sluis, Kan, & Dolan [Van der Sluis et al.]). Section R5 investigates the possibility of extending the network approach to other disorders (e.g., Axis II personality disorders [Bornstein; Ross]). Section R6 focuses on an important question, posed by several commentators, as to what constitutes a mental disorder (Haslam; Hood & Lovett; Zachar).

Response/Cramer et al.: Comorbidity: A network perspective

Figure R1. A hypothetical network model for major depression. Circles represent latent variables, and squares represent observed variables. The nine symptoms of major depression are represented as dark gray squares/circles. The pink squares represent multiple measurements for latent symptoms (i.e., weight and sleep problems in this example; see sect. R2). The purple squares represent the cognitive level of the model, the blue square the neurophysiological level, and the yellow squares the genetic level. Abbreviations key: mInt, loss of interest; mDep, depressed mood; mFatig, fatigue; mRest, restlessness; mSleep, sleep disturbances; mWeight, weight problems; mSuic, (thoughts of) suicide; mRep, self-reproach; mConc, concentration problems; polysomno, polysomnography; EncPosMem, problems in encoding/retrieving positive autobiographical memories; NegIntMood, negative interpretation of bad mood; and LeftHippo, smaller volume of the left hippocampus.

Finally, commentators raised methodological objections that claimed either to invalidate the network model we suggested (e.g., Danks, Fancsali, Glymour, & Scheines [Danks et al.]; Krueger, DeYoung, & Markon [Krueger et al.]), or to sustain a common cause view on mental disorders (e.g., Belzung, de Villemeur, Lemoine, & Camus [Belzung et al.]; Humphry & McGrane). In section R7, we discuss these issues and argue that – despite methodological difficulties that have to be addressed in the future – the network model should be viewed as the prime candidate to elucidate the origins of mental disorders. R2. Latent variables in the network approach Markus and Molenaar remark that, if the network approach is to move from a mere representation of the data to a possible representation of the underlying causal and functional relations between its components, one

requires a way to deal with the fact that the observations (i.e., symptom reports) are likely to be imperfect indicators of these components (i.e., the actual symptoms). These commentators note that, if measurement error is neglected, relations between symptoms can be inaccurately represented because of attenuation effects. The only way to deal with this is to invoke latent variables into the model. Other commentators express this concern as well when discussing symptoms that should be measured in multiple ways (Krueger et al.; McFarland & Malta) or non-symptom causal processes that mediate the direct relations between symptoms (Belzung et al.; Danks et al.; Haig & Vertue; Humphry & McGrane). Our response is simply to acknowledge that this is the case; in fact, in our target article, we specifically hint at this idea in the last paragraph of section 4. We construct the situation as follows: At the level of individual symptoms, we take symptom reports to be measures. If measurement error is to be accounted for at this level, one would indeed need multiple indicators BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Cramer et al.: Comorbidity: A network perspective per symptom and a parallel extension of the network model with latent variables; for example, a network model for depression could include sleep disturbances as a latent variable measured with three observable indicators (i.e., clinical interview, polysomnography, laboratory observation; see McFarland & Malta). Figure R1 depicts such a network model with sleep disturbances and weight problems as latent variables. Also, a model in which some non-symptom causal processes are latent because they are measured in multiple ways (e.g., “major life events” for depression) is easy to conceive, and we welcome the development of such extensions of the model (Belzung et al.; Danks et al.; Haig & Vertue; Humphry & McGrane). The central tenet of our target article is, therefore, not to shun latent variables completely. For example, a measurement model that includes a latent variable makes perfect sense in case of the symptom “insomnia” with three indicators. This is because (1) a natural referent exists (i.e., not falling asleep/not staying asleep), of which we know (2) how it affects our three measurements (e.g., trouble with falling asleep will be measured as a long time lying awake before falling asleep for the first time during a nightly observation in the laboratory); and we know (3) that it explains the correlation between the three measurements (i.e., the common cause of measures obtained in a sleep laboratory and of ticking the box “long time to fall asleep” in a questionnaire). In case of mental disorders, on the other hand, a latent variable model is an unlikely candidate for giving a truthful explanation of the associations between distinct symptoms of a disorder. In other words, we do not object to measurement models per se, but to the idea that the association between a mental disorder and its symptoms is one of measurement. First, many supposed latent variables in psychological science – such as depression or neuroticism – do not appear to have a natural referent (for an extensive elaboration on this point, see Borsboom et al. 2009a). Second, without a natural referent, we have no idea how the supposed measurements would be affected by the latent variable, and we therefore cannot justify a common cause interpretation, where the disorder explains correlations between its symptoms. Thus, the things that render the relation between insomnia and three observed variables one of measurement are lacking in the case of, say, depression. Naturally, if one day we should find a natural referent for the hypothetical construct “depression,” and we could prove that referent to be the common cause of all depression symptoms, the network model would be disproved. But we doubt that day will ever come. R3. Refining the network approach: Genetics, brain, and cognition The network model in our target article is, naturally, not the end of the story (Ross). To the contrary, the network we presented for comorbidity between major depression and generalized anxiety represents a starting point. Refining this model in particular – and the network idea in general – should be the focus of future research in order to adequately (1) test the validity of the model and (2) generate hypotheses about the etiology of particular mental disorders (Johnson & Penke). 180

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Johnson & Penke correctly state that an important goal of the network model is to help unravel the etiology of a wide variety of mental disorders. We acknowledge that a plethora of work has already been done in that regard, but, as we also argued in our target article, that work might be grounded in the wrong psychometric theory of mental disorders. As such, etiology is currently interpreted in terms of the development of a single vulnerability (i.e., the common cause) that causes a cluster of symptoms. For example, an evolving lack of serotonin may be hypothesized to cause the symptoms of major depression. However, if a network approach, rather than a latent variable model, correctly describes the system, the conceptualization of etiology and vulnerability radically changes, for we are no longer talking about one, but about a multitude of vulnerabilities at the genetic, neurological, and cognitive levels that may explain the onset of symptoms and the relationships between them (Fleeson et al.; Hyland; Rubinsten & Henik; Yordanova et al.). Figure R1 depicts such a hypothetical descriptive network model for the nine symptoms of major depression. The etiology may then be conceptualized in terms of the development of such a network over time; naturally, this process may differ over individuals. Many mental disorders have a strong genetic component, as evidenced by high heritability estimates, but, despite numerous research efforts, the genetic culprits have not been found (Van der Sluis et al.). This poses a dilemma. Are the heritability estimates wrong – and is the genetic influence on mental disorders hence highly exaggerated – or is there something wrong with the methods we use to investigate this issue? Van der Sluis et al. suggest the latter and corroborate this by referring to the practice of correlating genes to the entire aggregate of symptoms. If the network model is accurate in describing the origins of mental disorders, this method provides limited prospects for success in gene hunting. Since, in this case, there simply is no common cause, its hypothesized proxy (i.e., a sum score) is an amalgam of distinct factors and will only capture the genetic components that are shared by the aggregated symptoms and relations between them. As we have argued in our target article, it is likely that different genes (or constellations of genes) influence different symptoms (and relations between them). For instance, it is not a wild guess to assume that the symptoms “sleep disturbances” and “thoughts of suicide” are controlled by a different set of genes (with some overlap; see Fig. R1). Multiple genes for each symptom separately does render the entire picture far more complex, and we agree with Van der Sluis et al. that the network model faces a challenge in that regard. Part of this complexity could possibly be tackled by examining the time series of symptom development and relating the patterns that emerge from such analyses to (constellations) of genes. While we generally reject the idea of one common cause underlying a constellation of symptoms, we by no means dismiss the potential relevance of pathological mechanisms discovered by the quest of finding such causes. For example, a smaller left hippocampal volume has been consistently found in people with major depression (e.g., see Bremner et al. 2000). Although it appears unlikely that this mechanism causes all depression symptoms, it could be one of the vulnerabilities underlying one or more symptoms; for instance, thoughts of suicide (see Fig. R1). Also

Response/Cramer et al.: Comorbidity: A network perspective at the neurological level, Rubinsten & Henik argue that deficiencies along the intraparietal sulcus (IPS) – commonly associated with numerical cognition – are the common cause of the symptoms of developmental dyscalculia (DD). Although we agree that the evidence points to the relevance of IPS deficiencies, we are not so sure that those deficiencies are the common cause. Since DD involves deficiencies in a variety of complex abilities that require input from memory, attention, and spatial systems, a single underlying vulnerability is highly unlikely (e.g., see Cohen Kadosh & Walsh 2009; Landerl et al. 2004). Thus, also in the case of DD, existing neurophysiological findings can be incorporated easily into a network perspective once one is willing to accept the demise of the “common cause” idea. At the cognitive level, it is, for instance, well known that both major depression and generalized anxiety are intimately connected to negative beliefs, as is evidenced by the success of cognitive therapy in reducing depression and preventing relapse (DeRubeis et al. 2005; Kuyken et al. 2008; Papageorgiou & Wells 2001; Paykel et al. 1999; Wells & Carter 2001; see also Hyland). We are skeptical about Hyland’s view that those beliefs form an interconnected system that completely explains the onset of depression and/or generalized anxiety. Rather, we hypothesize that negative beliefs directly influence (1) symptoms – for example, negative thinking that causes a depressed mood; and (2) relations between symptoms – for example, an overly negative interpretation of one’s depressed mood that results in making a suicide plan (see Fig. R1). Staniloiu & Markowitsch report another intriguing possibility: Problems in encoding and retrieving positive autobiographic memories could result in an inability to imagine an optimistic future, which may lead to the onset of the symptom “suicide attempt” (Markowitsch et al. 2003; Schacter & Addis 2009; Sharot et al. 2007). R4. How to investigate the network model? A research agenda We have provided several arguments for the thesis that a network model paints a more realistic picture of mental disorders than the latent variable model does. Naturally, future research must determine whether the network model is also the better theory in reality, and several commentators have put forward some excellent suggestions for a research agenda (e.g., Davis & Plomin; Fleeson et al.; Tzur-Bitan, Meiran, & Shahar [Tzur-Bitan et al.]). Given the complexity of the network approach, such an agenda is necessarily comprehensive. As such, when Krueger et al. ask, “How would one use the information in Figure 4 to explain to a policy maker how we might go about spending public funds wisely in the service of working to ameliorate the burden of depression and anxiety? By funding hundreds of separate projects focused on understanding each line in the figure?” – our short answer is yes. For those skeptical of this answer, we suggest that the same question may be asked about, say, complex systems like the earth’s climate. Should we really fund hundreds of projects investigating the diverse factors that influence climate change? The answer to that question is uncontroversially affirmative, and it has

not proven difficult to persuade policy makers of this fact. We do not see why the situation would be different for mental disorders. Given this perspective, we think of three lines along which network research should ideally be aligned: (1) validating the network model, (2) elucidating the vulnerabilities underlying (relations between) symptoms (see also Fleeson et al.) and (3) tracking the developmental trajectories of symptom constellations. R4.1. Validating the network model

Relations between symptoms represent an ideal opportunity to test the network model against the latent variable model: If no latent variable exists, one should find that experimentally manipulating one symptom results in change in another symptom. Some work has already been done in that regard; for example, unsurprisingly, one look at the literature reveals a direct effect of sleep deprivation on fatigue (e.g., see Durmer & Dinges 2005). Other symptom relations, such as the one between loss of interest and worrying about multiple events in Figure 4 of our target article, appear less obvious and need experimental verification in the future. In a more indirect manner, the network model could be confirmed by the genetic association studies (GAS) on the individual symptoms, as proposed by Van der Sluis et al.; it would be especially interesting to execute such analyses on patterns found in time series that describe symptom dynamics. If the network model is true, this type of GAS should reveal constellations of genes that better account for the high heritability of mental disorders than GAS on a sum score. In the same vein, Davis & Plomin suggest multidimensional scaling as a method to reveal the genetic closeness of multiple symptoms. If such endeavors would point to the presence of direct relations between symptoms, the latent variable model could be put to rest in psychopathology. R4.2. Elucidating vulnerabilities

Fortunately, there may be no need for funding “hundreds of projects,” as Krueger et al. fear, since many of such projects, aimed at understanding the inner workings of a variety of symptoms, have already been carried out; most symptoms in Figure 4 of our target article are associated with large scientific literatures (e.g., fatigue, anxiety). With regard to vulnerabilities underlying the relations between symptoms, not all edges are an a priori mystery to us; for example, the mechanisms that are involved in the influence of sleep deprivation on fatigue are well known (e.g., see Durmer & Dinges 2005). With regard to symptom relations whose underlying mechanisms are less well-known, insights from treatment rationales should further our understanding. For instance, mindfulness-based cognitive therapy offers a specific hypothesis with regard to the relation of depressed mood with the other symptoms of depression: Depressed mood triggers ruminative thinking, which – if not hindered by a successful intervention – could lead to other depression symptoms (e.g., see Ma & Teasdale 2004; Nolen-Hoeksema 2000; Segal et al. 2002). Another example comes from the panic disorder literature in which renewed interpretation of bodily signals is used to break the link between having a panic attack and worrying BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Cramer et al.: Comorbidity: A network perspective about its consequences (“I will have a heart attack”; e.g., see Clark et al. 1994). On a related note, several successful interventions are not primarily aimed at reducing or eliminating symptoms or the relations between them but, rather, at reinforcing so-called protective factors. For example, the relative success of the methadone program is attributable to reinforcing coping skills and finding work and housing (i.e., protective factors) while stabilizing the addiction with the methadone. Once a stable situation is created, addicts enter a total abstinence program (e.g., see Gossop et al. 2002; Van den Brink et al. 1999). Such treatment programs could provide some valuable insights into the mechanisms by which one progresses from a disordered to a healthy state. R4.3. Tracking developmental trajectories

Much of the current literature reports research that involves interindividual research, often carried out crosssectionally. Although such research can provide important insights, Wass & Karmiloff-Smith correctly suggest that it results in a snapshot of reality: an interindividual picture of mental disorders, frozen at a particular time frame. In reality, it is likely that, for instance, edge strengths differ across individuals, as well as across time. If so, another line of research is required to generate answers to two pivotal questions: (1) How do mental disorders develop, and (2) how does that development differ across individuals (Fleeson et al.; Rothenberger et al.). Such variations should be detectable through the intra-individual analysis of time series, as noted by various commentators (e.g., Fleeson et al.; Tzur-Bitan et al.; Van Geert & Steenbeek). In earlier times, it was quite difficult to obtain data suitable for such analyses. Fortunately, we now live in a time in which intensive time-series data can be gathered relatively easily (e.g., by letting patients report the status of symptoms through handheld devices, etc.). We think that, within a few years, it will become possible to analyze symptom development in real time, and to update network structures and parameters as the data come in. And when that time comes, we are confident that thorough investigation of the network approach will result in a better understanding of symptoms, their relationships, and their course in individuals over time. R5. Extending the network approach to other disorders In our target article, we introduced the network approach for two disorders that are prime examples of Axis I disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association 1994). Any theory that presents itself as the potential substantive theory of mental disorders must be able to explain more than comorbidity between major depression and generalized anxiety disorder (Johnson & Penke). As a first step, we deem it necessary to evaluate to what extent the network approach fits a variety of other mental disorders (also see Cervone). With regard to other Axis I disorders, some commentators have presented specific examples of (clusters of) disorders for which common causes are supposedly 182

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identified, thereby rendering the network approach invalid in those cases (e.g., Ross; Rubinsten & Henik). For example, Ross argues that addictions share a common cause: namely, hyperactivation of the dopamine reward circuit combined with weakened frontal and prefrontal serotonin and gamma-aminobutyric acid (GABA) circuits. We share Ross’s view on the importance of these brain pathologies in addiction; however, we do not agree that such pathologies automatically qualify as the common cause of addictions. The most commonly reported consequences of the dysfunctional dopamine, serotonin, and GABA circuits are (1) the strong desire to consume salient targets, coupled with (2) difficulty resisting that desire. In other words, the brain pathologies that Ross mentions result in the core characteristics of an addiction. However, does this make those brain pathologies the common cause of addiction? To qualify as such, those pathologies should also cause the other symptoms of addiction. This is unlikely. If we take a look at the DSM-IV criteria for substance abuse, for instance, we notice (1) the apparent inability of dysfunctional neurotransmitter circuits to explain “recurrent substance use resulting in a failure to fulfill major role obligations”; and (2) the undeniable possibility of direct relations between the symptoms of addiction: “Recurrent substance use in situations in which it is physically hazardous” (e.g., drunk driving) can cause “recurrent substance-related legal problems” (e.g., getting arrested for drunk driving). As such, we think addiction can potentially be envisioned as a causal chain of symptoms in which one symptom – desire to consume and inability to withstand this – may be triggered by dysfunctional dopamine, serotonin, and GABA circuits; thus, no common cause, but one pathological mechanism – in combination with other etiological factors – potentially results in a cascade of events in a network of addiction symptoms (i.e., the “fan-out” principle that Wass & Karmiloff-Smith mention). Such a chain of symptoms is also likely in panic disorder and other – very heterogeneous – Axis I disorders such as schizophrenia and attention-deficit hyperactivity disorder (ADHD). Hence, in these cases the network approach cannot be ruled out a priori (e.g., Borsboom 2008). Considering the extension of the network approach to Axis II disorders, Bornstein sees some roadblocks that need to be overcome in the case of personality disorders (PDs). First, patients with PDs tend to experience their symptoms as congruent with themselves. As a result, those patients have limited insight into their own condition. Bornstein rightly sees two resulting consequences: (1) Self-report measurements alone will not be adequate in assessing people with suspected personality pathology, and (2) the symptoms that patients cannot reflect on themselves are in a sense “latent.” However, we do not think these consequences pose serious problems for the network approach since – as we outlined in section R2 – it can easily deal with latent variables that have an established measurement relationship with a set of indicators, including tests that do not rely on self-assessment. Second, the revision of PD symptoms is founded on a desire to both increase diagnostic accuracy and reduce comorbidity. According to Bornstein (2003), this practice has resulted in simply removing symptoms from the diagnostic checklist, and, as Bornstein rightly claims, this poses

Response/Cramer et al.: Comorbidity: A network perspective a potential problem for the network approach; however, not in terms of its potential as substantive theory of mental disorders, but in terms of its practical applicability to PDs with potentially incomplete symptom inventories. So, in the case of Axis II PDs, we see no immediate problems that the network approach cannot surmount. R6. What is a mental disorder? In our target article, we argued that boundaries between mental disorders are necessarily fuzzy. In contrast, Haslam argues that boundaries between categories of the same disorder (e.g., “disordered” versus “not disordered”) are not fuzzy at all. To address this apparent dilemma properly, we dissect a disorder network in two components: (1) its structure and (2) its state. The structure of a disorder network refers to the strength of the relations between symptoms. As we show in Figure R1, those relations are controlled by a host of vulnerabilities (e.g., negative interpretation of one’s mood resulting in a relatively strong relation between depressed mood and thoughts of suicide). Since those vulnerabilities probably differ across individuals, it is safe to assume that the resulting basic network structure is individually tailored as well. Now, pertaining to comorbidity, it is likely that, in some cases, individual network structures do not obey the DSM boundaries between disorders (nor any other fixed boundaries). It is likely, as well, that certain vulnerabilities influence relations between symptoms of different disorders: for instance, ruminative thinking may strengthen the relation between “depressed mood” and “chronic anxiety.” As such, the boundary between major depression and generalized anxiety for someone with a ruminative thinking style probably (1) does not equal the DSMdefined boundary (because of a strong relation between “depressed mood” and “chronic anxiety”) and (2) lies somewhere else than the boundary of someone without that thinking style. Thus, at the individual level, the line can be drawn practically anywhere and therefore we defend the notion of fuzzy boundaries in these cases. In other cases, a sharp boundary between two disorders might be more feasible; for instance, because relations between symptoms of these disorders are virtually nonexistent or negative. For example, large individual differences in the boundary between social anxiety and psychopathy are not very likely given the opposite nature of the symptoms of those disorders (e.g., “excessive selfconsciousness and anxiety in everyday social situations” versus “grandiose sense of self-worth”; cf. Hare 2003). The state of a disorder network depends on how much symptoms are “on.” When adhering to a categorical perspective, disorder networks can be in two or more stable states. For example, with two stable states, one commonly distinguishes between a healthy state, in which few symptoms are “on,” and a disordered state, in which several symptoms are “on.” In these cases, a sharp boundary is needed to distinguish few from several. Now, we agree with Haslam that such sharp boundaries are theoretically possible and that evidence for two latent classes corroborates that hypothesis (provided that the analysis was conducted on a large and representative sample). However, as we already argued for a network’s structure, it is unlikely that boundaries between states are invariant over persons; for, in subjective

terms, some people feel depressed because they have sleep and concentration problems for two weeks, whereas others succumb to a full-blown depression only after a prolonged period of experiencing a multitude of symptoms. Therefore, in these cases, a more dimensional perspective might be in order; that is, no sharp boundaries between categories, but, instead, a continuum of network activation. Here, we think that symptom severity might be an excellent candidate for representing the degree of network activation (Markus): the more severe someone’s symptoms are, the more that person is located toward the “disordered” end of the continuum. In theory, any network with connected nodes (i.e., structure) that can be in different states could be taken to qualify as a mental disorder. As such, liberalism could be viewed as a mental disorder (Zachar): a set of connected political beliefs (e.g., if you believe in freedom of religion for everyone, then it is more likely that you are tolerant of minorities) that we call “liberalism” when a sufficient number of nodes are activated. In practice, though, we – and probably the majority of humankind with us – do not consider liberalism to be a mental disorder. Why? The DSM provides a sensible answer: The symptoms of any candidate mental disorder should cause “clinically significant distress or impairment in social, occupational, or other important areas of functioning” in the person who is experiencing those symptoms (American Psychiatric Association 1994). Although liberalism apparently causes distress in some other people (see Savage 2005), it clearly does not satisfy the DSM’s prerequisite. Thus, providing a sensible boundary between disorders and non-disorders, we would welcome this prerequisite as an extra node in the symptom space. About 40% of people with major depression experience a new depressive episode after treatment (e.g., Paykel 2008). Any substantive theory of mental disorders must be able to explain such recurrence, a phenomenon that is very common in a host of mental disorders. In our opinion, the network approach is up to that task. Take, for instance, an alcoholic who, because of treatment, manages to stay sober, as a result of which the other symptoms of his or her substance abuse also subside. Also suppose that this person’s network has strong connections between symptoms; that is, if one symptom turns on, it is likely that the other symptoms will turn on, as well. As such, we have a situation in which the substance abuse network is in a more or less healthy state (i.e., no symptoms are “on”) while the structure of the network is risky (and thus unhealthy). Now, this situation is exactly what makes a disorder likely to recur: If, for whatever reason, this person decides to drink one beer, it will likely result in a cascade of symptoms being turned on, and eventually the network will return to a disordered state. In other words, recurrence is most likely when the healthy state of a disorder network is unstable because of the strong connections between its symptoms. We think this is precisely what clinicians mean when they talk about silent disorders, and therefore we do not agree with Hood & Lovett that the network approach cannot accommodate such notions. On a final note, in the case of major depression, it is established that one of the most reliable predictors of recurrence is the presence of residual symptoms (e.g., Kennedy & Paykel 2004). But we also know that not every patient with residual BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Cramer et al.: Comorbidity: A network perspective symptoms experiences a subsequent recurrence. If we are right in suggesting that recurrence is most likely when the structure of the network is strong, residual symptoms in depression patients offer a way to prove this hypothesis: Of patients with residual symptoms, only those with strong connections between symptoms should eventually experience a new episode of major depression. R7. Networks versus common causes: Methodological issues Several commentators raise methodological issues regarding the network approach as opposed to latent variable models. In the following, we discuss criticisms according to the methodological topics mentioned by the commentators. R7.1. Local independence

Many commentators question our criticism of the local independence assumption. In their opinion, a unidimensional model with local independence is unnecessarily strict (e.g., Humphry & McGrane; Markus; Molenaar). It is true that violations of local independence can be represented in a latent variable model, for instance, by allowing correlated residuals or direct relations between indicator variables. However, these modeling possibilities should not be given too much conceptual weight. Being more than a convenient restriction, local independence has the status of an axiom in measurement models used in psychometrics (e.g., Ellis & Junker 1997; Holland & Rosenbaum 1986; Junker & Sijtsma 2001). This makes sense because psychometric models aim to give conditions under which composite scores (e.g., summed item scores) can be treated as measures of a latent variable. A prerequisite for this is that the item scores measure the same latent variable, which plausibly requires that the latent variable functions as a common cause; and the classical way of testing this is by testing whether the latent variable screens off the associations between the item scores. This is precisely what local independence requires. Thus, although it is statistically possible to allow for direct relations between indicator variables in a model, this should be considered a deviation from a psychometric norm (which in itself is reasonable in setting up a measurement model). As such, a unidimensional model with local independence is anything but a “straw man” (Danks et al.). R7.2. Model equivalence

Several commentators raise the possibility that we may have overstated the difference between networks and latent variable models. Danks et al. note that cyclic graphs and latent variable models are closely related; Molenaar points to the fact that longitudinal factor models are equivalent to specific types of directed network models; and Humphry & McGrane indicate that latent variable models concern individual differences and, as such, may allow for individual level causal relations without violating the individual differences model. It is true that latent variable models and network models are statistically indistinguishable in certain situations. A 184

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prominent example of such an exact indistinguishability is the mutualism model of intelligence proposed by Van der Maas et al. (2006), which is a network model that can produce data that are exactly equivalent to a single factor model. Similar relations are likely to exist for item response theory (IRT) models; Molenaar, in earlier work (see Molenaar 2003, p. 82) has noted the close relation between Markov field models, such as the Ising model, and IRT models like those of Rasch (1960) and Birnbaum (1968). Indeed, one supposes that model equivalence may obtain as well in those cases. Does this render the network model and the latent variable model equivalent in general? No, because the inability to distinguish between different possible generating models in a given data-set does not imply that the models are equivalent with respect to all possible datasets or under all possible interventions. Thus, the advice in a model equivalence situation is to get better data, such as intensive time series (see sect. R4). R7.3. Parsimony

Krueger et al. defend the latent variable model by emphasizing its superior parsimony relative to the network approach. First, latent variable models are not inherently more parsimonious than network models because the number of parameters of the latter can be made arbitrarily small. For instance, suppose that one has k observed dichotomous symptoms. If one assumes a completely connected network consisting of bidirectional relations of equal size, where these relations are functionally the same for any two nodes (e.g., logistic relations with equal intercepts and slopes, as in a Boltzmann machine; see Ackley et al. 1985), then, statistically speaking, one has an extremely parsimonious model even though it may consist of many – namely, k(k – 1)/2) – connections between variables. Second, it should be recognized that even though parsimony is a useful criterion in choosing between statistical models, it will lead to truth only if reality itself is simple; if this is not the case, then we may deceive ourselves by overemphasizing parsimony. As Tryon (1935, p. 428) remarked, “The ‘law’ of parsimony is not a natural law, but a rule agreed upon among men to simplify their thinking.” While simplifying our thinking is clearly useful in scientific investigation, complex realities will ultimately require complex models. In the case of mental disorders, we doubt that reality is simple given the likelihood of variation in network structure over individuals and time. As such, an extremely restricted model such as a Boltzmann machine – although favorable in terms of its parsimony – might not be particularly viable. Therefore, we think that the sword of parsimony should be wielded with caution, for we may accidentally kill promising candidate models through its use. R7.4. Extensions of the network approach

Danks et al. provide one of the most critical analyses of our approach. First, they raise a number of questions concerning terminology and procedure. For instance, they criticize our use of the term centrality because “[centrality] is neither a causal nor a statistical notion.” This is obviously correct; it is a notion that comes from network analysis

References/Cramer et al.: Comorbidity: A network perspective and has proved to be useful in many contexts (e.g., see Boccaletti et al. 2006). Danks et al. also question our statement that observables in a standard psychometric latent variable model are exchangeable. In a measurement model, observables do not differ with respect to the property they measure; they are thus exchangeable in this sense. And it is this exchangeability that – among other things – renders the standard measurement model inappropriate in the context of psychopathology, for how could “weight loss” measure the same property as “suicide plans”?1 Finally, Danks et al. indicate that the data we analyzed involved a great amount of missingness. We agree but refer to Note 6 of our target article, where we highlight an appropriate estimation approach we used to deal with the data, which is missing at random because of the skip structure of the interview schedule used in the National Comorbidity Survey Replication (NCS-R). Second, Danks et al. state that we “do not engage what is known” about the investigation of causal relations, instead settling for an unsatisfactory and unrestrictive visualization method. They propose that causal inference algorithms should be used instead and report the outcome of an algorithmic search procedure. Perhaps ironically, the use of such procedures formed the starting point of our research. However, the search procedures as implemented in the program TETRAD (Scheines et al. 1996) returned causal structures that we felt were extremely hard to make sense of. This is also the case for the model suggested by Danks et al., in which, for instance, the core symptoms of depression and generalized anxiety (i.e., depressed mood and anxiety) are completely disconnected from the model. Our diagnosis of this situation is that two assumptions of the search algorithms in existence are not satisfied in the data at hand: (1) Individuals have the exact same causal structure and (2) resulting graphs are acyclic. In contrast, we think that the network structure of mental disorders (1) varies over individuals and (2) likely contains feedback loops. Therefore, we judge the implementation of causal search algorithms to be preliminary; it would be more sensible to gather time-series data on symptom dynamics and to fit models on an intra-individual basis. However, what we can do unproblematically, absent such intensive timeseries data, is to provide a starting point for further investigations and hypothesis formation, based on the visualization of statistical associations that exist in the data, and this is what we aimed to do. This does not commit us to any particular type of modeling, while it serves the purpose of introducing and explaining the network approach extremely well. In conditions that justify their use, however, we acknowledge that causal modeling and search algorithms may be very useful. NOTE 1. The exchangeability of items with respect to the property they measure is clear from the fact that one can parameterize, for instance, standard IRT models such as the one- and two-parameter logistic models by identifying the latent variable with the expectation of any one of the item responses (Gunter Maris, personal communication). A similar situation holds for the (essentially) tau-equivalent model of classical test theory (Lord & Novick 1968), in which the expectations of observed variables are simple transformations of one another, and for the congeneric model of factor analysis, in which the observed variables are linear transformations of one another (Jo¨reskog 1971).

Intuitively, this means that if one has a single perfect thermometer, adding information from other, noisy thermometers is useless (note that this makes sense in a measurement situation). In contrast, if one knew the expectation of the item “how much weight have you lost?” one would presumably still want to know whether the person had suicide plans.

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Pre´cis of Doing without Concepts Edouard Machery Department of History and Philosophy of Science, 1017CL, University of Pittsburgh, Pittsburgh, PA 15260 [email protected] www.pitt.edu/machery/

Abstract: Although cognitive scientists have learned a lot about concepts, their findings have yet to be organized in a coherent theoretical framework. In addition, after twenty years of controversy, there is little sign that philosophers and psychologists are converging toward an agreement about the very nature of concepts. Doing without Concepts (Machery 2009) attempts to remedy this state of affairs. In this article, I review the main points and arguments developed at greater length in Doing without Concepts. Keywords: categorization; concept; concept combination; dual-process; eliminativism; exemplar; induction; meaning; natural kinds; prototype; reference; theory

The study of concepts is in an odd state of disarray. Cognitive scientists working on categorization, induction, and reasoning have discovered a dazzling amount of phenomena. New work on prototypes in the 1990s and early 2000s, innovative ideas on causal cognition in the first decade of the twenty-first century, the development of the neo-empiricist approach that assimilates the tokening of a concept to a multi-modal perceptual simulation, and the promising growth of the neuropsychology of concepts have rejuvenated the field. At the same time, this extraordinary amount of findings has yet to be organized in a coherent theoretical framework. The current theories of concepts – prototype theories, exemplar theories, theory theories, and neo-empiricist theories – fail to explain all the known phenomena, and there is very little agreement about what concepts are. Doing without Concepts (Machery 2009) attempts to provide such a theoretical framework. In this article, I review the main points and arguments developed at greater length in the book, and I conclude that abandoning the very notion of concept is probably required to remedy the state of disarray of the current psychology of concepts. 1. Regimenting the use of concept in cognitive science Because cognitive scientists rarely spell out the notion of concept in detail, I begin by making explicit the notion of concept that is typically used within cognitive science. My goal in chapters 1 and 2 is threefold: To clarify this notion, to regiment the use of the term concept, and to show that philosophers and cognitive scientists theorize about different things when developing theories of concepts. The cognitive processes that underwrite cognitive competences are typically assumed to access some relevant information or knowledge. Some bodies of information are only accessed by particular processes: For instance, our implicit knowledge of the syntax of the natural languages we speak (e.g., English) is only accessed by the processes involved in parsing and in producing # Cambridge University Press 2010

0140-525X/10 $40.00

sentences. When this is the case, I will say that the relevant body of information is “proprietary to a particular cognitive process.” By contrast, some information is “non-proprietary”: It is accessed by the cognitive processes that underlie several distinct cognitive competences. Cognitive scientists often assume that the cognitive processes underlying our higher cognitive competences access the same bodies of knowledge. For instance, the processes underlying categorization, induction, and speech are hypothesized to access the same body of knowledge about dogs when people classify something as a dog, when they make an induction about dogs, and when they understand sentences containing the word dog. This knowledge is assumed to be stored in long-term memory. These preliminary points having been made, I propose to characterize the notion of concept as follows: Within cognitive science, a concept of x is a body of information about x that is stored in long-term memory and that is used by default in the processes underlying most, if not EDOUARD MACHERY is Associate Professor of History and Philosophy of Science at the University of Pittsburgh, a resident fellow of the Center for Philosophy of Science (University of Pittsburgh), a member of the Center for the Neural Basis of Cognition (Carnegie Mellon University and University of Pittsburgh), and an associate editor of the European Journal for Philosophy of Science. His research focuses on the philosophical issues raised by psychology and cognitive neuroscience with a special interest in concepts, moral psychology, the relevance of evolutionary biology for understanding cognition, modularity, the nature, origins, and ethical significance of prejudiced cognition, and the methods of psychology and neuroscience. Machery has published more than 50 articles and chapters on these topics in venues such as Analysis, The British Journal for the Philosophy of Science, Cognition, Mind & Language, The Monist, Philosophy and Phenomenological Research, and Philosophy of Science. He is also involved in the development of experimental philosophy, having published several noted articles in this field.

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Machery: Pre´cis of Doing without Concepts all, higher cognitive competences when they result in judgments about x. I call this characterization “C.” It is important to highlight some significant properties of concepts, so understood. First, concepts can be about classes of objects (e.g., DOG ), events (e.g., GOING TO THE DENTIST ), substances (e.g., WATER ), and individuals (e.g., BARACK OBAMA ). Second, concepts are non-proprietary: DOG is used by the processes underlying categorization, induction, linguistic understanding, metaphor building, planning, and perhaps other competences. Third, the elements of information that are constitutive of a concept can vary over time and across individuals. Fourth, it might be unclear whether a given element of information about x belongs to a concept of x. Finally, concepts are used by default in the cognitive processes underlying higher cognitive competences (I call “Default” the hypothesis that some bodies of knowledge are retrieved by default when one is categorizing, reasoning, drawing analogies, making inductions, and so on). This entails that our concept(s) of, say, dogs is (are) only a subset of our whole knowledge about dogs: The knowledge that is constitutive of DOG is the knowledge about dogs that is retrieved by default from long-term memory when we reason about dogs, categorize things as dogs, and so forth. I call “background knowledge” about dogs the knowledge about dogs that is not part of the concept(s) of dogs. Our background knowledge about a category, a substance, some kind of events, and the like, can be called upon occasionally when the default body of knowledge is insufficient to solve a cognitive task. At this point, it is useful to spell out the notion of being used by default at greater length. A default body of knowledge about x is the body of knowledge that is presumptively taken to be relevant when one reasons about x, when one categorizes things as x, and so on. The knowledge that is stored in a concept of x is preferentially available when we think, reason, and so forth, about x. So to speak, it spontaneously comes to mind. The proposed characterization of the notion of concept captures much of what is implicit in the use of the term concept in cognitive science. However, it is also clear that C is partly at odds with some characterizations of the notion of concept found in the literature, which are discussed at length in Chapter 1 of Doing without Concepts. In any case, with the proposed characterization of the notion of concept, I do not merely aim at capturing the use of concept in cognitive science. I also want to regiment it: I contend that some bodies of knowledge are retrieved by default from long-term memory when one reasons, categorizes, and so forth (see section 3 for a defense of this claim), and I propose that the term concept should be used to refer to these bodies of knowledge. 2. Individuating concepts It is certainly possible for a given individual to have several concepts of the same category (e.g., several concepts of chairs) or of the same substance (e.g., several concepts of gold): For instance, one might think of chairs in several distinct ways, each of which corresponds to a different concept of chairs. This possibility raises the following question, which is examined in Chapter 3 of Doing without Concepts: What does it mean for two bodies of knowledge about x (e.g., the knowledge that 196

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water is typically transparent and the knowledge that water is made of molecules of H2O) to be part of the same concept of x (WATER ) rather than of two distinct concepts (WATER 1 and WATER 2)? I propose the following individuation criteria (respectively, Connection and Coordination). When two elements of information about x, A and B, fulfill either of these criteria, they belong to two distinct concepts: 1. If retrieving A (e.g., water is typically transparent) from long-term memory and using it in a cognitive process (e.g., a categorization process) does not facilitate the retrieval of B (water is made of molecules of H2O) from long-term memory and its use in some cognitive process, then A and B belong to two distinct concepts (WATER 1 and WATER 2). 2. If A and B yield conflicting judgments (e.g., the judgment that some liquid is water and the judgment that this very liquid is not water) and if I do not view either judgment as defeasible in light of the other judgment (i.e., if I hold both judgments to be equally authoritative), then A and B belong to two distinct concepts (WATER 1 and WATER 2). Let’s clarify these two criteria a bit further. Connection unpacks the idea that the components of a given concept must be connected (or “linked” as I say in Doing without Concepts): If the beliefs that water is typically transparent and that water is made of molecules of H2O are part of the same concept, WATER , then using the first belief to classify some sample as water enables me to conclude that it is made of molecules of H2O. Accessing one part of a concept makes the other parts accessible and poised to be used in cognitive processing (for related discussion, see Millikan 2000, Ch. 10). Coordination expresses the idea that the parts of a single concept should not result in conflicting judgments that are both taken to be equally authoritative. Coordination is compatible with the parts of a given concept yielding conflicting judgments, provided that all but one are viewed as defeasible. To illustrate, although the belief that grandmothers have gray hair and the belief that grandmothers are mothers of parents would yield different judgments if they were used to categorize a young-looking grandmother (e.g., Sarah Palin), they could still both be part of the concept GRANDMOTHER if one of the two judgments (Sarah Palin is not a grandmother) is defeated by the other one (Sarah Palin is a grandmother). In effect, the first belief would be treated as a heuristic that sometimes leads us astray. These two criteria merely explain what it is for a given individual to have one or several concepts about some class of objects, some substance, and so on (withinperson individuation). It does not explain what it is for two or more individuals to have the same concept of x (between-person individuation). Now, one might expect a theoretician of concepts to provide individuation criteria for this situation, too. However, I doubt that these are needed for the psychology of concepts, since they seem to play no explanatory role in psychology. 3. Defending the proposed notion of concept Some cognitive scientists and philosophers of psychology reject Default (viz., the assumption that some bodies of knowledge are retrieved by default when one is categorizing, reasoning, drawing analogies, and making inductions).

Machery: Pre´cis of Doing without Concepts In Chapters 1 and 8 of the book, I rebut the criticisms mounted against this assumption. Several findings suggest that typicality varies across contexts. Roth and Shoben (1983) have shown that depending on the linguistic context (e.g., when participants are presented with “Stacy volunteered to milk the animal whenever she visited the farm” or “Fran pleaded with her father to let her ride the animal”), participants judge that different animals are typical (cows and goats for the first linguistic context, horses and mules for the second). Similarly, Barsalou (1985) reports that judgments of typicality vary across contexts. In Study 2, participants’ typicality judgments about the members of two groups differed when these groups were conceptualized differently (as physical education teachers and current event teachers, on the one hand, and as two invented types of programmers – namely, Q programmers and Z programmers – on the other hand). This study also shows that when participants are familiarized with a given category in different contexts, their judgments of typicality vary. Barsalou (1987; 1993) also reports that the typicality of objects varies when participants are asked to take different points of view on these objects. For example, people judge differently the typicality of birds when they take the point of view of someone from China and when they report their typicality evaluation from their own point of view. In addition, the correlation between typicality judgments across participants is low (circa .5) and lower than expected for a given subject on two different occasions (around .8). Theorists have used such findings to challenge the idea that some bodies of knowledge are retrieved by default when one categorizes, draws inductions, reasons, and the like. Barsalou (1985) concludes that: People may not retrieve the same concept from long-term memory every time they deal with a particular category. Instead they may construct a diverse variety of concepts in working memory to represent a particular category across different situations such that the concept used to represent a category is rarely, if ever, the same. According to this view, long-term memory does not contain invariant concepts. (Barsalou 1985, p. 646)

(For similar arguments, see also Barsalou 1987; Smith & Samuelson 1997, p. 170.) Theorists who reject Default have drawn two distinct conclusions. As discussed in Chapter 8 of Doing without Concepts, some theorists, such as L. Smith (Smith & Samuelson 1997), contend that Default is part of the very notion of concept, and they conclude that, since there are no such things as bodies of knowledge retrieved by default from long-term memory, there are no such things as concepts. Smith and Samuelson (1997, p. 190) conclude that “a successful theory of categories [. . .] might require that we give up timeless abstractions such as concepts.” While agreeing that Default is part of the notion of concept typically used in cognitive science, other theorists, such as Barsalou and Prinz, propose to redefine the notion of concept. Concepts should be thought of as the bodies of knowledge in working memory that are used at a given time in a given task: They are constructed on the fly to deal with the peculiarities of the task at hand; and they typically vary from time to time (Barsalou 1993, p. 29; Prinz 2002; Malt & Sloman 2007). Though Barsalou (1993), Prinz (2002), L. Smith (see Smith & Samuelson 1997), and Malt and Sloman (2007)

take the body of evidence reviewed above to establish either conclusion, I demur on three grounds. First, the hypothesis that some bodies of knowledge are retrieved by default from long-term memory and used in the processes underlying the higher cognitive competences is consistent with some variation in the bodies of knowledge that are used at any given time. This variation can have two sources. When we reason about x, in addition to the default body of knowledge about x, we sometimes retrieve some specific elements from our background knowledge about x. In addition, once retrieved from memory, the body of knowledge that is retrieved by default can be tailored to the peculiarities of the given situation. On this view, knowledge retrieval would be a two-step procedure: (1) Retrieve the default body of knowledge from longterm memory; (2) tailor it to the situation. (Sperber & Wilson [1998] present similar ideas.) Thus, the mere fact that performances in experimental tasks vary from time to time does not show that there are no bodies of knowledge retrieved by default from long-term memory. What would not be consistent with Default is a very large variability across contexts of the knowledge brought to bear on tasks. And, indeed, Barsalou claims that there is a “tremendous variability in performances . . . not only in category membership, but also in typicality, definitions, and probably most other categorization tasks” (1993, p. 34; my emphasis). However, as we shall now see, the relevant variability in performances is in fact moderate. Second, the nature of the variation found by Barsalou, Malt, Sloman, and others is either irrelevant to evaluate Default or supportive of it. Let us consider first the pieces of evidence that turn out to be irrelevant to evaluate Default. Many findings about the context-sensitivity of typicality are misleading. Roth and Shoben’s (1983) findings merely show that (unsurprisingly) people evaluate differently the typicality of target animals with respect to the category of animals that get milked and the category of animals one uses to ride. That typicality varies when evaluated from different points of view (Barsalou 1993), does not show that people’s concepts vary across occasions, since, in effect, one asks participants not to use their own concept of x to complete a task when one asks them to make judgments about x from someone else’s point of view. Similarly, the variability of the typicality judgments made by different individuals says nothing about whether a given subject retrieves a default body of knowledge across occasions. Showing, as Barsalou (1985) does, that the typicality structure of a given class of objects can substantially vary when it is conceptualized differently (viz., as physical education teachers and current events teachers, on the one hand, and as Q programmers and Z programmers, on the other hand) is interesting; but this finding does not show that the concept of a given category varies across contexts and circumstances because, properly speaking, current events teachers and Z programmers are two distinct categories, although they are composed of the same individuals. Showing that the typicality structure of a given category varies when people are familiarized with this category in different situations is also interesting; but again, it says little about whether someone who is familiarized with a given category one way will rely on a default body of knowledge about this category. In addition, some results that allegedly BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Machery: Pre´cis of Doing without Concepts undermine Default in fact support it. As noted, Barsalou (1987; 1993) reports that, on average, the test-retest reliability of typicality judgments is at least .8. It is also higher when participants are re-tested one hour and a day after the first test. Furthermore, Barsalou reports that the typicality of highly typical and atypical items does not change much. These results are evidence that, across occasions, a default concept is retrieved from long-term memory. Finally, a large body of evidence supports Default. Consider linguistic understanding (Ziff 1972, discussed in Murphy & Medin 1985). The sentence, “A cheetah can outrun a man,” is meaningful, and most people would agree with it. However, as Murphy and Medin put it (1985, p. 303), it is true only if the represented cheetah is not “a 1-day old cheetah, or an aged cheetah with arthritis, or a healthy cheetah with a 100-pound weight on its back.” But when we read, “A cheetah can outrun a man,” these representations of cheetahs do not come to mind. This phenomenon suggests that when a speaker utters “A cheetah can outrun a man,” or when a hearer or a reader understands this sentence, she retrieves from memory a default body of knowledge about cheetahs. Perhaps one will object that when one reads the sentence, “A cheetah can outrun a man,” one merely constructs a context-appropriate interpretation of “cheetah” rather than retrieving a default body of knowledge about cheetahs. If this were true, then people would also construct a context-appropriate representation of cheetahs if they had to decide whether the sentence, “A man can outrun a cheetah,” is true. They would, for example, imagine an old, three-legged cheetah, and the sentence, “A man can outrun a cheetah,” would then be judged true too. However, I predict that, under time pressure, people would judge the sentence, “A man can outrun a cheetah,” to be false. This would be evidence that in such conditions they retrieve the very default body of knowledge they retrieve when they read, “A cheetah can outrun a man.” Naturally, with no time pressure, they could construct an interpretation of cheetah under which the sentence, “A man can outrun a cheetah,” is true. But this is consistent with the existence of default bodies of knowledge, because, as I have already proposed, people can and do retrieve some additional information (viz., some information not contained in their concepts) from their background knowledge. Behavioral studies also show that some information about a category, substance, and so on, is retrieved automatically in every context (Barsalou 1982; Whitney et al. 1985). Barsalou (1982) found that when people judge that a property (e.g., stinks) spontaneously “comes to mind” when they read a given noun (e.g., “skunk”), reaction times in a property-verification task are similar when the noun is presented in a relevant linguistic context (“The skunk stunk up the entire neighborhood”) and when it is presented in an irrelevant linguistic context (“The skunk was under a large willow”). By contrast, reaction times are larger in the latter condition (“The roof had been renovated prior to the rainy season”) than in the former condition (“The roof creaked under the weight of the repairman”) when people judge that a property (e.g., can be walked upon) does not spontaneously come to mind when they read a given noun (e.g., “roof”). Barsalou calls the first kind of property 198

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“context-independent” and the second kind “contextdependent.” Cognitive neuroscience provides further evidence in support of Default (although the relevant studies were not developed to test this hypothesis). After having trained participants with novel tools, Weisberg et al. (2006) recorded brain activation in a perceptual task (a visual matching task). To complete this task, one needs only appeal to some structural information about the shape of the novel tools; thus, one would expect the medial portion of the fusiform gyrus to be activated (for review, see Martin 2007). Interestingly, activation was also recorded in the intraparietal sulcus, the premotor cortex, and the medial temporal gyrus, areas of the brain that are known to store information about the typical movements associated with tool use. It thus seems that the perceptual task resulted in the automatic retrieval of information that was not needed to solve the task, consistent with the idea that people have bodies of knowledge that they retrieve by default (for similar findings, see also James & Gauthier 2003; Hoenig et al. 2008). 4. Developing a psychological theory of concepts It is important to keep in mind that the notion of concept proposed earlier (viz., C) does not amount to a theory of concepts. Rather, C does two things: It spells out what is implicit in cognitive scientists’ use of the term concept, and it proposes to regiment this use. So, what does a psychological theory of concepts consist in? As I explain in Chapter 1 of Doing without Concepts, psychological theories of concepts typically attempt to identify the properties that are typical of concepts (“the general properties of concepts”). Five kinds of properties are of interest to cognitive scientists. First, cognitive scientists are interested in the nature of the information that is constitutive of concepts. For instance, cognitive scientists want to know whether concepts consist of some statistical information about the properties that are characteristic of a class or of a substance, as prototype theorists have proposed (e.g., Hampton 1979; 1981; 2006; 2007; Smith 2002), or whether they consist of causal generalizations (e.g., Gopnik & Meltzoff 1997; Griffiths et al. 2007; Murphy & Medin 1985; Rehder 2003a; Tenenbaum et al. 2007). Second, cognitive scientists are interested in the nature of the processes that use concepts. For instance, some psychologists have argued that these processes are based on similarity (e.g., Hampton 1993), while others disagree (e.g., Rips 1989). Third, cognitive scientists develop hypotheses about the nature of the vehicles of concepts: Thus, neo-empiricists such as Barsalou and Prinz contend that the vehicle of concepts is similar to the vehicle of perceptual representations (Barsalou 1999; 2008b; 2009; Machery 2006c; Prinz 2002; 2005). Fourth, for about a decade, cognitive scientists have attempted to identify the brain areas that are involved in possessing concepts (for reviews, see, e.g., Mahon & Caramazza 2009; Martin 2007; Pulvermu¨ller 2005). Finally, cognitive scientists have developed hypotheses about the processes of concept acquisition (e.g., Ashby & Maddox 2004; Gopnik 2003). In addition to developing hypotheses about the general properties of concepts, cognitive scientists have shown

Machery: Pre´cis of Doing without Concepts some interest in distinguishing different types of concepts and in identifying the properties of these types of concepts. Medin et al. (2000) have rightly insisted on the importance of this task and on its relative neglect by cognitive scientists. Why do cognitive scientists want a theory of concepts? Theories of concepts are meant to explain the properties of our cognitive competences. People categorize the way they do, they draw the inductions they do, and so on, because of the properties of the concepts they have. Thus, providing a good theory of concepts could go a long way toward explaining some important higher cognitive competences.

by virtue of having specific bodies of knowledge about them. For instance, prototype theorists do not need to hold, and typically do not hold, that having a prototype is a condition for being able to have attitudes about the objects of our attitudes. In fact, prototype theorists are silent on this question. The upshot of this argument should be clear. Although both philosophers and cognitive scientists use the term concept, they are not talking about the same things. Cognitive scientists are talking about a certain kind of bodies of knowledge, whereas philosophers are talking about that which allows people to have propositional attitudes. Many controversies between philosophers and psychologists about the nature of concepts are thus vacuous.

5. Concept in cognitive science and in philosophy The term concept is used in philosophy, particularly in the philosophy of mind, as well as in cognitive science. Chapter 2 of Doing without Concepts examines the relation between these two uses. It is common among philosophers to assume that concept is used in the same sense in philosophy and in cognitive science and that psychologists’ theories of concepts aim at answering the issues philosophers are interested in (Edwards 2009; Fodor 1998; 2008; Laurence & Margolis 1999; Margolis 1994; 1995; Margolis & Laurence 2006; Rey 1983; 1985; 2009b). In addition, it is common to hold that as answers to the issues of interest in philosophy, psychological theories of concepts are defective. Thus, Fodor (2003) concludes his review of Gregory Murphy’s book, The Big Book of Concepts, as follows: It is part of our not knowing how the mind works that we don’t know what concepts are or what it is to have one. Just about everything that current cognitive science says about either topic is wrong .. . . Gregory Murphy’s book tells you most of what there is to the psychology of concepts. Read it, therefore, by all means; but don’t even consider believing it. (Fodor 2003, p. 4)

It is also not uncommon to see some philosophical theories criticized for being unable to explain how we categorize, make inductions, and so on (Prinz 2002; for discussion, see Edwards 2009). Philosophers’ take on psychological theories is mistaken: Philosophical and psychological theories of concepts are not meant to answer the same questions and are thus not competing. Typically, by concept, philosophers refer to that which allows people to have propositional attitudes (beliefs, desires, etc.) about the objects of their attitudes. The concept of a triangle is therefore that which allows people to have propositional attitudes (beliefs, desires, etc.) about triangles. A theory of concepts in philosophy attempts to determine the conditions under which people can have propositional attitudes about the objects of their attitudes (Fodor 1998; 2008; Peacocke 1992; 2008;), but not to explain the properties of our higher cognitive competences. By contrast, psychologists attempt to explain the properties of our categorizations, inductions, and so forth, but they do not attempt to determine the conditions under which people are able to have propositional attitudes about the objects of their attitudes. Furthermore, psychologists do not need to hold, and typically do not hold, that we are able to have propositional attitudes about the objects of our attitudes

6. The heterogeneity hypothesis versus the received view Cognitive scientists of concepts naturally acknowledge differences between concepts: The concept of dogs is clearly different from the concept of cats. More interesting, they also acknowledge differences between kinds of concepts: For instance, there has been much work in experimental and developmental psychology on the differences between the concepts of animals and the concepts of artifacts (e.g., Bloom 1996; Gelman 1988; 2003; Gelman & Markman 1986; Malt & Sloman 2007). But above and beyond these differences, cognitive scientists often assume that concepts share many properties that are scientifically interesting. In Chapter 3 of Doing without Concepts, I call this assumption “the received view.” It is well expressed by Gregory Murphy: The psychology of concepts cannot by itself provide a full explanation of the concepts of all the different domains that psychologists are interested in.. . . The details of each of these must be discovered by the specific disciplines that study them. . .. Nonetheless, the general processes of concept learning and representation may well be found in each of these domains. (Murphy 2002, pp. 2 –3)

The received view has been instrumental in the debates that have marked the history of the psychology of concepts since the 1970s. Cognitive scientists who are committed to different theories of concepts (say, a particular prototype theory and a particular exemplar theory) have attempted to discover properties of our higher cognitive competences (e.g., the exemplar effect reported in Medin & Schaffer 1978) that were easily explained by the theory they endorsed (e.g., the exemplar theory), but were not easily explicable by the competing theory (prototype theories do not naturally explain the exemplar effect; for discussion, see Smith & Minda 2000). This research strategy makes sense only if one supposes that a single theory of concepts should be able to account for all the relevant phenomena. If, contrary to the received view, the class of concepts divides into several kinds that have little in common, the distinct theories of concepts that characterize these kinds of concepts will account for different phenomena, and the fact that theory A, but not theory B, explains some phenomenon, such as the exemplar effect, will not necessarily constitute evidence against theory B. As I explain in Chapter 3, the received view stands in sharp contrast with a view about concepts developed in my own work (see also Machery 2005): the Heterogeneity BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Machery: Pre´cis of Doing without Concepts Hypothesis. According to this hypothesis, the class of concepts divides into several distinct kinds that have little in common – “the fundamental kinds of concepts.” Because the class of concepts divides into distinct fundamental kinds, it is a mistake to assume that there are many general properties of concepts, and that a theory of concepts should attempt to describe these. Although the heterogeneity hypothesis can be developed in several ways (Machery 2005; 2006a; Piccinini & Scott 2006), I contend that a given category (e.g., dogs), a given substance (e.g., water), or a given kind of events (e.g., going to the dentist) is typically represented by several distinct concepts (e.g., DOG 1 and DOG 2). These coreferential concepts belong to the fundamental kinds of concepts. Each coreferential concept can be used to categorize, draw inductions, understand the relevant words, make analogies, and so forth (Fig. 1). In addition, the heterogeneity hypothesis contends that these concepts are often used in distinct processes. That is, we have several categorization processes, several induction processes, and the like, each of which uses a distinct fundamental kind of concepts (Fig. 2). If the heterogeneity hypothesis is correct, the class of concepts is not a natural kind. Natural kinds are classes whose members share many scientifically important properties in virtue of one or several causal mechanisms (Boyd 1991; 1999; Griffiths 1997; Machery 2005). Water and dogs are natural kinds in this sense; for example, samples of water have many properties in common in virtue of consisting of the same molecules of H2O. In a given science, the scientific classificatory scheme is developed to identify the natural kinds in the relevant domain because identifying these kinds allows scientists to discover new generalizations. Scientific classificatory schemes are modified when they do not identify the relevant natural kinds (as happened during the chemical revolution in the eighteenth century), and scientific notions are often eliminated when it is found that they fail to pick out natural kinds (for discussion, see sect.

Figure 1.

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13). Because the hypothesized fundamental kinds of concepts have little in common, the class of concepts cannot be a natural kind if the heterogeneity hypothesis is correct. 7. What kind of evidence could support the heterogeneity hypothesis? Chapter 5 of Doing without Concepts describes three kinds of evidence that can provide support for the heterogeneity hypothesis. I consider them in turn in this section. Suppose that the class of concepts divides into several fundamental kinds, and suppose also that coreferential concepts are often used in distinct cognitive processes (i.e., distinct categorization processes, distinct induction processes). What properties would we then expect to observe in experimental tasks? First, if experimental conditions can be designed that trigger only one of the hypothesized categorization processes or only one of the hypothesized induction processes, we should expect some experimental findings to be best explained if the concepts used in the relevant experimental tasks are identical to a first fundamental kind of concepts, other experimental findings to be best explained if the concepts used in the relevant experimental tasks are identical to a second fundamental kind of concepts, and so on. For instance, if one hypothesizes that the fundamental kinds of concepts are exemplars and prototypes, then one might find categorization tasks where participants’ categorization performances are best explained if the concepts used in these tasks are prototypes and other categorization tasks where participants’ categorization performances are best explained if the concepts used in these tasks are exemplars. Second, suppose that in some conditions, several of the hypothesized categorization (or induction) processes are triggered at the same time. Then, in some circumstances, these processes will produce congruent outputs (e.g., categorization judgments), while they will produce incongruent outputs in other circumstances. When the latter

The heterogeneity hypothesis.

Machery: Pre´cis of Doing without Concepts

Figure 2.

Several processes underlying a given cognitive competence.

happens, participants will have to decide between conflicting judgments. Participants should thus be expected to be slower when the hypothesized processes are expected to yield conflicting outputs than when they are not. Test-retest reliability should also be expected to be lower in the experimental conditions where it is hypothesized that the hypothesized categorization (induction) processes will result in incongruent outputs than when it is hypothesized that they will result in congruent outputs. Noticeably, this kind of evidence (particularly, slower reaction times) has extensively been used in cognitive science to argue that a given task involves two independent cognitive processes (e.g., Greene et al. 2001). Finally, experimental and neuropsychological dissociations can be used to determine whether a given task involves several processes. The epistemology of dissociations is intricate (Ashby & Ell 2002; Caramazza 1986; Glymour 1994; Dunn & Kirsner 1988; 2003; Plaut 1995; Shallice 1988; Van Orden et al. 2001), but I maintain that dissociations provide evidence about the number and nature of the processes underlying a given competence. 8. The fundamental kinds of concepts Now that the nature of the evidence required to support the heterogeneity hypothesis has been clarified, it is time to lay my cards on the table: What are the fundamental kinds of concepts? And what is the evidence for their existence? In what follows, I will briefly describe the kind of evidence supporting the heterogeneity hypothesis, but due to limitations of space this will not amount to a comprehensive articulation of the evidence adduced in Chapters 6 and 7 of Doing without Concepts. In Chapter 4, I propose that the class of concepts divides into at least three fundamental kinds of concepts – prototypes, exemplars, and theories. These three theoretical constructs are well known in the psychology of concepts as they correspond to the entities posited by the main theories of concepts that have been developed since the 1970s (for a review, see Murphy 2002). Although there are several distinct theories about what prototypes,

exemplars, and theories are, these theories agree about the distinctive features of each type of concept. In substance, prototypes are bodies of statistical knowledge about a category, a substance, a type of event, and so on. For example, a prototype of dogs could store some statistical knowledge about the properties that are typical of dogs and/or the properties that are diagnostic of the class of dogs. According to prototype theories, when I categorize, draw an induction, make an analogy, and so forth, I spontaneously bring to mind the properties that are typical, diagnostic (etc.) of the relevant category, substance, and so forth. Prototypes are typically assumed to be used in cognitive processes that compute the similarity between a prototype and other representations, such as the representations of the objects to be categorized, in a linear manner (Hampton 1979; 1993; 2006; 2007; Rosch & Mervis 1975; Smith 2002). Exemplars are bodies of knowledge about individual members of a category (e.g., Fido, Rover), particular samples of a substance, and particular instances of a kind of event (e.g., my last visit to the dentist). For instance, according to exemplar theories, a concept of dogs would consist of a set of bodies of knowledge about specific dogs (Rover, Fido). When I categorize, draw an induction, make an analogy, and so on, I spontaneously bring to mind the properties of specific members of the relevant categories, of specific samples of the relevant substances, et cetera. Exemplars are typically assumed to be used in cognitive processes that compute the similarity between a set of exemplars and other representations, such as the representations of the objects to be categorized, in a nonlinear manner (Medin & Schaffer 1978; Nosofsky 1986; 1992; Nosofsky & Stanton 2005). Theories are bodies of causal, functional, generic, and nomological knowledge about categories, substances, types of events, and the like. A theory of dogs would consist of some such knowledge about dogs. When I categorize, draw an induction, make an analogy, and so on, I spontaneously bring to mind this causal, functional, generic, and nomological knowledge. Recent work on causal knowledge suggests that theories might be used in cognitive processes that are similar to the algorithms involved in causal reasoning (Gopnik et al. 2004). BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Machery: Pre´cis of Doing without Concepts Thus, the heterogeneity hypothesis proposes that for many categories, substances, kinds of events, we typically have a prototype, a set of exemplars, and a theory about them. Thus, we might have a prototype of dogs, a set of exemplars of particular dogs, and a theory about dogs. Furthermore, prototypes, exemplars, and theories are often used in distinct processes. The heterogeneity hypothesis proposes that we have a prototype-based categorization process, an exemplar-based categorization process, and a theory-based categorization process. Note that the hypothesis is not merely that our knowledge about dogs includes some knowledge about their typical or diagnostic properties; some knowledge about some particular dogs; and some causal, functional, and generic knowledge (as Rey [2009b] erroneously believes). This would be a fairly uncontroversial claim. Rather, the claim is that, for most categories, substances, et cetera, we have several bodies of knowledge that are retrieved by default and that are often used in distinct cognitive processes (e.g., several distinct categorization processes). The heterogeneity hypothesis also contends that the fundamental kinds of concepts have little in common. This is indeed the case if these fundamental kinds really consist of prototypes, exemplars, and theories. They consist of different types of knowledge, they are used in different kinds of processes, and they are probably acquired by distinct processes. Given what cognitive scientists working on concepts are interested in (see sect. 4), they count as very different kinds of entities. One might perhaps object that prototypes, exemplars, and theories do have some properties in common. In particular, they are all bodies of knowledge, they are all stored in long-term memory, and they are all used in the processes underlying higher cognition. This, however, does not undermine the heterogeneity hypothesis, for the claim that prototypes, exemplars, and theories have little in common really states that the fundamental kinds of concepts have in common few properties that are scientifically interesting and discovered empirically. Prototypes, exemplars, and theories have in common numerous properties that are not of interest to cognitive scientists (e.g., they are all mental states). In addition, far from being discovered empirically, the mentioned commonalities between prototypes, exemplars, and theories (e.g., they are all bodies of knowledge, they are all stored in longterm memory, etc.) are in fact used to identify what concepts are. So, what is the evidence for the claim that our long-term memory stores prototypes, exemplars, and theories? When one examines 30 years of research on categorization and induction, as I do in Chapters 6 and 7, one finds out that in both areas of research, some phenomena are well explained if the concepts elicited by some experimental tasks are prototypes; some phenomena are well explained if the concepts elicited by other experimental tasks are exemplars; and yet other phenomena are well explained if the concepts elicited by yet other experimental tasks are theories. As already noted, if one assumes that experimental conditions prime the reliance on one type of concepts (e.g., prototypes) instead of other types (e.g., exemplars and theories), this provides evidence for the heterogeneity hypothesis. Let’s illustrate this situation with the work on categorical induction – the capacity to conclude that the 202

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members of a category possess a property from the fact that the members of another category possess it and to evaluate the probability of this generalization (for review, see Feeney & Heit 2007; Heit 2000; Murphy 2002, Ch. 8; Sloman & Lagnado 2005). A large number of phenomena suggest that prototypes or exemplars are sometimes involved in induction (Osherson et al. 1990; Sloman 1993). Similarity-based models of induction, which assume that processes underlying induction are defined over either prototypes or exemplars, explain best two well-known findings about induction – the similarity effect and the typicality effect. Other phenomena are best explained if the concepts involved in the relevant experimental conditions are causal theories. Investigating the judgments made by tree experts (landscapers, taxonomists, and parks maintenance personnel) about the strength of inductive conclusions about trees, Proffitt et al. (2000) found that, rather than relying on typicality (as predicted, for instance, by Osherson and colleagues’ similarity-coverage model), the pattern of answers and the justifications provided suggest that experts often base their judgments on theories about hypothetical causal mechanisms (see also Lo´pez et al. 1997). As explained in section 7, the fact that different properties of our inductive competence are best explained by theories positing different theoretical entities (viz., prototypes, exemplars, or theories) constitutes evidence for the existence of distinct kinds of concepts used in distinct processes. Strikingly, this conclusion is consistent with the emerging consensus among psychologists working on induction that people rely on several distinct induction processes (Murphy 2002; Proffitt et al. 2000; Rehder 2006; Sloman & Lagnado 2005). A natural question raised by these findings concerns the conditions that prime the reliance on prototypes rather than exemplars and theories or on theories rather than prototypes and exemplars (and so on) in induction (see sect. 12). Because cognitive scientists have rarely fully embraced the idea that there are several distinct kinds of concepts and several processes defined over them, there is little systematic work on this question (but see Rehder 2006). The research on categorization and concept learning, reviewed in Chapter 6, tells an even clearer story, providing evidence for the existence of prototypes, exemplars, and theories that are used in distinct categorization processes. The research on concept combination, reviewed in Chapter 7, also shows that when people produce a complex concept, they appeal to exemplars, prototypes, and theories. However, in contrast to the research on induction and categorization, it appears that a single process uses prototypes, exemplars, and theories (instead of several distinct combination processes, each of which uses a distinct kind of concepts). 9. Neo-empiricism A number of cognitive scientists have recently developed a new approach to the nature of concepts (Barsalou 1999; 2008a; 2009; Barsalou et al. 2003; Gallese & Lakoff 2005; Glenberg 1997; Martin 2007; Martin & Chao 2001; Prinz 2002; 2005), which I have called “neo-empiricism” (Machery 2006c; 2007). Although there are

Machery: Pre´cis of Doing without Concepts differences between neo-empiricist theories, they all endorse the two following theses: 1. The knowledge that is stored in a concept is encoded in several perceptual and motor representational formats. 2. Conceptual processing involves essentially reenacting some perceptual and motor states and manipulating these states. Thesis 1 is about the format of concepts: Neo-empiricists claim that conceptual knowledge is encoded in perceptual and motor representational formats. By contrast, amodal theorists contend that our conceptual knowledge is encoded in a representational format that is distinct from the perceptual and motor representational formats (Barsalou et al. 2003, p. 85). This distinct representational format is usually thought of as being language-like, although, importantly, amodal representations need not form a language (see further on). Thesis 2 concerns the nature of the cognitive processes underlying categorization, induction, deduction, analogy-making, planning, or linguistic comprehension. The central insight is that retrieving a concept from long-term memory during reasoning or categorization consists in tokening some perceptual representations, a process called simulation or reenactment. Cognitive processing consists in manipulating these reenacted percepts (e.g., Barsalou 1999, p. 578). Following Barsalou (1999), I will use the term perceptual symbols to refer to concepts understood in accordance with Theses 1 and 2. Perceptual symbols might be one of the fundamental kinds of concepts, but I argue in Chapter 4 of Doing without Concepts that the evidence provided so far falls short of establishing this. I have identified three main shortcomings of the research in support of neo-empiricism (see also Machery 2007; for other arguments, see Dove 2009; Machery 2006c; Mahon & Caramazza 2008). First, what I have called “Anderson’s problem” in reference to Anderson’s (1978) work on the controversy between imagistic and propositional theories of thinking. Neo-empiricists typically contrast the predictions made by amodal theories of concepts and the predictions made by neo-empiricist theories of concepts (e.g., Pecher et al. 2004; Solomon & Barsalou 2001; 2004; Yaxley & Zwaan 2007), and they then attempt to show that the neo-empiricist predictions, but not the amodal predictions, are verified. The problem is that there is no such thing as the amodal prediction of concepts; rather, different amodal theories of concepts make different predictions, depending on what they assume about the processes that use amodal concepts. In numerous cases, some amodal theories of concepts make exactly the same predictions as the neo-empiricist theories of concepts developed by cognitive scientists such as Barsalou (for some examples, see Machery 2007; 2009; Mahon & Caramazza 2008). As a result, neo-empiricist findings do not distinguish between neo-empiricism and amodal theories of concepts in general. Rather, they provide evidence against specific amodal theories of concepts, while being naturally accommodated by other amodal theories of concepts. The second shortcoming is what I have called “the problem from imagery.” Most proponents of amodal theories of concepts (e.g., Fodor 1975; Simon 1995) acknowledge that in some situations, people rely on imagery (e.g., visual imagery). For instance, we visualize our own home

when we are asked how many windows it has. What proponents of amodal theories of concepts deny is that imagery is the only type of processes people have: People also have amodal concepts that are used in non-perceptual processes. The fact that proponents of amodal theories of concepts recognize the role and importance of imagery entails that when amodal theorists expect people to rely on imagery to solve a particular task, showing that people use imagery in this task fails to provide evidence for neo-empiricism and against amodal theories of concepts (for some examples, see Machery 2007). The third shortcoming is what I have called “the generality problem.” Neo-empiricists typically assume that all concepts are perceptual symbols. However, it could be that perceptual symbols constitute only a kind of concepts – a hypothesis that would naturally be consistent with the heterogeneity hypothesis. In fact, research suggests that at least some conceptual representations – namely, the representations of the magnitudes of classes of objects or sequences of sounds – are not perceptual, but amodal (Dove 2009; Machery 2007). Although these representations do not form a language and thus are different from the hypothesized representations of Fodor’s (1975; 2008) language of thought, they are not perceptual either (Dove 2009; Machery 2006c). Dove (2009) has developed the generality problem in great detail, showing that the research in support of neo-empiricism has typically focused on a single kind of concepts – namely, “concrete or highly imageable concepts” (2009, p. 431) – and that neo-empiricist findings are unlikely to be found with concepts with low imageability such as abstract concepts. Others have identified further difficulties. Reviewing a range of neuroscientific work on concepts and various important behavioral studies, Mahon and Caramazza (2008) grant that the perceptual and motor systems are often activated during conceptual processing, but they insist that this activation falls short of supporting neoempiricism, for it can be interpreted in two different ways. First, the interpretation preferred by neo-empiricists: The brain areas involved in perceptual and motor processing or the areas near those are activated because concepts are perceptual and motor representations and perceptual representations are realized in these areas. Second, the amodal interpretation of these findings: The activation of these brain areas results from the activation of other brain areas, not involved in perceptual processing, and from this activation spreading from the latter areas to the former (a well-known phenomenon). Both interpretations account equally well for the neo-empiricist findings. Finally, let’s say a few words about the neo-empiricist research in cognitive neuroscience. A large number of fMRI studies show that tasks meant to tap into the processes underlying higher cognition (particularly, tasks involving the understanding of words) activate either the very brain areas involved in perceptual and motor processing or brain areas near those (see, e.g., Barsalou 2008a; Kiefer et al. 2007; Martin 2007; Martin & Chao 2001; Pulvermu¨ller 2005; Simmons et al. 2007; ThompsonSchill 2003). However, in contrast to neo-empiricists’ usual interpretation of these findings, I contend that much of the neuroscientific research on concepts challenges this approach. Because neo-empiricists insist that tokening a concept means tokening some perceptual BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Machery: Pre´cis of Doing without Concepts representations, they are committed to the view that concept retrieval should activate our perceptual areas (Simmons et al. 2007). However, a typical finding in neuroscience is that the brain areas activated are near, and thus not identical to, the brain areas involved in perceptual or motor processing (a point acknowledged by Simmons et al. 2007). Furthermore, in much neo-empiricist research on concepts in neuroscience, the brain areas that are activated in the tasks meant to tap into the processes underlying higher cognition are anterior to the brains areas activated in perceptual processing (e.g., Boulenger et al. 2009; Chao & Martin 1999; Hauk et al. 2004; Kable et al. 2005; Martin et al. 1995; Pulvermu¨ller & Hauk 2006). A plausible interpretation is that the brain areas activated in the tasks tapping into higher cognition are amodal representations, which are distinct from the perceptual representations activated in the tasks tapping into perceptual processes, but near them. To conclude, it might be that perceptual symbols are a fundamental kind of concepts, but research still fails to establish it beyond doubt. 10. Hybrid theories of concepts Several hybrid theories of concepts have been developed since the 1970s, and there is a fair amount of differences between them, but they all agree on several crucial points (Anderson & Betz 2001; Keil 1989; Keil et al. 1998). Hybrid theories of concepts grant the existence of several types of bodies of knowledge, but deny that these form distinct concepts; rather, these bodies of knowledge are the parts of concepts. Like the heterogeneity hypothesis, hybrid theories of concepts typically concur that these parts store different types of information. For instance, some hybrid theories (Gelman 2004) have proposed that one part of a concept of x might store some statistical information about the x’s, while another part stores some information about specific members of the class of x’s, and a third part some causal, nomological, or functional information about the x’s. Furthermore, they often contend that the distinct parts that compose a given concept are used in different processes (e.g., Osherson & Smith 1981). For example, the parts that compose a given hybrid concept might be used in distinct categorization processes, distinct induction processes, and so on. Although hybrid theories of concepts and the heterogeneity hypothesis agree on several points, they are far from being identical. In section 2, I proposed two individuation criteria that specify when two bodies of knowledge about x form two distinct concepts rather than a single concept. Hybrid theories of concepts contend both that the different coreferential bodies of knowledge are connected and that they are coordinated. The heterogeneity hypothesis assumes that at least one of these two claims is false. Evidence tentatively suggests that prototypes, set of exemplars, and theories are not coordinated. Malt’s (1994) work on how people conceptualize water shows that people have at least two distinct concepts of water – a theoretical concept of water that identifies water with any substance composed of molecules of H2O and a prototype that identifies water with any substance that shares some typical properties (origins, use, appearances). More recently, Machery and Seppa¨la¨ (forthcoming) have 204

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shown that many participants (between 20% and 80%, depending on the pair of sentences considered) are willing to endorse apparently contradictory sentences of the following form: 1. In a sense, tomatoes are a fruit. 2. In a sense, tomatoes are not a fruit. 3. In a sense, whales are fish. 4. In a sense, whales are not fish. That is, many participants say that both (1) and (2) and both (3) and (4) are true. Although there are several possible explanations of these findings, a plausible explanation is that people retrieve different concepts of tomato when they read (1) and (2). When they retrieve a theory of tomatoes, they answer that (1) is true, whereas they answer that (2) is true when they retrieve a prototype of tomatoes. This suggests that their prototype and their theory form two distinct concepts rather than two parts of the same concept.

11. Multi-process theories The heterogeneity hypothesis proposes that prototypes, exemplars, and theories are often used in distinct cognitive processes (e.g., distinct categorization processes). I call “multi-process theories” those theories that contend that a given cognitive competence (e.g., categorization, induction, or the capacity to make moral judgments) is underwritten by several distinct processes. Chapter 5 of Doing without Concepts is dedicated to examining this kind of cognitive theory. Dual-process theories, which have been embraced in social psychology, are a type of multiprocess theory, characterized by a distinction between two types of processes (slow, analytic, intentional processes and fast, automatic processes; for discussion, see, e.g., Evans 2007; Evans and Frankish 2009; Gigerenzer & Regier 1996; Sloman 1996). Gigerenzer and Todd’s fast-and-frugal-heuristics research program is another kind of multi-process theory (Gigerenzer et al. 1999). The default hypothesis in cognitive science is that a cognitive competence is underwritten by a single cognitive process, and the burden of proof typically is on those who hold a multi-process theory for some cognitive competence. In light of the recent work on a range of cognitive competences, this state of affairs should be revised. Evidence suggests that cognitive competences are commonly underwritten by several distinct processes. Postulating that a given cognitive competence is underwritten by distinct processes raises a host of questions that have rarely been explicitly confronted by proponents of multi-process theories (but should be). The two most important issues are the following ones: A. In what conditions are the cognitive processes underlying a given cognitive competence triggered? Are they all always triggered? Are they rather triggered in distinct circumstances? Or, perhaps, in overlapping circumstances? If they are not all always triggered, what cues or processes determine their triggering? Is their triggering under intentional control? B. If the cognitive processes that underlie a given cognitive competence are triggered in the same conditions, how does the mind choose between their outputs or integrate them? It is fair to say that current multi-process theories, such as the dual-process theories, have typically failed to give

Machery: Pre´cis of Doing without Concepts clear answers to these questions. This limits their capacity to genuinely predict experimental outcomes. What about the prototype-based, exemplar-based, and theory-based cognitive processes? In what conditions are they triggered? And if they are triggered simultaneously, how does the mind choose between their outputs? There is no systematic work on these issues; in fact, I hope that this book will invite cognitive scientists to systematically investigate them. What is known can be presented briefly. It appears that the categorization processes can be triggered simultaneously (e.g., Allen & Brooks 1991; Smith et al. 1998), but that some circumstances prime reliance on one of the categorization processes. Reasoning out loud seems to prime people to rely on a theory-based process of categorization (Smith & Sloman 1994). Categorizing objects into a class with which one has little acquaintance seems to prime people to rely on exemplars (Smith & Minda 1998). The same is true of these classes whose members appear to share few properties in common (Minda & Smith 2001; Murphy 2002; Smith & Minda 2000). Very little is known about the induction processes except for the fact that expertise seems to prime people to rely on theoretical knowledge about the classes involved (Lo´pez et al. 1997; Proffitt et al. 2000). 12. Open questions One of the virtues of the heterogeneity hypothesis is to bring to the fore a range of questions that have not been systematically examined by cognitive scientists. I now summarize some of these issues. First, psychologists should investigate the factors that determine whether an element of knowledge about x is part of the concept of x rather than being part of the background knowledge about x. Frequency of use is the only factor that has been systematically investigated (Barsalou 1982). Other factors should be considered – including attention and explicit teaching. Second, there are several prototype theories, several exemplar theories, and several theory theories. Although evidence indicates that we have prototypes, exemplars, and theories, it remains unclear, however, which prototype theory, exemplar theory, or theory theories is correct. That is, the exact nature of prototypes, exemplars, and theories remains to be investigated. Cognitive scientists have typically attempted to show that concepts are prototypes, or that concepts are exemplars, or that concepts are theories, but they have paid little attention to investigating the nature of prototypes, exemplars, and theories in great detail. Similarly, it is important to determine which prototype-based model of categorization (induction, etc.), which exemplar-based model of categorization (induction, etc.), and which theory-based model of categorization (induction, etc.) is correct (instead of comparing, say, a specific exemplar-based and a specific prototypebased model of categorization, as has usually been done). Recently, some psychologists have taken up the important task of comparing the models of categorization and of induction developed by prototype theories (Sloman & Lagnado 2005), as well as the models of categorization and of induction developed by theory theorists (Rehder & Kim 2006). Such efforts should be systematically pursued.

Third, multi-process theories are also another important research area that requires systematic attention. I have sketched a framework for developing multi-process theories of the higher cognitive competences, identifying several key questions that need to be answered by proponents of these theories. Multi-process theories need to deal with some important issues that have not been fully solved. Of particular importance is the kind of evidence that can support multi-process theories. Among the three kinds of evidence I have distinguished, the legitimacy of dissociations remains controversial and should be investigated further. It is also plausible that other kinds of evidence can support multi-process theories. While contemporary psychologists often endorse dual theories of cognition that distinguish System 1 and System 2 processes, there are numerous other types of multiprocess theory. Furthermore, existing multi-process theories, such as dual-process theories, do not specify in which conditions the hypothesized processes are triggered and how their outputs are integrated, and, as a result, they are unable to yield clear predictions instead of mere post hoc accommodations. Psychologists should also develop detailed multi-process theories of those cognitive competences that are the best candidates for being realized by several distinct processes – namely, categorization and induction (see sect. 11). So far, we know very little about how the distinct cognitive processes that realize competences such as categorization and induction are organized. We do not really know whether outside the lab the categorization (or induction) processes are triggered simultaneously or in distinct conditions. We do not really know what determines their triggering. And we do not know what happens to the outputs of the categorization (or induction) processes when these processes are simultaneously triggered. 13. Concept eliminativism Let us take stock. We have seen that the class of concepts divides into several distinct kinds of concepts, namely, prototypes, exemplars, and theories, which have little in common. Categories, substances, events are often represented by several coreferential concepts (a prototype, a set of exemplars, and a theory). These are not parts of concepts, but are rather bona fide concepts. Prototypes, exemplars, and theories are also typically used in distinct cognitive processes, for example, in distinct categorization processes, although little is known about the organization of these processes. It is rarely the case that a given cognitive competence is underwritten by a single cognitive process; rather, the mind usually includes several distinct processes that do the same thing. Finally, the heterogeneity hypothesis focuses attention on a range of empirical questions, for which systematic empirical information is missing, partly because cognitive scientists have not considered the heterogeneity hypothesis seriously. To conclude this article, I want to discuss a radical proposal inspired by the views about concepts presented in Doing without Concepts and in this pre´cis article: Cognitive scientists might be better off renouncing the very notion of concept. Rather, they should use theoretical terms introduced to refer to the fundamental kinds of concepts – namely, prototype, exemplar, and theory. The BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts heterogeneity hypothesis contends that the class of concepts is not a natural kind: It does not support many causally grounded generalizations because the class of concepts divides into several fundamental kinds that have little in common. Furthermore, theoretical terms are often rejected when it is found that they fail to pick out natural kinds. To illustrate, some philosophers (Murphy & Stich [1999] building on Griffiths [1997]) have proposed to eliminate the term emotion from the theoretical vocabulary of psychology on precisely these grounds. The proposal here is that concept should be eliminated from the vocabulary of cognitive science for the same reason. Chapter 8 of Doing without Concepts examines the intricate and controversial logic of eliminativist arguments (see also Mallon et al. 2009; Murphy & Stich 1999; Stich 1996). Many eliminativist arguments attempt to conclude that there are no x’s (e.g., no beliefs: see Churchland [1981] and Stich [1983]; or no races: for discussion, see, e.g., Mallon [2006]) from the fact that the definition of x is not satisfied. For instance, if nothing satisfies the definition of belief or concept, it is concluded that there are no beliefs of concepts. However, such eliminativist arguments are bound to be unsuccessful because they are enmeshed with controversial issues concerning how words such as belief or concept refer (Mallon et al. 2009). What we need is another kind of eliminativist argument, which clarifies when it is legitimate to eliminate a scientific term from a scientific classificatory scheme. In a nutshell, I propose that scientific terms should be eliminated on pragmatic grounds (this is what I have called “scientific eliminativism”): To determine whether x has a legitimate place in the vocabulary of a given science or whether it should be eliminated, one should examine whether using x helps to fulfill the goals of this science – particularly, whether it helps its classificatory purposes. Picking out natural kinds is the primary function of theoretical terms in many sciences (Quine 1969; but perhaps not in all sciences: Russell 1948). Thus, when it is found out that a scientific term fails to pick out a natural kind, there is a presumption that it should be eliminated from the relevant science. However, one still needs to consider and weigh the costs and benefits of eliminating this term. Perhaps keeping this term might simplify communication between scientists. On the other hand, keeping this term might prevent the development of a more appropriate classificatory scheme (a common situation, I suspect). If the relevant term does not pick out a natural kind and if the benefits of keeping it do not clearly overweigh the costs, then one should eliminate it. Because concept does not pick out a natural kind if the heterogeneity hypothesis is correct, there is a presumption that it should be eliminated from the theoretical vocabulary of psychology. Furthermore, the continued use of concept in cognitive science might invite cognitive scientists to look for commonalities shared by all concepts or to develop another theory that would encompass all the phenomena known about the processes underlying higher cognition. If the heterogeneity hypothesis is correct, these efforts would be wasted. By contrast, replacing concept with prototype, exemplar, and theory would bring to the fore the urgent open questions discussed in section 12. For instance, speaking of a prototype-based categorization process, an exemplar-based categorization 206

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process, and a theory-based categorization process makes it clear that there are several categorization processes and brings to the fore the questions of the organization of these categorization processes. Now, one might worry that eliminating the word concept would make communication among cognitive scientists cumbersome. To some extent, this is likely to be true, as is suggested by the frequent use of this term in this article. But I doubt that the elimination of concept would make communication too cumbersome; after all, when required, cognitive scientists can always appeal to the description “bodies of knowledge used in higher cognition.” It seems likely that using such a description will not invite cognitive scientists (or at least not to the same extent) to look for commonalities among all bodies of knowledge used in higher cognition. If this is correct, then the costs resulting from the elimination of concept are limited, and cognitive scientists should eliminate the notion of concept from their theoretical apparatus. ACKNOWLEDGMENTS I would like to thank Guy Dove, Kevan Edwards, and Paul Bloom for helpful comments on previous drafts.

Open Peer Commentary Default knowledge, time pressure, and the theory-theory of concepts doi:10.1017/S0140525X10000324 Thomas Blanchard Department of Philosophy, Rutgers University, New Brunswick, NJ 08901-1107. [email protected]

Abstract: I raise two issues for Machery’s discussion and interpretation of the theory-theory. First, I raise an objection against Machery’s claim that theory-theorists take theories to be default bodies of knowledge. Second, I argue that theory-theorists’ experimental results do not support Machery’s contention that default bodies of knowledge include theories used in their own proprietary kind of categorization process.

Edouard Machery (in Doing without Concepts, Machery 2009, p. 12) claims that psychologists (including theory-theorists) take concepts to be “bodies of knowledge that are used by default in the processes underlying the higher cognitive competences” (p. 11). According to Machery’s gloss, default knowledge about, for example, dogs is the knowledge that is “preferentially available” (p. 11) and “spontaneously comes to mind” (p. 12) in most contexts in which we make judgments about dogs. People’s knowledge about a category is not exhausted by their default knowledge, but their non-default (background) knowledge is less easily retrievable and is used only when default knowledge is insufficient for the task at hand. Machery suggests that whether or not a subject’s judgment about x provides evidence about the content of her default body of knowledge relative to x depends (at least partly) on whether or not she made the judgment under time pressure. Thus, he notes in his Pre´cis (target article, sect. 3, para. 8), the fact that under no time pressure people may retrieve a representation of a cheetah that makes the sentence “A man can outrun a cheetah” true is no evidence that this representation belongs to their default knowledge

Commentary/Machery: Pre´cis of Doing without Concepts rather than to their background knowledge about cheetahs. In situations with no time pressure, people can and do retrieve information from their background knowledge. This turns out to be crucial when we consider the nature of the theory-theorists’ experiments. There is a stark contrast between the kinds of experimental tasks relied upon by, say, prototype-theorists on the one hand and theory-theorists on the other hand. Prototype theorists have usually relied upon experimental tasks in which subjects were instructed, for example, to list properties associated with a category in a short time period or make categorization judgments under explicit time pressure (see, e.g., Hampton 1979; Rosch & Mervis 1975). By contrast, many of the experimental tasks designed by theory-theorists do not involve any element of time pressure. Consider, for instance, Rips’s (1989) famous pizza experiment. As Smith and Sloman (1994) have rightly noted, “there was no mention of speed in Rips’ instructions” (p. 380). Or consider Keil’s (1989) transformation experiments, in which subjects were asked to make judgments about the biological membership of an animal that has undergone unusual transformations. Nothing in Keil’s text indicates that subjects had to make their categorization judgments under any particular time pressure. This raises two issues for Machery’s discussion of the theorytheory. First, because Rips’s and Keil’s experimental tasks did not involve any element of time pressure, they do not show that the knowledge retrieved by subjects to solve these tasks belongs to their “default knowledge” in Machery’s sense. Subjects might have retrieved information from their background knowledge to make their categorization judgments. (Given the unusual nature of the tasks they were asked to solve, this would not be surprising). However, theory-theorists take Rips’s and Keil’s experiments to provide crucial insights into the nature and structure of concepts. If theory-theorists assume concepts to be default bodies of knowledge, then they have misinterpreted Rips’s and Keil’s results. A more charitable hypothesis is that, pace Machery, theory-theorists do not consider that being used by default in higher-level cognitive competences is a necessary condition for a piece of information to count as conceptual knowledge. This hypothesis is supported by some of the theory-theorists’ own claims. For example, both Murphy and Medin (1985, p. 296) and Gelman (2003, p. 244) take theories to be concepts, but allow that in many contexts, subjects rely by default on prototypical features to make routine and quick categorization judgments, and retrieve theoretical information only when prototypical information is insufficient for the task at hand. Here is the second issue. Machery (2009, pp. 52, 119) contends that for most categories, our default knowledge includes a theory of that category, and that theory-like default bodies of knowledge are associated with a distinct kind of categorization process. He mentions Rips’s and Keil’s results in support of this claim (pp. 183– 87). However, these experiments do not support Machery’s contention, since they are consistent with the claim that theories belong to background rather than to default knowledge. Moreover, the literature generated by Rips’s results partly disconfirms Machery’s claim. Thus, Smith and Sloman (1994, pp. 379– 80) failed to replicate Rips’s results when they instructed subjects to make their categorization judgments quickly. This suggests that the theoretical information on which subjects relied in Rips’s original experiment did not belong to their default knowledge. Now, some experimental results mentioned by Machery do support the idea that default bodies of knowledge include theoretical information. Thus, Luhmann et al. (2006; cited in Machery 2009, p. 186) have shown that subjects can use causal knowledge to make categorization judgments even under time pressure. In the learning phase of the experiment, subjects learned about the causal relations between various features of fictional animals. In a subsequent categorization phase, subjects relied upon this causal knowledge to make categorization judgments, even when they were instructed to respond as quickly as possible.

This is evidence that their default knowledge about those fictional animals included causal (hence theoretical) knowledge. However, as Luhmann et al. argue, their results are consistent with (and according to them, even support) the idea that their subjects’ theory-driven categorization process was not “qualitatively different from traditional similarity-based processes based on weighted feature matching” (2006, p. 1104): Subjects may simply have assigned strong weights to causal features when they first learned the category, and later used those features weights in a simple similarity computation when they made categorization judgments. Thus, these results do not support Machery’s claim (2009, p. 119) that theory-like default bodies of knowledge are used in a categorization process similar to scientific reasoning and different in kind from the categorization processes associated with, for example, prototypes. ACKNOWLEDGMENT I thank Alvin Goldman for helpful advice and comments.

Carving nature at its joints using a knife called concepts doi:10.1017/S0140525X10000336 Justin J. Couchman, Joseph Boomer, Mariana V. C. Coutinho, and J. David Smith Department of Psychology, University at Buffalo, State University of New York, Buffalo, NY 14260. [email protected] [email protected] [email protected] [email protected]

Abstract: That humans can categorize in different ways does not imply that there are qualitatively distinct underlying natural kinds or that the field of concepts splinters. Rather, it implies that the unitary goal of forming concepts is important enough that it receives redundant expression in cognition. Categorization science focuses on commonalities involved in concept learning. Eliminating “concept” makes this more difficult.

Categorization researchers hypothesize that minds group objects to reflect natural groupings in the world. For example, theorists often point to the family-resemblance relationships embodied by biological taxonomies. Poodles resemble each other more than they do Labradors; dogs resemble each other more than they do bears, and so forth. These similarities presumably hold in the world and in our mental lives. Of course, there is a fundamental separation between categories in the world and concepts in the mind. Fortunately, though, categorization science finds its grounding and coherence at both the ecological (world) and psychological (mind) levels of analysis. Unfortunately, this often leads to the misconception that “category” and “concept” are equivalent or interchangeable terms – as Machery correctly points out in Doing without Concepts (Machery 2009, pp. 8 – 14). The world contains coherent assemblages of objects that are sometimes called natural kinds, family-resemblance categories, or consequential stimulus regions. All organisms share the profoundly important goal of learning the concepts that summarize these assemblages. These concepts are behavioral and psychological equivalence classes – they let creatures behave similarly toward similar things and understand similar things similarly. Concepts are the mind’s tool for representing and responding to natural kinds. The conceptual goal of living organisms is unitary – to develop psychological representational schemes that preserve these assemblages in acting and understanding. The fitness consequence of not doing so is unitary. The assemblages of things in the world – the natural kinds – may have a unitary structure, too. One sees that categorization science is motivated by this BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts unitariness. That concepts are a staff of life for living organisms is a profound truth that would be lost by breaking the field into process splinters. This is a principal reason for preserving the coherence of the field. Now it is true that minds lack direct access to the world’s things in themselves. Therefore, minds must create mental concepts that estimate natural-kind categories. Given this necessary distinction between natural-kinds in the world and our mental estimates of them, it is not clear why concepts, prototypes, exemplars, theories, or any other mental representation would be considered a natural kind. Indeed, if there are any groups that are not natural kinds, mental representations are likely among them. Machery (2009, p. 241) contends that because it does not pick out a unified natural kind, “concept” is a useless, or perhaps even harmful, term. We disagree. It does not shatter the field of concepts if it turns out – as it does – that concept estimation produces a marvelous variety of concepts and learning processes – including prototypes, exemplar memories, category rules, decision bounds, and theories. This only means that the phenomenon of concepts is rich and diverse. It also means that the unitary goal of estimating concepts is central enough to survival that it deserves and receives redundant expression within cognitive systems. To us, this centrality emphasizes the need for a coherent field of concepts and categorization. Of course, the field sometimes seems less than coherent because researchers tailor category tasks to elicit different processes. Prototype theorists use large stimulus sets that reduce stimulus repetition and defeat exemplar memorization. These categories also have family-resemblance organization that encourages prototype formation. Exemplar theorists often use categories that share little or no family resemblance. Perhaps the most iconic of these are “5–4” categories. As Machery (2009, pp. 175– 77; see also Smith & Minda 2000) correctly notes, these categories share few features, preventing prototype formation, and the stimulus sets are small. Participants see many repetitions of each stimulus and naturally memorize them. Rule-based tasks are clearly simply unidimensional in character, encouraging hypothesis testing, reasoning, and solution by rule formation. However, the progress in this area shows the method in the madness. Researchers have found that the dominant categorization process is strongly affected by the size of categories, their perceptual coherence, the dimensionality of the task’s sufficient solution, the stage of category learning, the default tendencies of the categorizing organism, and so forth (Ashby & Maddox 2005; Blair & Homa 2003; Couchman et al., in press; Homa et al. 1981; Murphy 2002; Smith & Minda 1998; 2000). Moreover, it is clear that these generalizations are not just isolated findings. It is insufficient to claim that the effect of stimuli on categorization is captured completely by a description of prototypes, exemplars, or theories (as Machery [2009, sect. 8.3.2] requires). Rather, organisms tune sensitively to the affordances of their category experience in order to choose the most cognitively economical and adaptive learning solution they can. Viewed from a broader perspective, these different strategies are instances of organisms navigating upon a multi-dimensional fitness surface. But the goal, the navigation, and the surface all embody a unitary fitness potential that must not be overlooked and that really is not divisible. This broader perspective also raises many important theoretical questions. What species have which categorization potentialities and which constraints? What were the antecedents of humans’ categorization system in the vertebrates or primates? What are the affordances of language and symbolic reasoning, and how do they change the nature of concept formation? What are the developmental stages by which humans acquire their mature concepts and adaptive action patterns in the world? What are the implications of cognitive development, cognitive aging, and neurological impairments for category

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learning, both regarding constraints and regarding preserved or spared capacities on which remediation could capitalize to maximize education and training? Replacing “concept” with “prototype,” “exemplar,” or “theory” as Machery (2009, p. 242) suggests would unwarrantedly deemphasize these important questions. In our view, answering all these questions is also best served by understanding the unitary nature of the task at hand. That task is to carve nature at its joints using the psychological knife called concepts. It is true, it is profoundly important to know, and it is all right for the progress of science that the knife is Swiss-Army issue with multiple blades.

Not different kinds, just special cases doi:10.1017/S0140525X1000052X

David Danks Carnegie Mellon University and Institute for Human & Machine Cognition, Department of Philosophy, Pittsburgh, PA 15213. [email protected] http://www.hss.cmu.edu/philosophy/faculty-danks.php

Abstract: Machery’s Heterogeneity Hypothesis depends on his argument that no theory of concepts can account for all the extant reliable categorization data. I argue that a single theoretical framework based on graphical models can explain all of the behavioral data to which this argument refers. These different theories of concepts thus (arguably) correspond to different special cases, not different kinds.

One of Machery’s central arguments for his Heterogeneity Hypothesis in Doing without Concepts (Machery 2009) is that, for each different theory of concepts, there are reliable datasets – behavioral, neuroscientific, and dissociation – that are best (or only) explained by that theory. That is, prototypebased, exemplar-based, and theory-based kinds of concepts are all required in order to explain all of the data. Thus, Machery concludes, we have heterogeneity: for many of the cognitive items thought to be single concepts, people have cognitively distinct prototype-like, exemplar-like, and theory-like kinds of concepts. This argument requires that these three theories/kinds of concepts be qualitatively different, not special cases of a more general framework. If there were a unifying account that encompassed all three theories of concepts, then that theory would (by hypothesis) be able to explain all the same data as the heterogeneity hypothesis that Machery proposes in his book. Hybrid theories of concepts are the most prominent instances of such a unifying account, and Machery provides numerous arguments against them precisely because they have the potential to undermine his data-based argument for the heterogeneity hypothesis. Hybrid theories are not, however, the only way to unify the three theories of concepts. The behavioral data used in Machery’s argument can all, I contend, be explained by a single theory of concepts based on graphical models. At a (very) high level, a graphical model encodes a set of relationships, whether informational/probabilistic, causal, communication, taxonomic, or other. More formally, a graphical model has two components: (1) a graph composed of nodes/vertices and edges (directed and/or undirected) that encode the qualitative relationships; and (2) a representation of the quantitative relationships. The precise quantitative component depends on model-specific features; joint probability distributions and structural/linear equations are typical. Bayesian networks (causal or probabilistic), structural equation models, and Markov random fields/networks are probably the most common graphical models, though there are many other types. (Lauritzen [1996] provides a comprehensive formal overview of graphical models.)

Commentary/Machery: Pre´cis of Doing without Concepts One instance of the theory-theory (causal model theory) is already explicitly formulated in the language of graphical models (specifically, causal Bayesian networks). A deeper connection is suggested by the fact that all three theories of concepts ultimately understand concepts as structured relations among features, components, causes and effects, and so on, though there are between-theory differences in the nature of the objects and relations. The suggested deep connection exists: All three theories of concepts can provably be represented in the graphical models framework (Danks 2004; 2007). More precisely, for each particular prototype-based, exemplar-based, or theory-based1 theory of concepts, there is a corresponding class of graphical models such that (A) there is a one-to-one mapping between (i) a concept in the “traditional” psychological theory, and (ii) a particular graphical model in that class; and (B) inference/reasoning using that graphical model is behaviorally indistinguishable from using the corresponding prototype-based, exemplar-based, or theorybased concept. As a concrete example, multiplicative prototype concepts with second-order features are isomorphic to Markov random fields with restricted clique potential functions. (Danks [2004; 2007] proved this result for categorization; the results have since been extended to all of the activities that Machery discusses.) These three “kinds” of concepts are (at least, formally) each representable as a different set of restrictions within the shared graphical models framework, specifically the framework of so-called chain graphs. These formal results open the door for homogeneity to reemerge as a live possibility. Of course, this homogeneity occurs at the level of graphical models, rather than exemplars versus prototypes versus theories. An individual can have what appear to be both prototype-based concepts and exemplarbased concepts simply by having different specific graphical models. The force of the argument for heterogeneity is thus blunted: There is a single theory that can account for all of the (behavioral) data. Importantly, this proposed theory is not a hybrid theory: It does not hold that a single concept is composed of different graphical models corresponding to the different types of concepts. Rather, the proposal is that any particular concept corresponds to only one graphical model, and diversity in our graphical models leads to apparent diversity in types of concepts. This account focuses on the formal/computational structure of the different theories of concepts, and one might object that it ignores other, more “metaphysical” claims made by their proponents. This concern goes to the hard question of how to define or characterize a theory. At one extreme, we could define a theory (of concepts) by its behavioral predictions, and ignore all other assertions made about the theory. At the other extreme, we could say that a theory is given by a maximal set of consistent claims made by proponents of that theory, regardless of the nature of those claims. The graphical models account takes the intermediate view that a theory should be interpreted relatively minimally as the set of claims necessary to explain the phenomena that purportedly fall within its reach (similar in spirit to the approach in Strevens 2000). I contend that (though I do not have room to argue for) the proper “minimal” understanding of theories of concepts is in terms of the formal/computational structure that they attribute to particular concepts. Although there is a clear rhetorical difference between saying a concept is based on a “prototype” versus an “exemplar,” I contend that the difference matters only when it leads to a difference in the concept’s formal/computational structure. Machery provides an admirable survey of the vast behavioral/ psychological literature on concepts, and his conclusion – there are at least three distinct kinds of concepts – is reasonable if there is no unifying framework for those “kinds.” But all of the behavioral data that Machery discusses can in fact be explained as bodies of knowledge encoded as graphical models: The differences in behavior can be explained as different graphical

structures – that is, different special cases – rather than truly distinct kinds. ACKNOWLEDGMENT The author is partially supported by a James S. McDonnell Foundation Scholar Award. NOTE

1. Statements about “the” theory-theory of concepts apply only to the few, computationally precise instances of that idea (e.g., causal model theory).

An additional heterogeneity hypothesis doi:10.1017/S0140525X10000348 Guy Dove Department of Philosophy, University of Louisville, Louisville, KY 40297. [email protected]

Abstract: In this commentary, I make three points concerning Machery’s response to neo-empiricism. First, his methodological critique fails to remove the threat that neo-empiricism poses to his conceptual eliminativism. Second, evidence suggests that there are multiple semantic codes, some of which are not perceptually based. Third, this representational heterogeneity thwarts neo-empiricism but also raises questions with respect to how we should “do without concepts.”

Machery (2009) provides a substantial and convincing argument that prototypes, exemplars, and theories form distinct mental kinds. The real question is whether or not this heterogeneity should cause us to eliminate concepts as a mental kind. Neoempiricism (e.g., Barsalou 1999; Glenberg 1997; Prinz 2002) poses a direct threat to this proposal because it provides a unified account of concepts. As Machery notes, neo-empiricism involves two core claims: that conceptual knowledge is encoded in sensorimotor representations and that conceptual processing involves some kind of perceptual simulation. If these claims apply to prototypes, exemplars, and theories, then Machery’s case for eliminativism falls apart. This is not simply a hypothetical argument; at least one prominent neo-empiricist (Prinz 2002) endorses both the independence of prototypes, exemplars, and theories and the theoretical unity of concepts. In Doing without Concepts, Machery claims that neo-empiricism faces two main problems. The first is that certain amodal models (i.e., ones containing non-perceptual representations) are compatible with the empirical findings cited in support of neo-empiricism. The second is that many amodal theorists acknowledge that perceptual imagery is important to some cognitive processes. Machery (2009, p. 116) concludes that “there is no strong evidence that concepts (or some concepts) are in fact similar to perceptual representations.” This argument is inconclusive, though, because it fails to provide strong evidence against perceptually based conceptual representations or, indeed, for amodal ones. In his Pre´cis, Machery identifies an additional problem for neo-empiricism, which he refers to as the generality problem. This problem arises because there are robust bodies of evidence suggesting that some specialized conceptual representations are amodal. Although the generality problem clearly undermines strong forms of neo-empiricism, it is compatible with weaker forms (Machery 2007; Weiskopf 2007). I suggest that neo-empiricism faces a more serious problem. This problem is not merely a lack of generality but rather the presence of heterogeneity. Evidence from a number of sources suggests that conceptual knowledge is encoded in multiple representational formats, some of which are not perceptual (Dove 2009). In other words, it supports what might be called the representational heterogeneity hypothesis (RHH). BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts The case for the RHH can be seen most clearly in recent neuropsychological and neuroimaging research on imageability. Traditionally, cognitive scientists examined imageability in terms of processing advantages for high-imageable concepts over lowimageable ones in several cognitive tasks (Paivio 1987; Wattenmaker & Shoben 1987). Two major theories emerged in response to the behavioral evidence: the dual-code theory (Paivio 1987) and the context-availability theory (Schwanenflugel & Shoben 1983). Recent evidence from cognitive neuroscience supports both to some degree (Dove 2009). Because my concern is the RHH, I will focus on the evidence for distinct representational formats. First, consider neuropsychological case studies. Several research teams describe aphasic patients with significant left hemisphere damage who exhibit a selective semantic impairment for high-imageable words (Berndt et al. 2002; Bird et al. 2003; Crepaldi et al. 2006). Patients with a selective semantic impairment for lowimageable words have also been found (Marshall et al. 1996). Second, a number of event-related potential (ERP) experiments support a neuroanatomical distinction between concepts of high and low imageability. For example, Holcomb et al. (1999) created a task that involved manipulations of both context and concreteness. ERP recordings were time-locked to sentence final words in a word-by-word reading task in which participants made semantic congruency judgments (e.g., Armed robbery implies that the thief used a weapon vs. Armed robbery implies that the thief used a rose). They found that sentence-final concrete words generated a larger and more anterior N400 than sentence-final abstract words in both contexts. Further studies have found context-independent topographic effects associated with imageability in single-word presentations (Kellenbach et al. 2002; Swaab et al. 2002). Thus, ERP studies employing diverse tasks support the notion that different cognitive systems are associated with the semantic processing of high- and low-imageable words. Third, the idea that neural activity is modulated by imageability is generally supported by neuroimaging data. A number of studies find that high-imageable words elicit greater activation than low-imageable words in superior regions of the left temporal lobe (Binder et al. 2005; Giesbrecht et al. 2004; Mellet et al. 1998; Noppeney & Price 2004; Sabsevitz et al. 2005) and inferior regions of the left prefrontal cortex (Binder et al. 2005; Giesbrecht et al. 2004; Goldberg et al. 2006; Noppeney & Price 2004; Sabsevitz et al. 2005). Giesbrecht et al. (2004), for example, manipulated both imageability and semantic priming (a measure of the influence of context) in an event-related fMRI study. Participants were presented with prime word followed by a target word. The words were either semantically related (bread and butter) or unrelated (wheat and slipper). Half of the pairs consisted of two high-imageable words, and half of the pairs consisted of two low-imageable words. In keeping with the general finding that context effects are distinct from imageability effects, each of these manipulations modulated activity in anatomically distinct areas of the left hemisphere. The RHH seems to be grist for Machery’s mill. It certainly undermines the threat posed by neo-empiricism to unify concepts. In keeping with this, Piccinini and Scott (2006) have argued that the divide between cognitive processes that require language and those that do not provides a better case for conceptual eliminativism than the heterogeneity identified by Machery. However, the RHH also raises an interesting problem. Because there is every reason to suppose that prototypes, exemplars, and theories have high- and low-imageable variants, these kinds are likely to be representationally heterogeneous. Following Machery’s reasoning, this should threaten their status as theoretical kinds. It seems reasonable to resist this inference, but then the challenge is to provide an argument for resisting eliminativism in the context of prototypes, exemplars, and theories that does not apply to the larger context of concepts. Whether this can be accomplished or not remains to be seen.

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Unity amidst heterogeneity in theories of concepts doi:10.1017/S0140525X10000543 Kevan Edwards Philosophy Department, Syracuse University, Syracuse, NY 13210. [email protected] http://philosophy.syr.edu/FacEdwards.htm

Abstract: This commentary raises two concerns with Machery’s approach in Doing without Concepts. The first concern is that it may be possible to preserve a unified theory of concepts by distinguishing facts about concept individuation from facts about cognitive structures and processes. The second concern questions the sharpness of the distinction Machery draws between psychological and philosophical conceptions of concepts.

I think Machery is entirely right, in Doing without Concepts (2009), to draw attention to the phenomenon that he refers to as the heterogeneity of concepts (as the term “concept” is used in cognitive psychology). I also agree that this stands in the way of any unified account of concepts as exemplars, prototypes, or constituents of theories. Nevertheless, I take issue with Machery’s eliminativist conclusion. The logical heart of my worry is that heterogeneity across exemplars, prototypes, theories, and so on, precludes a unified theory of concepts only insofar as one assumes that such a theory needs to be built out of the kinds of structures and processes described by advocates of these approaches. I think this assumption is worth questioning, and I want to suggest an alternative approach that rejects it. The alternative begins with the idea that concepts are individuated by considerations more abstract than their role in cognitive structures and processes. The specific version of this idea that I favor individuates concept types in terms of the individuals, kinds, or properties they represent or refer to (see Edwards 2009; 2010). Before saying more about this approach, I want to mention another issue that might partially explain why Machery is relatively blind to – or perhaps it is more charitable to say, disinterested in – this kind of alternative. When it comes down to how to construe the starting point for a theory of concepts, Machery (2009, pp. 32 – 51) is quick to side with psychologists as opposed to philosophers. Machery chooses sides in part to undermine various philosophical concerns on the grounds that philosophers are engaged in a fundamentally different research project. I think Machery may be overlooking some potentially relevant issues here. Just to be clear, I agree that philosophers typically begin from a different starting point than psychologists and tend to emphasize different considerations. However, this is always the case with topics located at the intersection of academic disciplines. Admittedly, this realization does not show that philosophers and psychologists are converging on the same theoretical entity in this particular case. Nevertheless, I think one ought to be very cautious in concluding that different apparent starting points amount to a fundamental difference in subject matter. Given space constraints, I can’t properly address Machery’s claims on this subject; but I’ll briefly mention several issues regarding which philosophers may be able to contribute to how psychologists think about concepts. One topic to highlight – not surprisingly – is the notion of representation. It seems to me this is a place where philosophers and psychologists tend to take recognizably different approaches. Psychologists, even those who clearly are working within the framework of a Representational Theory of Mind, often appear to presume something in the vicinity of representational content without making this explicit. Consider, for example, how natural it is to talk about exemplars or prototypes being of or about a category. In contrast, philosophers, in particular philosophers of mind, have been something close to obsessed with either explaining such talk in naturalistically respectable terms or explaining how to do without it. Examples of the former

Commentary/Machery: Pre´cis of Doing without Concepts include Dretske (1981), Fodor (1987; 1992), Millikan (1984; 1993); examples of the latter include Field (2001), Horwich (1998a; 1998b). I think Machery’s neglect of these discussions partially explains why a representation-based view of concepts isn’t on his radar. I should note that I am here echoing concerns raised by Rey (2009b). Another topic much discussed in recent philosophy can be used to shed light on the potential relationship between representation and more blatantly psychological considerations: I have in mind the debate between so-called reductionists and non-reductionists, especially insofar as this has involved questions about multiple-realization (see, in particular, the debate between Fodor (1974; 1997) and Kim (1992; 1998). Speaking very roughly, the upshot of this debate has been that both antireductionists and their opponents have confronted the fact that reality exists (or is properly describable/conceptualized) at different levels of abstraction. At a minimum, this forces one to be careful about the level at which a particular theoretical entity is construed. The cost of picking too low a level of analysis is that one wrongly identifies the target entity with a disjunction of lower-level entities each of which is better described as a realization (or instance) of the target. To repeat a now hackneyed example, it is a mistake to identify doorstops with the arrangements of physical stuff (slices of wood, hinged pieces of metal, bricks, old printers, etc.) out of which particular doorstops are made. The now standard functionalist line is that something is a doorstop by virtue of playing a characteristic role in a larger system; for example, a system involving doors, people, and so on. Someone who holds a representational view of concepts can make a similar suggestion. Type-individuating concepts in terms of their representational properties frees up the possibility for different instances or realizations of a concept type to show up in substantially different cognitive structures and processes. The alternative approach to which I have gestured brings many questions in its wake. I have not, for example, supplied arguments for the conclusion that a representationalist view of concepts should supplant rather than supplement accounts that appeal to mental structures and processes. Similarly, I have not said anything about whether embracing a representationalist alternative forces a substantive (rather than merely terminological) shift in work done by advocates of exemplar, prototype, and theory-based approaches. Moreover, there are familiar reasons to worry about a representation-based approach. The goal of the present commentary is to make two relatively modest points: (1) There is an admittedly radical approach to concepts that needs to be undermined before one opts for eliminativism. (2) Various issues that have featured prominently in recent philosophy have the potential to impinge upon discussions of concepts in psychology, and to do so in potentially important ways.

Two uneliminated uses for “concepts”: Hybrids and guides for inquiry doi:10.1017/S0140525X1000035X Chad Gonnerman and Jonathan M. Weinberg Department of Philosophy, Indiana University, Bloomington, Indiana 47405. [email protected] [email protected] http://www.indiana.edu/eel

Abstract: Machery’s case against hybrids rests on a principle that is too strong, even by his own lights. And there are likely important generalizations to be made about hybrids, if they do exist. Moreover, even if there were no important generalizations about concepts themselves, the term picks out an important class of entities and should be retained to help guide inquiry.

We concur with Machery’s broad assessment in Doing without Concepts (Machery 2009) that the science of concepts has

revealed matters to be rather a mess. But we strongly disagree with what Machery takes as the philosophical upshot of that messiness, as expressed in his book’s title: that psychologists should scrub “concept” from their lexicon. First, the hybrid option is still live, and if concepts are hybrids, they may well be natural kinds. Second, even if “concept” fails to pick out a natural kind, it may yet be scientifically useful, even on Machery’s own terms. Machery correctly observes that hybrid theorists owe an account of when two bodies of knowledge are parts of the same concept. He proposes that psychologists are committed to the principle that they are genuine parts only if they are coordinated (Machery 2009, p. 64), that is, if they generate consistent evaluations of cases. But he adduces some evidence that coordination often fails across parts of putative hybrids: for example, one representation says tomatoes are vegetables; a bit later, the other says they are not (p. 72). But this coordination condition is too strong, as it would break up bodies of knowledge that psychologists treat as conceptual wholes, namely, sets of exemplars. Contextual shifts, such as those that adjust the perspective of the subject (e.g., from artist to biologist), can, within selective-retrieval models, shift which exemplar is retrieved, even when both exemplars are stored as being about the same thing – and thereby can shift, for particular cases, whether they will be counted as in the target category (see Braisby 2005). Sets of similarly stored exemplars would thus fail Machery’s coordination condition – forcing their dissolution, likely down to individual exemplars. But that’s not how psychologists treat such exemplars. His argument against hybrids proves too much. That the coordination condition is too strong does not remove the need for hybrid theorists to provide an account of conceptual wholes. Here is one proposal, building on machinery used much earlier in Machery’s book, when he attempts to distinguish conceptual from background knowledge (pp. 11 – 12). There, he suggests that a body of knowledge about x is in the concept of x just in case it is “preferentially available when we think, reason, and so on, about x” (p. 11). There is nothing unique to a hybrid that keeps its fans from saying such things about its subconceptual parts. A hybrid theorist could therefore propose that two coreferential representations are parts of the same concept of x just in case each is preferentially available in such a way when we think, reason, and so on, about x. (We note that Machery’s account of conceptualization may require some technical tweak for Frege cases; we expect that whatever would do that for his account, would also work for this proposal.) But a failure of coordination is irrelevant to this weaker condition. Whether this proposal succeeds is an open question, and that is sufficient for our purposes here in contending that hybrids are still a live option. For, if “concepts” picks out hybrids, psychology should probably keep the term, as there will likely be informative generalizations to be made. As Machery argues, empirical research suggests that for most categories we have a prototype, a body of exemplars, and a theory, but not a definition. So, a hybrid theorist would be able to theorize that most concepts partially consist of a prototype, and so on, but not a definition. But we cannot read such generalizations off of Machery’s reference-fixing description (p. 12); they are discoveries. So, if concepts are hybrids, “concept” probably picks out a class that supports many informative, useful generalizations. Even if the above is incorrect about hybrids, “concept” would still have important work to do. As Machery suggests, scientists should keep a term if it plays a useful role (p. 239). “Concept” does that, by efficiently marking out a class that scientists want to make claims or ask questions about, even if it should turn that there are vanishingly few generalizations to be made about something merely in virtue of its being a concept. There are many terms in good scientific standing across various disciplines that play such roles, including, we would suggest, “algorithm,” “sub-atomic particle,” or “nutrient” (and even some specific nutrients, such as “vitamin B”; see Elder 1994, p. 259.) None of these categories seems to support many generalizations about BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts their members qua members, yet each is very useful in organizing established knowledge and continuing inquiry in their respective domains. And the same holds for “concept.” For example, the neo-empiricist, friendly to Machery’s general take on our conceptual systems, might want to defend the substantive claim that perceptual symbols are a kind of concept. Such a claim would be substantive, to the effect that the delineated class contains some additional sorts of entities. Indeed, Machery himself wonders whether there are other kinds of concepts (p. 249). For example, he writes, “Evidence shows that people have some knowledge about ideals. What is now needed is to determine whether these bodies of knowledge qualify as concepts” (p. 249). This strikes us as a meaningful and important question, and one for which the term “concept” is obviously useful in asking. So, independent of the question of hybrids, psychologists should keep the term. Even if Machery is right, and concepts are not a natural kind, the potential dangers here would be better addressed through reformation instead of elimination. The practical advice to take away from Machery’s arguments may be, not that scientists should get rid of “concept,” but that they should be more careful in understanding that this term likely fails to pick out a very tidy sort of natural kind. Doing so should allow them to steer around the sorts of dangers that Machery (2009) hypothesizes about (e.g., pp. 242–243), without sinking an otherwise fruitful vehicle of inquiry. The psychology of categorization, inference, and so on may be much messier than philosophers and psychologists have hoped. But “concept” is still likely to be a vitally important word for theorizing about that mess.

Concept talk cannot be avoided doi:10.1017/S0140525X10000361 James A. Hampton Department of Psychology, City University London, London, EC1V OHB, United Kingdom. [email protected] www.staff.city.ac.uk/hampton

Abstract: Distinct systems for representing concepts as prototypes, exemplars, and theories are closely integrated in the mind, and the notion of concept is required as a framework for exploring this integration. Eliminating the term “concept” from our theories will hinder rather than promote scientific progress.

While most people interested in concepts will find much to agree with in this book (Doing without Concepts, Machery 2009), it is the eliminativist thesis that will find most resistance. Machery provides analogical cases in psychology such as “emotion” and “memory.” Emotion and memory, it is argued, may prove to be terms referring to a varied set of phenomena, without any identifiable single associated brain system. Similar cases can be found in other sciences – for example, “species” and “planet.” The concept of species is problematic because there is not always a clear criterion for differentiating one species from another; instead biological laws describe the distribution of genes over populations of individuals (Mayr 1982). While problems of definition mean that “species” is not a well-defined term in biology, it would, however, be hard to imagine biological discourse without it. There are just too many general truths that need to be expressed. Similarly, astronomers ran into trouble with the designation of Pluto as a planet, given the discovery of other large orbiting bodies that had been labeled as asteroids. But the term still has a referential meaning. Science needs more loosely defined general referring expressions in addition to the carefully defined terms that figure in theories. I argue that cognitive science still needs the notion of “concept,” even if it proves multifaceted and hard to define satisfactorily. Machery’s argument rests on there being three distinct forms of knowledge that are recruited by default by cognitive processes:

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namely, prototypes (P), exemplars (E), and theories (T). The danger of eliminating the notion of concept is that the importance of the relations between these forms of knowledge risks being underplayed. First, there is the obvious point that the P, E, and T representations (let’s call them PET) of dog all refer to the same class – they are broadly co-referential (give or take some differences in categorization resulting from exceptional contexts). What makes them co-referential is the fact that they represent the same concept. Without a notion of concept, it is hard to explain why they co-refer. More importantly, the term “concept” is needed as part of an account of the many situations in which the PET systems interact. How does one discuss concept combination, including the formation of composite prototypes, the importing of exemplar knowledge, and the coherence checking of the result through background theory, if one cannot have the integrative term “concept” to specify just what it is that is being combined. The combination occurs at the concept level, and the description of the processes involved then requires elaboration in terms of the PET systems. Similarly, in concept learning, we need an overarching notion of concept in order to describe how PET systems interact. Experiential concepts like DOG or CUP may first be learned by a child through interacting with individuals encountered in everyday life. When a variety of individuals are known, and it is necessary to learn to use the words “dog” and “cup” correctly, then prototypes may be formed, enabling generalization to other individuals, discrimination of other classes, and the accumulation of generic knowledge. As the child then develops wider knowledge, the prototype notion of DOG may be supplemented by theoretically driven concepts like mammal or species, and by essentialist ideas about the causal properties of biological kinds, or the need to defer to expert opinion about correct classification. Far from aiding scientific advance, treating the PET systems as largely independent of each other may impede investigation of the important ways in which information is transferred between them. It can also be argued that the three systems are not as easily distinguished as Machery would require. Consider prototypes and exemplars. Machery agrees that much of the research and debate concerning prototypes and exemplars has been directed at a very restricted form of behavior, namely, learning to classify simple geometrical shapes in a laboratory setting where the categories to be learned are not easily distinguished without extensive training. Even in this arcane area of psychology, there is considerable evidence that under different conditions people will either learn individual exemplars or will abstract prototypes (Smith & Minda 1998). If we move to the more “conceptual” domain of natural language terms, then the question of prototype versus exemplar models hardly arises. For example, Storms et al. (2000) have investigated whether typicality in superordinate categories like FISH, FRUIT, or FURNITURE is best predicted by similarity to the category prototype or by similarity to “exemplars.” But in this case the exemplars are simply prototypes defined at a more specific level (e.g., CHAIR and TABLE). So the question is not which of two distinct systems is driving the behavior, but rather which level of abstraction is involved within a single representational system. Some concepts do have genuine exemplars – the concept of “Beethoven Symphony” to a musician will be heavily dependent on knowledge of the nine exemplars. But there will be a close link between knowledge of the exemplars and generalized knowledge about the typical structure and expressive vocabulary found in the works. Likewise, there has been a rapprochement between prototype and theory-based elements of concepts. In discussing the notion of prototype (Hampton 1998), I have proposed that the distinguishing feature of prototype representations is that they represent the center of a class and not its boundary. It is this fact that gives rise to category vagueness, typicality gradients, the lack of explicit definitions, and the preponderance of generic (rather than necessary) features in people’s accounts of the content of

Commentary/Machery: Pre´cis of Doing without Concepts the concept. The notion of prototype as a form of schema is therefore free to be supplemented by causal connections within the representation resulting in a structured frame representation (Barsalou & Hale 1993). Mutability and centrality of properties, modal judgments of necessity, and dissociations between similarity-based typicality and theory-based categorization can all be accommodated within this single representational system. In short, it is too soon to be counseling despair about integrating prototype, exemplar, and theory-based representations into a coherent account of the concept of concept.

Eliminating the “concept” concept doi:10.1017/S0140525X10000567 Stevan Harnad Institut des Sciences Cognitives, Universite´ du Que´bec a` Montre´al, Montreal, Canada, H3C 3P8; School of Electronics and Computer Science, University of Southampton, SO17 1BJ Southampton, United Kingdom. [email protected] http://users.ecs.soton.ac.uk/harnad

Abstract: Machery suggests that the concept of “concept” is too heterogeneous to serve as a “natural kind” for scientific explanation, so cognitive science should do without concepts. I second the suggestion and propose substituting, in place of concepts, inborn and acquired sensorimotor category-detectors and category-names combined into propositions that define and describe further categories.

Whatever a “concept” is, we have at least one for every thing we can recognize, act on, name, or describe, including not only the things denoted by all the dictionary words we understand, but also everything we know what to do with (Harnad 2007), even if we don’t know its name or it has none – perhaps because, like “things that are bigger than a breadbox,” no one has ever bothered to name it. “Things” can be individual objects (nonliving or living), kinds, events, actions, properties, or states. We have “concepts” of countless such things, and having the concept simply means being able to do something with respect to those things, an action that has a right and wrong about it – anything from approaching/avoiding the thing, to interacting with or manipulating it in some way, identifying it (correctly) by name, saying true things about it, imagining it, and thinking and reasoning about it. In Doing without Concepts, Machery (2009) suggests that although there is no “natural kind” corresponding to the intersection of prototypes, examples, theories, and sensorimotor representations, each may still turn out to be a legitimate natural kind of its own. I will sketch an alternative that scraps both the use and the mention of “concept” altogether. Consider concept’s twin, “percept.” If a concept is, roughly, an “idea,” then a “percept” is an “image.” Should we ban talk of percepts, too? Pylyshyn (1973) suggested banning talk of “images” – as unobservable, unmeasurable, homuncular, and, most important, nonexplanatory – to be replaced by propositions, and, eventually, computations, which are genuinely explanatory, in that they can generate the capacity that the images or “percepts” had been meant to explain (Harnad 2006). With findings on mental rotation (Shepard & Cooper 1982), however, “percept” has made a comeback, in the form of internal analog structures and processes that have some of the properties of images but can do the internal generative work, with no homunculus, sometimes more efficiently than computation. (Digital computation can always approximate analog dynamics as closely as we like: A picture is always worth more than 1,000 words, but 10,000 words come closer. It cannot, however, be words all the way down; Harnad 1990.) Apart from their sensory shapes, objects have sensorimotor “affordances”: things that objects are amenable to having done with them (by our bodies, and their shapes). A chair (but not a

pyramid or a pincushion) affords sittability-upon; a doornail, but not a doormat, affords grasping and turning. But is an affordance-detector a “representation”? We need to be able to recognize birds, for example, before we can start doing anything with them, including talking and thinking about them. No machine vision program could perform anywhere near human level using prototype-matching to recognize birds; raw example-storage would do even worse. And without those, verbal theories could not even get off the ground (because it can’t be words all the way down). So what we need first is not bird representations, but birddetectors. For most of us, visuomotor contact is our first introduction to birds, but it is not “we” who pick up the affordances; we are no more aware of the tuning of our internal category detectors than subjects in mental-rotation experiments are aware of rotating their inner images. Internal mechanisms do this “neoempirical” work for us (Barsalou 1999; Glenberg & Robertson 2000). The work of cognitive science is to discover those mechanisms. That done, it no longer matters whether we call them concepts, ideas, notions, representations, beliefs, or meanings. Cognitive science has not yet done this job, though Turing (1950) set the agenda long ago: Scale up to a model capable of doing everything we can do (Harnad 2008). The first hurdle is sensorimotor category detection: the mechanism for learning categories from sensorimotor interactions with the world, guided by error-correcting feedback. We share this capability with most other species: learning to detect and act upon sensorimotor affordances. To categorize is to do the right thing with the right kind of thing (Harnad 2005). Some categories are innate: We recognize and know what to do with them because natural selection already did the “learning” by genetically pretuning our ancestors’ brains. But most categories we have to learn within our lifetimes, including everything named and described in our dictionaries plus many things, actions, events, properties, and states we never bother to name: We learn to do the right thing with them, and perhaps describe them, on the fly. How did we get those names and descriptions? Our species is the only one that has them. According to our account so far, we only have the categories for which we have learned through direct experience what to do with their members. One of the most adaptive things our species alone does with many of our categories is to name them. For, with language evolved our capacity to produce and understand strings of category names that encode truth-valued propositions, predicating something about something. This allowed us to acquire new categories not only by sensorimotor induction, but also by verbal instruction. For once we have a set of categories “grounded” directly in our sensorimotor capacity to detect their members and nonmembers, we can also assign each category an agreed, arbitrary name (Harnad 1990), and then we can define and describe new categories, conveying them to those who do not yet have them, by combining and recombining the names of our already grounded categories (Cangelosi & Harnad 2001) in propositions. Then and only then does the “theory-theory” come in, for verbal definitions and descriptions are higher-order categorydetectors, too, as long as all their component terms are grounded (Blondin-Masse´ et al. 2008). Here we are right to call them “representations,” for they are descriptions of categories, and can be given to and received from others without every individual’s having to learn the categories directly from experience – as long as the category-names used in those descriptions are ultimately grounded in direct sensorimotor categories. There is much ongoing research on the mechanisms of sensorimotor category learning in computers, neural nets, robots, and the brain, as well as on the origins and mechanisms of natural language processing. It is nowhere near Turing-scale, but this sketch rearranges the cognitive landscape a bit, to preview how we can, as Machery suggests, do “without concepts”: What takes their place is innate and mostly learned sensorimotor category-detectors (for which the learning mechanisms are still not known, but BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts neither prototypes nor exemplars are likely to play much of a role in them), progressively supplemented by verbal category representations composed of grounded category names describing further categories through propositions. The real challenge is getting this to work, Turing-scale. Alongside that momentous and substantive task, which of the landmarks we elect to dub “concepts” or “ideas” seems pretty much a matter of taste. [A fuller version of this commentary, entitled “Concepts: The Very Idea” is available at http:// eprints.ecs.soton.ac.uk/18029.]

Defending the concept of “concepts” doi:10.1017/S0140525X10000373 Brett K. Hayes and Lauren Kearney School of Psychology, University of New South Wales, Sydney, NSW 2052, Australia. [email protected] [email protected]

Abstract: We critically review key lines of evidence and theoretical argument relevant to Machery’s “heterogeneity hypothesis.” These include interactions between different kinds of concept representations, unified approaches to explaining contextual effects on concept retrieval, and a critique of empirical dissociations as evidence for concept heterogeneity. We suggest there are good grounds for retaining the concept construct in human cognition.

The past decade has seen prolific growth in research on human concepts, both in terms of the phenomena studied and the generation of explanatory models. With research proceeding on so many fronts it is tempting to see the field as becoming fragmented with little prospect of a unified theory. However, it is hard to see how the field would be advanced by assuming that individual objects and events are represented by “several concepts” (Doing without Concepts, Machery 2009, p. 52), especially when the properties of these alternate concepts and the demarcation between them have not been specified. Moreover, we argue that there are good reasons for rejecting the “heterogeneity hypothesis.” Here we identify three problems with this view. 1. Are objects really represented in multiple ways? It is true that a variety of theoretical models (prototype, exemplar, theory-based) have been proposed to explain how concepts are represented and used However, Machery’s implication that each has equal explanatory power and therefore warrants status as a separate kind of concept is incorrect. While there are certainly limits to the things that exemplar models can explain (see Murphy 2002), there is also little doubt that exemplar models do a better job of explaining laboratory data on category learning (see Kruschke [2005] for a review) and other key phenomena (e.g., the effects of category-irrelevant features on classification, as in Allen & Brooks 1991) than models which only assume storage of prototypical features. The “theory” approach was originally proposed to explain aspects of conceptual experience that lie outside the purview of “data-driven” approaches, including sensitivity to conceptual coherence and the causal basis of categories (Murphy & Medin 1985). However, there have been significant advances towards integration of data-driven and theory-based approaches. Heit’s (1994a) integration model incorporates prior knowledge into an exemplar model to successfully predict patterns of category learning in knowledge-rich domains (Carmichael & Hayes 2001). In Rehder and Murphy’s (2003) KRES model, background knowledge is represented in a connectionist network to explain how data-driven category learning is accelerated in the presence of knowledge and how ambiguous features are reinterpreted in the light of feedback. Similar efforts to integrate data-driven and knowledge-based approaches have met with considerable success in explaining category-based induction (e.g., Kemp & Tenenbaum 2009). Note that these are not “hybrid models” in

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the sense used by Machery. They do not assume separate “exemplar” and “knowledge” modules. Instead, prior knowledge is represented in a format similar to that used for learning new exemplars (e.g., the integration model instantiates prior knowledge as retrieval of relevant exemplars from memory; KRES does it via feature to feature associations and similarity between known and novel concepts). These models show that the conceptual knowledge associated with “theory” and “exemplar” approaches need not be thought of as independent and that their interaction can be accounted for within a unified theoretical framework. 2. Different representations for different tasks? One of the key sources of evidence cited in support of the heterogeneity hypothesis is that different aspects of object knowledge are activated in the service of different goals or tasks. Classification learning, for example, focuses attention on the differences between categories, whereas inductive predictions rely on knowledge of within-category structure (Markman & Ross 2003). The first thing to note is that this kind of flexibility is not limited to concepts. It is a ubiquitous feature of human cognition. For example, changes in retrieval conditions lead to important changes in the kinds of memories that are activated (Roediger 2008), but this does not mean that we should abandon the notion of object “memory.” Second, it is important to recognise that empirical findings pointing to the “context dependency” of concepts can be oversold. A consistent empirical finding is that although context is important, a core conceptual representation of a given object is often retrieved when the object is used in a variety of conceptual tasks (Markman & Ross 2003). For example, taxonomic features appear to be activated by default in a variety of tasks involving biological concepts, whereas the activation of causal relations is dependent on task goals and domain experience (Shafto et al. 2007). Finally, and most critically, it is possible to develop models that have the flexibility to explain context dependent changes in concept retrieval – SUSTAIN (Love et al. 2004) is a good example. Such models achieve flexibility by incorporating wellestablished psychological processes, such as selective attention and discrimination learning, and the use of a range of similarity metrics. However, they do so while retaining an assumption of a conceptual “core” in object representation. As well as explaining data that Machery claims are problematic for standard models, these approaches may be extended to examine how conceptual knowledge influences performance in domains such as problemsolving (Markman & Ross 2003) and recognition memory (Heit & Hayes 2008), which have traditionally been divorced from the study of concepts. 3. What kind of evidence is needed to test the heterogeneity hypothesis? Machery suggests that the heterogeneity hypothesis

is supported to the extent that we can identify experimental or neuropsychological dissociations across different conceptual tasks. Although similar views are frequently espoused in the psychological literature (e.g., Ashby & Maddox 2005), they should be treated with considerable caution. Deciding whether a given data pattern supports models that posit a single causal process (e.g., exemplar memory) or multiple, independent processes is a complex and tricky business. Patterns of single or double dissociations in categorization performance across tasks or patient populations can be produced by single-process models (e.g., Newell & Dunn 2008). Rather than simply assuming that patterns of dissociation point to a particular kind of cognitive architecture, future progress in mapping conceptual representations will need to apply rigorous analytical techniques such as state-trace analysis (Dunn 2008) and a careful comparison of well-articulated single- and multipleprocess models. The empirical work summarised by Machery suggests that our concept representations are complex and multifaceted. However, we argue that the balance of empirical and modelling work shows that the notion of “a concept” remains a useful heuristic in psychological explanation.

Commentary/Machery: Pre´cis of Doing without Concepts

The faux, fake, forged, false, fabricated, and phony: Problems for the independence of similarity-based theories of concepts doi:10.1017/S0140525X10000385 Anne J. Jacobson Department of Philosophy, University of Houston, Houston, TX 77204-3004. [email protected] www.class.uh.edu/phil/jacobson

Abstract: Some things in our environment are not what they seem, and they provide a challenge to theories of concepts that emphasize similarity. Section 1 of my commentary explores a dilemma this situation creates for Machery. Section 2 describes a more general problem for prototype and exemplar theories. Section 3 locates a place for similarity-based concepts, and indicates an alternative to Machery’s thesis. 1. A dilemma for Machery. The large number of words we have

for potentially deceptive things attests to the fact that they label a phenomenon that is important to us. Nonetheless, the knowledge often essential to distinguishing between the real thing and something else – causal knowledge – is left out of the prototype and exemplar accounts. Thus, Machery (2009) says, “neither prototypes nor exemplars store causal knowledge,” and so tasks that require subjects “bring some causal knowledge to bear” (Doing without Concepts, p. 187) are evidence for a third theory of concepts, the theory or knowledge account. As a consequence, very good mimics and imitations, whose similarity to the real thing is often undetectable to the casual observer, answer to the criterion given by prototype and exemplar theories. Many red pandas look a great deal like raccoons, but they are not raccoons (Flynn et al. 2000; Sato et al. 2009). Seeming gold rings may be made of copper, cubic zirconias fool many people who take them to be diamonds, and gopher snakes are close indeed in appearance to rattlers, though they are not poisonous. Machery argues that typically we have at least three independent but co-referential concepts for each kind of thing: a prototypical concept, an exemplar concept, and a knowledge/theory concept (see Murphy [2002] for the “knowledge” label). But there is a problem: Cases of good fakes (e.g., cubic zirconias vs. diamonds) either fit a correct use of the prototypical or exemplar concept or they do not. Suppose they do; if so, then the prototypical or exemplar concept of a diamond will apply to things that the knowledge/theory account concept does not, and the two will not be co-referential. Alternatively, suppose that cubic zirconias that fit the prototypical concept are not correctly called diamonds. In this case, their independence is threatened. The criteria given by the knowledge/theory account are able to overrule those of the other two. Machery resists the idea that the knowledge/theory concept can so dominate (Ch. 3, sect. 3.3). However, given his claim that the sentence, “Tina Turner is a grandmother” (Machery 2009, p. 72) is true under one interpretation and false under another, the example shows that if one type does not dominate, different uses of “grandmother” will vary in reference. The argumentative context in which Machery considers fakes leads him to restrict his attention to sentences. Further, his intuition does not clearly say that “Fake dollars are dollars” is false (2009, p. 72). But, as Machery insists, the range of the use of concepts covers far more, and intuitions about sentences do not settle whether criteria from the knowledge/theory account will overrule in practice. 2. A more general problem. The failure to encode causal knowledge creates another problem for prototype and exemplar concepts and, by implication, Machery’s thesis. Experimental work in Machery’s discussion of prototype and exemplar theories is largely represented by descriptions of testing subjects on lists, sentences, pictures, and drawings, including patterns of dots. In contrast, Murphy (2002, p. 60) remarks that his knowledge/ theory account rejects the idea that we “learn concepts in

isolation from everything else (as is the case in many psychology experiments).” But our classifications of objects need to work to identify and track them in a dynamical environment. That sort of task carries quite different demands; among other things, classification needs to yield some clues about how our environment will unfold. Infants have a capacity relevant to identifying kinds in a changing environment, one that Carey and Xu (2001) claim controversially does not appear until 12 months. That is, at some point infants expect kinds of objects to persist and not change into another kind. Before then, mere spatial-temporal continuity dominates; a duck moving behind a screen and an emerging rabbit do not have to be two distinct objects. Knowledge that kinds persist and do not turn into one another looks like causal knowledge of how things work; and Carey and Xu maintain it is preceded by other causal knowledge. Further, the presumption that kinds persist facilitates the acquisition of knowledge about how objects of some kind interact with their environment. Such knowledge is important because it enables one to anticipate some of what will happen. From this perspective, there is at least a tension between saying that concepts are important in classifying and saying that they do not encode causal knowledge. It is unclear why using non-causal concepts would have any survival advantages, except in situations where the tasks to be performed have restricted success conditions. We look at such a task in the next section. 3. Conclusion. Prototypes and exemplar theories may still characterize what is needed to fulfill some of the tasks vision needs to undertake. One is that of perceptual organization. Our vision at one stage is a succession of saccades; the successive-saccades stage leads to our experience of a scene of stable objects. This important result appears to be driven by gestalt groupings, but it can be facilitated by top-down categorizing that may in many cases be triggered by similarity to a prototype or exemplar ¨ g˘men 2007; O ¨ g˘men et al. 2006). Success in this sort of task is (O simply getting some organization. It can be achieved even if one is being taken in by fakes and the faux. The preceding material suggests that there are layers to our uses of concepts, ones distinguishable in terms of the complexity of the knowledge needed. In creatures made for action, perceptual organization is required for almost any vision-guided action. Its success conditions may be much less demanding than those of another task, anticipating how one’s environment is going to unfold. Finally, human cognitive life and public communication, among other things, have a considerable interest in getting it right, and not being taken in by potential deception. These different stages can be seen as posing less and more demanding tasks for our uses of concepts. Machery has instead posited multiple independent processes, but we have seen reasons for questioning the independence. That said, one should nonetheless expect that bold hypotheses from a subtle mind like Machery’s will prove more resilient than one might first expect. ACKNOWLEDGMENTS I am indebted to Josh Brown and Josh Weisberg for discussions of this material.

Hybrid vigor and conceptual structure doi:10.1017/S0140525X10000555 Frank Keil Department of Psychology, Yale University, New Haven, CT 06520. [email protected]

Abstract: Machery rightly points out a diverse set of phenomena associated with concepts that create challenges for many traditional

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Commentary/Machery: Pre´cis of Doing without Concepts views of their nature. It may be premature, however, to give up such views completely. Here I defend the possibility of hybrid models of concept structure.

In Doing without Concepts, Machery (2009) provides a service to us all by reminding us of the challenges of specifying what concepts are and how they are mentally represented. Moreover, by moving to the radical position that we should do away with the concepts altogether, he forces all of us to think more deeply about why we might want to preserve such notions. Finally, he is correct in pointing out the ways in which philosophical and psychological approaches to concepts often seem to be asking and answering different questions. Despite all this, it seems too extreme to assume that more traditional notions of concepts are bankrupt or that philosophers and psychologists are always talking past each other. Here I focus on one alternative to Machery’s proposal that he seems to dismiss too lightly – the idea that concepts might have a hybrid structure. I am not yet sure about the extent to which the potentially hybrid facets to concepts are actually parts of the concepts per se, or whether they should instead be considered as linked to concepts that are themselves much simpler atoms in the manner described by Fodor (1998). Cognitive science still has not fully answered Fodor’s reasons for doubting that many cognitive phenomena associated with concepts reflect internal structural features of concepts as opposed to aspects of how we use and work with concepts; but to accept Fodor’s arguments is to discount Machery’s view as well, so let us assume here that we do want to assume internal structures to representations of concepts and that those structures help explain many psychological phenomena associated with concepts such as induction, categorization, and conceptual change. Fodor (1998) has characterized concepts as “the smallest units of thought” and, in this respect, many psychologists and philosophers agree. Does such a characterization compel us to Machery’s heterogeneity hypothesis, namely, that we must have several distinct concepts of water because, depending on context, we seem to use the concept in different ways? It is difficult to see why. Machery discounts the hybrid alternative by arguing that people will endorse conflicting statements about kinds such as tomatoes, whales, and the like, and those conflicts can only be explained by assuming that they are drawing on different concepts. These different uses are supposedly not “coordinated,” and therefore people cannot be referring to the same concepts. But this coordination problem does not seem to be so lethal for hybrid views. If a given concept has a hybrid structure consisting, for example, of typicality-based information, causal schema, functional relations, and logical entailments, it might well be the case that different contexts cause people to weight those properties very differently and respond in different manners across tasks. Ever since Lakoff’s (1972) demonstrations that different “hedges” such as “technically speaking” and “loosely speaking” can cause us to categorize kinds such as whales differently, it has been known that some hallmark ways we use concepts, such as categorization, can show strong variations as a function of situational and sentential concepts. But, if hybrid models are right, they seem more than adequate for dealing with such phenomena. Machery would need to provide a detailed model of internal hybrid representations of concepts that showed how they were intrinsically unable to computationally model such effects, and he has not yet done so. Machery suggests that the parts of hybrid concepts must be “coordinated” such that this cannot create inconsistencies, such as categorization judgments that whales both are and are not fish. This coordination property is seen as an essential part of hybrid models, and hybrid models are described as incoherent or empty without it. This was not an obvious conclusion. Consider, for example, contexts in which we might describe a person as “short” and then “tall.” If we see a 2-meter person practicing with Olympic gymnasts, we might well call him tall; but when observing him practice with an Olympic basketball team,

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we would call him short. We may well know his true height and the true heights of the other players, but the contexts call for different ways of assigning thresholds on the vertical dimension that we would then use to consider someone tall or short. Is this to be taken as evidence for multiple concepts of tall and short? Are there then an indefinitely large number of such concepts that are depending on all the micro-contexts that could shift the thresholds to tiny degrees? There is a strong tendency to resist such a route, and it seems that, for similar reasons, we should resist claims that hybrid structures are undermined by conflicting categorization judgments in different contexts. Categorization inconsistencies do not pose a problem if there are still systematic ways that categorization judgments can be shown to vary across contexts as a function of a description of their internal hybrid structure. Machery would be correct in pointing out that such systematic accounts are not yet fully worked out, but there are no obvious reasons why they might not be in the long run. Hybrid approaches also have other appeals. They can, for example provide continuity and coherence to models of conceptual change over time, as, for example, when the causal or rulebased aspects of a concept become more differentiated as a child grows older and come to be weighted more and more relative to the associative components (Keil 1989; Keil & Newman 2010). Machery’s heterogeneity alternative sees the child as progressing through a series of unconnected concepts that somehow magically tend to unfold in the same way across children. Hybrid models can also help explain how concepts differ across broad categories such as natural kinds and artifacts, where different components of the hybrid might be present to different degrees and accordingly assigned different weights, as well as being processed in different ways (Hampton et al. 2009). Machery has done us all a great service. He raises a host of interesting troubles for many accounts of concepts, and he is to be commended for trying to build a larger common ground of inquiry between philosophers and psychologists. His book is a refreshing new perspective that prods all of us to further develop our own theories of concepts.

The function and representation of concepts doi:10.1017/S0140525X10000397 Sangeet S. Khemlania and Geoffrey Goodwinb a

Department of Psychology, Princeton University, Princeton, NJ 08540; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104. [email protected] [email protected] http://www.princeton.edu/khemlani http://www.psych.upenn.edu/people/ggoodwin b

Abstract: Machery has usefully organized the vast heterogeneity in conceptual representation. However, we believe his argument is too narrow in tacitly assuming that concepts are comprised of only prototypes, exemplars, and theories, and also that its eliminative aspect is too strong. We examine two exceptions to Machery’s representational taxonomy before considering whether doing without concepts is a good idea.

In Doing without Concepts (Machery 2009; hereafter DwC), Machery proposes that heterogeneity in the mental representation of “concepts” is sufficient to render that term useless. As he argues, the term can refer to exemplars, prototypes, and theories. However, it can also refer to defaults (Connolly et al., Fodor et al. 2007), aspects (Prasada & Dillingham 2009), Boolean concepts (Feldman 2000; Goodwin & Johnson-Laird 2010; submitted; Shepard et al. 1961), and connections yet to be discovered. Thus, in our view, Machery’s taxonomy is too narrow, and it underestimates the degree of heterogeneity that

Commentary/Machery: Pre´cis of Doing without Concepts exists in the representation of concepts. It excludes a variety of conceptual phenomena that do not fall within the purview of prototypes, exemplars, and theories. We turn next to describe two examples of such conceptual phenomena – Boolean concepts, and connections expressed by generics. Boolean concepts – those that are composed out of negation (not), disjunction (or), and conjunction (and) – are an important kind of everyday concept. They occur frequently in the form of laws, rules, or procedures. Indeed, Machery’s criteria for individuating concepts are themselves Boolean concepts (see sect. 2 of Machery’s Pre´cis of DwC in this issue). A concept does not need to be entirely Boolean in order for it to contain relevant Boolean structure, however. How individuals learn Boolean concepts is still not resolved, but many of the current leading contenders are not based on exemplars, prototypes, or theories (Feldman 2006; Vigo 2009). We have recently proposed an alternative theory, based on mental models, which analyzes the complexity of concepts in terms of the number of distinct possibilities that a concept can be compressed into (Goodwin & Johnson-Laird, submitted). This theory predicts the acquisition of Boolean concepts as well as, if not better than, the other leading contenders, and it too is not based on the representational mechanisms that Machery assumes to be exhaustive in explaining conceptual knowledge. Recently, we published a paper documenting the occurrence of “conceptual illusions,” in which people think that particular instances of a Boolean concept are possible when in fact they are not, and vice versa (Goodwin & Johnson-Laird 2010). A typical example is the concept: green and large, or else green, in a context in which all possible objects are either green or not, and either large or small (the “or else” here represents exclusive disjunction). A large percentage of individuals think that an object that is both green and large is possible given this description, which is in fact erroneous. The exclusive disjunction between the two clauses means that the only possible object is one that is both green and small. This error, as well as others like it, is predicted by the mental model theory’s principle of conceptual truth. And as far as we can tell, accounts based on prototypes, exemplars, or theories have no way to explain these errors. Other examples of conceptual phenomena unaccounted for by the taxonomy described in DwC include the connections and relations that link concepts together. Such connections can be concepts unto themselves, and are revealed by generic assertions such as “tigers are striped,” “barns are red,” or “ticks carry Lyme disease,” which express generalizations about kinds of things (Carlson & Pelletier 1995; Gelman 2003; Lawler 1973). All three assertions are true for different reasons, and as such, generics provide a means for studying the types of connections we represent between concepts of kinds and properties. We have found that for statements such as “tigers are striped,” the relation between the kind (“tigers”) and the predicate (“are striped”) can be distinguished from logical, statistical, and causal connections (Khemlani et al., submitted; Prasada et al., submitted). These distinctions could account for phenomena in concept learning and conceptual development without importing any assumptions made by other theories of concepts. Thus, by studying generics, it is possible to discover the conceptual structure of generalizations without assuming the representational structure of the concepts to which they refer. Machery proposes that future research should examine differences between generics, and particularly how they differ from quantified assertions (DwC, p. 200), and we agree wholeheartedly. Unfortunately, the proposals in DwC do not leave room to explore such advances in conceptual organization, as they encourage researchers to couch their work as falling within the domain of three fundamental classes of conceptual representation. We do not think these phenomena, which point to even greater heterogeneity in the mental representation of concepts than Machery suggests, strengthen Machery’s eliminativist argument to do away with concepts. The elimination of the term

“concept” in favor of greater specificity tacitly endorses the assumption that prototypes, exemplars, and theories (and whatever else), are all that comprise concepts. It thus presupposes that contingent facts about the mental representation of concepts are the sole criterion for deciding whether “concept” ought to be preserved. But this presupposition ignores the common function that diverse sorts of concept play in representing knowledge and in communication. Concepts represent and convey systematic bodies of information, and they would retain this function regardless of how they are mentally represented. In other words, we think that the question of what counts as a concept needs answering at the computational level, not at the algorithmic one (cf. Marr 1982). Thus, Machery’s eliminativist argument is too powerful. It gives no grounds for thinking that the term “concept” is in an especially precarious position. In much the same way that the term organizes a wide array of representational processes, so too do terms like “thinking,” “attention,” and “memory.” Would Machery have us do away with these terms as well, given heterogeneity in the cognitive processes to which they refer? Perhaps, but we think this is going too far. In sum, the key functions of concepts are to represent and communicate knowledge, and this general functional property is what argues in favor of preserving the term “concept.” We believe that heterogeneity at the level of mental representation is no obstacle to the further empirical investigation of concepts.

Concepts are a functional kind doi:10.1017/S0140525X10000403 Elisabetta Lalumera Dipartimento di Psicologia, Universita` di Milano-Bicocca, 20126 Milano, Italy, and Cogito Research Centre in Philosophy, Universita` di Bologna, 40100 Bologna, Italy. [email protected] http://sites.google.com/site/elisabettalalumera/

Abstract: This commentary focuses on Machery’s eliminativist claim, that “concept” ought to be eliminated from the theoretical vocabulary of psychology because it fails to denote a natural kind. I argue for the more traditional view that concepts are a functional kind, which provides the simplest account of the empirical evidence discussed by Machery.

The novelty of Machery’s proposal in Doing without Concepts (Machery 2009) is the claim that the term “concept” ought to be eliminated from the theoretical vocabulary of psychology, because it fails to denote a natural kind. I will not dispute the claim that concepts are not a natural kind. There is a growing consensus among psychologists that the structure of concepts may vary along many dimensions, depending on expertise, domain of objects categorized, and conceptual task involved. Much of this evidence is reviewed by Machery himself, as well as in other recent studies in the philosophy of psychology (Piccinini & Scott 2006; Weiskopf 2009b). My point here is that, on philosophical grounds, this evidence is perfectly compatible with the much less revisionary claim that concepts are a functional kind. Something is a concept by virtue of the function it performs within a cognitive system, and something is the concept of a certain category C (at least partially) by virtue of the further specific function of representing it. It is a further question whether or not the functional kind “concept” is realized by natural kinds (Weiskopf 2009b). Functional kinds can be individuated and described independently of their realizers. This, however, does not deprive them of a central role in experimental psychology. 1. Concepts are a functional kind. It is disputable that a full characterization of the psychological usage of “concepts” is: BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts “those bodies of knowledge that are used by default in the processes underlying the higher cognitive competences” (Machery 2009, p. 7). Concepts are also normatively characterized as those bodies of knowledge that can perform a double function – namely, abstraction and projection of knowledge – and that are able to be recombined almost freely in order to form more complex concepts and thoughts. Let us focus on the first two functions, as the third one is arguably not specific to concepts only. Abstraction is the “bottom-up” process of extracting information from a single encounter with an object or propertyinstance, and generalizing such information to all encounters with that object or property. The experience of tasting rhubarb once and finding it bitter would be of no use if I could not store it as information about rhubarb independently of the specific episode of tasting it, by means of a general representation – a concept (Bloom 2000). Category induction is the complementary “top-down” process of projecting such knowledge to new encounters. When you tell me you like rhubarb pie, I form the expectation that you will also like other bitter-tasting foods. This is an application of my concept of rhubarb. Thus, the function of a concept is that of a “mental glue,” which connects one’s past experience with the present (Millikan 1998; Murphy 2002). It is because they perform this complex function that concepts are used by default in our higher cognitive capacities, and not vice versa. Psychologists are interested in discovering how such function is performed – by what structures and mechanisms. The functional properties of concepts provide constraints on what may count as an adequate concept-realizer. This is the first sense in which concepts as functional kinds are not dispensable in psychology. 2. Concepts as a functional kind are multiply realizable in a broad sense. Vehicles and food are familiar non-mental func-

tional kinds. Traditionally, the essence of a mental functional kind is to bring about some outcome, or to exercise some capacity (Kim 1992; Putnam 1967). With “broad sense” here, I mean that they are realizable by items characterized by different structural properties. Assuming that abstraction and category induction are the fundamental capacity characterizing the functional kind “concept,” there are plausibly many ways in which a human mind can organize itself in order to exercise them. Capacities may well be individuated by their ends, not just by their means (Millikan 1998). So, for example, a chemist’s capacity to abstract and project information about water may involve means that I do not possess. But so long as we are both able to abstract, accumulate, and project information about water, we share the same capacity, that is, we both possess a concept of water. The variability of structure of the functional kind “concept” is just as compatible with the empirical evidence discussed by Machery, as his own proposal is. It has the advantage of leaving it open whether prototypes, exemplars, and theories exhaust the possible concept-realizers or, more plausibly, not. 3. Concepts as kinds are multiply realizable in a narrow sense. The narrow sense is the traditional sense associated

with the phrase “multiple realizability” in philosophy of mind. It is the idea that a token (mental) functional kind can be realized by different physical substances. The classical example is a state of pain, multiply realized by silicon chips or C-fibers. To deny that concepts are multiply realizable in this narrow sense is very demanding. It requires a commitment to a strong form of physicalism. Contemporary neurophysiological research on concepts aims at individuating which areas of the human brain are involved in specifically conceptual tasks and explaining how this is done. It is not committed, however, to the further metaphysical claim that no other kind of matter, appropriately organized, could bring about the same capacities. If neurophysiology is not, surely psychology need not be committed to a robust reductionist agenda. That is, there is no reason why psychology should dispense with the functional kind “concept” qua multiply realizable. The neurophysiological evidence quoted by Machery on the variability of concepts is compatible with the anti-eliminativist functional view.

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4. The functional kind “concept” has a role in psychology. Machery claims that there are pragmatic reasons

for the elimination of “concept,” if intended as a natural kind term. This is not so if it is taken as a functional kind term. Consider the task of accounting for the fact that one’s general representation of a category – say, dogs – changes over time. Initially it is constituted by a bunch of exemplars, then it develops into a summary prototypical representation, and later it becomes a theory. In order to describe the three structures as stages of a diachronic process of change, one needs to make reference to what is common to them, namely, realizing the capacity of representing dogs generally, or being instances of the functional kind “concept of dogs.” This intrapersonal explanation properly belongs to the psychologist’s agenda, and so do interpersonal accounts of variability.

From conceptual representations to explanatory relations doi:10.1017/S0140525X10000415 Tania Lombrozo Department of Psychology, University of California, Berkeley, CA 94720. [email protected] http://cognition.berkeley.edu/

Abstract: Machery emphasizes the centrality of explanation for theorybased approaches to concepts. I endorse Machery’s emphasis on explanation and consider recent advances in psychology that point to the “heterogeneity” of explanation, with consequences for Machery’s heterogeneity hypothesis about concepts.

The “theory” approach to concepts, one of three that Machery endorses in Doing without Concepts (2009), reflects a widespread view in psychology that is typically taken to be promising but in need of further development (e.g., Carey 1985; Gopnik & Meltzoff 1997). According to this approach, concepts constitute or are embedded within intuitive theories about the world. However, both philosophers and psychologists have been quick to point out that the notion of an intuitive theory is underspecified, and that appeals to scientific theories run the risk of substituting one mystery for another (Laurence & Margolis 1999). Machery is aware of these concerns, but he identifies the locus of the theory view’s commitments not in the notion of an intuitive theory or in appeals to scientific theory, but in explanation. For an advocate of the theory approach, concepts store “knowledge that can explain the properties of the category members,” so “much hangs on the notion of explanation” (Machery 2009, p. 101). Machery is right to focus on the central role of explanation for theory-based approaches to concepts. In a set of influential papers that kick-started theory theorizing, theories were defined as “any of a host of mental explanations” (Murphy & Medin 1985, p. 290) and characterized in terms of “laws and other explanatory mechanisms” (Carey 1985, p. 201). Categorization and category learning were described as “special cases of inference to the best explanation” (Rips 1989, p. 53), with a concept invoked “when it has a sufficient explanatory relation to an object” (Murphy & Medin 1985, p. 295). But such appeals to explanation were not proposed against a backdrop of psychological theories of explanation. Rather, they predominantly appealed to what Machery calls a “folk understanding of explanation” (2009, p. 102), contributing to the concern that theory-based approaches to concepts are underspecified. What to do? One option would be to develop psychological theories of explanation, with our folk understanding as a guide. A second option would be to ground psychologists’ appeals to explanation in philosophical theories of scientific explanation. Given the analogy between science and cognition that motivates

Commentary/Machery: Pre´cis of Doing without Concepts many proponents of the theory approach, this seems like a natural move. But it is not the one Machery advocates. In fact, Machery cautions that “philosophical accounts of scientific explanation would probably be useless for spelling out the psychological notion of theory” (2009, p. 102). I want to suggest that Machery is wrong to dismiss the psychological value of theories of explanation from the philosophy of science and to neglect recent advances that move the psychology of explanation beyond “folk understanding.” Scientists are, after all, psychological creatures, and there is every reason to expect the aspects of human cognition that shape everyday explanations to play a role in science. Scientists and everyday cognizers also face similar problems and have similar goals: They confront limited data, and from this they must construct a representation of the world that supports relevant predictions and interventions. But there is another reason to expect a close correspondence between philosophical and psychological accounts of explanation, one that stems from the philosophical methods typically employed. Here, in uncharitable caricature, is how theory development often proceeds: Philosopher P1 proposes theory T1 of explanation; philosopher P2 quickly generates putative counterexample C, a specific case in which T1 makes one prediction about what is explanatory, but philosopher P2’s intuition demurs. The philosophical community pronounces one way or the other, based largely on shared intuitions about C, so T1 stands (for now) or gives way to a new theory. This is not the most efficient way to collect data, and it would not pass muster for an experimental psychologist; but to the extent philosophers are like everyday folk, one would expect convergence between philosophical theories and descriptively adequate accounts of everyday intuitions. In fact, a growing body of experimental work suggests that theories of explanation from philosophy can usefully inform the psychology of explanation and bear a close correspondence to everyday judgments (for reviews, see Keil 2006; Lombrozo 2006). While there is no consensus on a theory of explanation in philosophy, different strands of theorizing seem to capture different aspects of the psychology of explanation. For example, some studies on the role of explanation in category learning have drawn on subsumption and unification accounts of explanation (e.g., Williams & Lombrozo, in press), while others on categorization and inference are consistent with causal theories (e.g., Rehder 2003b; 2006). Empirical research on the cognitive significance and consequences of different kinds of explanations – specifically, functional versus mechanistic explanations (Kelemen 1999; Lombrozo 2009; under review; Lombrozo & Carey 2006; Lombrozo et al. 2007) – has its roots in Aristotle, but can trace a path to contemporary philosophers such as Daniel Dennett. One reason to appreciate this richer, philosophically informed psychology of explanation is because it has implications for Machery’s heterogeneity hypothesis. In particular, the two distinct summary representations that Machery recognizes – theories and prototypes – can be understood as embodying different kinds of (potentially) explanatory structure. Machery recognizes this point, and in fact rejects philosophical accounts of explanation, such as Salmon’s statistical relevance model, in part because allowing statistical relationships to play a role in explanation would “blur the distinction” between theories and prototypes (Machery 2009, p. 102). But perhaps the fact that explanations are sensitive to causal and statistical relationships is a reason to endorse such accounts. Evidence suggests that explanations are sensitive to multiple kinds of knowledge – about causal structure and functional relationships (Lombrozo & Carey 2006), about statistical regularities (Hilton & Slugoski 1986), and about principled generic knowledge (Prasada & Dillingham 2006). These are precisely the kinds of knowledge that Machery suggests concepts contain. Recognizing the “heterogeneity” of explanatory structure does not eliminate the heterogeneity of concepts, but it does suggest a path to unifying concepts by appeal to explanation. It also pushes

back Machery’s concerns about natural kinds and elimination from concepts to explanation: What are the distinct kinds of explanatory relations, and do they as a class support relevant generalizations that suggest “explanation” is a natural kind and a valuable theoretical term for a mature psychology? Perhaps these are the questions we should be asking.

Concepts and theoretical unification1 doi:10.1017/S0140525X10000427 Eric Margolisa and Stephen Laurenceb a

Department of Philosophy, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada; bDepartment of Philosophy, University of Sheffield, Sheffield S3 7QB, United Kingdom. [email protected] [email protected] http://web.mac.com/ericmargolis/primary_site/home.html http://www.shef.ac.uk/philosophy/staff/profiles/slaurence.html

Abstract: Concepts are mental symbols that have semantic structure and processing structure. This approach (1) allows for different disciplines to converge on a common subject matter; (2) it promotes theoretical unification; and (3) it accommodates the varied processes that preoccupy Machery. It also avoids problems that go with his eliminativism, including the explanation of how fundamentally different types of concepts can be co-referential.

In Doing without Concepts, Machery (2009) claims that philosophers and psychologists are not talking about the same thing when they use the term concept, and that this is a consequence of their having differing explanatory interests. But there are reasons to reject Machery’s division between philosophical and psychological subject matters regarding concepts. First, we should recognize the significant influence that philosophical and psychological theorizing have had on each other. For instance, prototype theorists have been inspired by philosophical critiques of definitions, theory-theorists have drawn upon philosophical accounts of natural kind terms, and developmental psychologists have prioritized addressing the philosophical challenge of explaining how learning enriches a conceptual system. Likewise, philosophers have been deeply influenced by psychological work on typicality effects, essentialist thinking, and conceptual change in childhood (to name just a few examples). Second, even where philosophers and psychologists do have differing explanatory agendas, the same can be said of just about any two fields in cognitive science and in science generally. Linguists and psychologists have differing explanatory aims too, as do cognitive psychologists and neuro-psychologists – not unlike biologists and chemists. This hardly shows that theorists in these fields aren’t talking about the same thing (e.g., NPs, conditioning, or DNA). Third, there is a payoff to identifying a single subject matter that underlies the efforts in philosophy, psychology, and other areas of cognitive science. The result is greater theoretical unification – a prized explanatory virtue. Now concept is a term of art. But we would suggest that Machery gets off on the wrong foot by characterizing concepts as “bodies of information.” Instead, concepts should be taken to be mental symbols that have semantic structure (which fixes the propositional content of thoughts via a compositional semantics) and processing structure (which explains how concepts figure in various mental processes). Rather than saying that prototypes, exemplars, and theories constitute fundamentally different types of concepts, it is better to locate such bodies of information in a concept’s processing structure. On this approach, the concept DOG is akin to a word in a sentence, and its structure includes a prototype, a theory, and so forth (Laurence & Margolis 1999). The principal advantage to viewing concepts in this way is that it makes sense of how philosophers and psychologists can be talking about the same thing, BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts while illuminating the fertile cross-disciplinary interactions that the study of concepts enjoys. And though we (two philosophers) are promoting the idea that concepts are mental symbols, this is not an exclusively philosophical viewpoint. Versions of it are endorsed by many cognitive scientists (e.g., Carey 2009; Jackendoff 2002; Pinker 1997; Pylyshyn 2007; Sperber & Wilson 1995). Is our account of concepts a hybrid theory? Yes and no. It does bring together prototypes, exemplars, and theories by saying that they are bound to the same mental symbols. The concept DOG, for example, sometimes activates a prototype, sometimes exemplars, and sometimes a theory. But a concept need not have each type of processing structure, and the activation of one part does not require activating other parts. Machery argues that the heterogeneity hypothesis has the explanatory advantage of accounting for the diverse psychological processes that are associated with higher cognitive capacities. But a theory that unites diverse processing structure through links to a common mental symbol can handle this diversity just as well. Machery asks why theorists who reject the heterogeneity hypothesis do not concede that the various bodies of information (the prototype, theory, etc.) amount to distinct yet co-referential concepts of fundamentally different types – his own view (2009, p. 64). But what justifies Machery’s claim that, on his account, a dog prototype, a dog exemplar, and a dog theory are co-referential? To the extent that these representational structures have referents, the referents are hardly likely to be the same. For example, a dog-prototype would refer to things that are similar to the central tendency that the prototype describes, while a dog-theory would cover things that are at odds with the central tendency (e.g., the offspring of two dogs that doesn’t look anything like typical dogs). By contrast, on our account, the issue of concept identity is easily handled in terms of the type identity of the mental symbol that unifies these various knowledge structures. This symbol’s identity is a matter of what it refers to, plus features of the representation’s vehicle for distinguishing among co-referential concepts with differing cognitive significance (Margolis & Laurence 2007). As realists about concepts, we also do not find Machery’s case for eliminativism compelling. For one thing, we would argue that concepts as we understand them do constitute a natural kind by Machery’s criteria. But also, Machery’s standard for the reality of psychological kinds is too high. If his standard were enforced – if a kind has to play an important role in many scientific generalization beyond those that characterize it – we’d have to give up on many core psychological constructs, such as module, computation, and representation. But while these high-level kinds may not satisfy Machery’s criteria for realism, they play a key role in describing the mind’s operations and helping scientists to empirically investigate its overall organization. Moreover, Machery’s standard probably cannot even be maintained for his fundamentally different types of concepts; for example, numerous distinct types of structures tend to get lumped together under the heading of a theory. And though we lack the space to press the point here, Machery’s approach to elimination would also have dire consequences outside of psychology. Arguably, we would have to give up most high-level kinds, including cell, vertebrate, and chemical element. In sum, a realist account of concepts as mental symbols with both semantic and processing structure addresses the explanatory concerns that Machery raises while avoiding the problems connected to his eliminativism. Taking psychological and philosophical theories of concepts to be about a single subject matter allows for far greater theoretical unification, placing concepts at the center of a broad investigation into the nature of cognitive processes, cognitive development, meaning, justification, and the mind’s relation to the world. ACKNOWLEDGMENT Eric Margolis would like to thank Canada’s Social Sciences and Humanities Research Council for supporting this research.

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1. This article was fully collaborative; the order of the authors’ names is arbitrary.

Where are nature’s joints? Finding the mechanisms underlying categorization doi:10.1017/S0140525X10000439 Arthur B. Markman Department of Psychology, University of Texas, Austin, TX 78712. [email protected] http://www.psy.utexas.edu/psy/FACULTY/Markman/index.html

Abstract: Machery argues that concepts are too heterogeneous to be a natural kind. I argue that the book does not go far enough. Theories of concepts assume that the task of categorizing warrants a unique set of cognitive constructs. Instead, cognitive science must look across tasks to find a fundamental set of cognitive mechanisms.

There is a persistent worry that cognitive scientists may not be carving nature at its joints. This fear underlies debates over whether computational representations or dynamical systems best explain cognitive processing (e.g., Markman & Dietrich 2000; Spivey 2007). It lies at the heart of critiques of the use of brain imaging to understand cognitive function (Uttal 2001). This issue is also central to the target book, Doing without Concepts (Machery 2009). This question is important, because cognitive scientists typically organize theories around tasks. Memory is explored by having people study items and then probing their memory for those items at some later time. Decision-making research involves presenting people with a set of options and having them select one. As Machery points out, categorization research often involves specific tasks such as classification and category-based induction. Theories then aim to explain performance in these tasks. Machery takes the structure of the cognitive science literature on concepts as a given and then suggests that the notion of a concept is misleading. On his view, there are (at least) three distinct types of concepts: prototypes, exemplars, and theories. Using a single term – concepts – to refer to all of these is dangerous, because it fails to carve nature at its joints. I suggest that the problem is even worse than Machery makes it out to be. Fundamentally, the set of tasks that we study involves a series of cross-cutting cognitive mechanisms. At present, cognitive science assumes that tasks like classification and categorybased induction require an explanation that involves some set of representations and processes that are shared (to some degree) across different kinds of categorization tasks, but are relatively distinct from the kinds of representations and processes that are involved in decision-making, memory, or attention. Ultimately, we need to reorient our theories to find the commonalities across tasks that are typically thought of as different. In the study of concepts, there are already some hints in the literature that this reorientation is starting to take place. The most prominent shift in research on categorization comes from work relating categorization to memory. For example, the research by Ashby, Maddox, and colleagues draws parallels between behavioral and neurobiological research on categorization and memory (Ashby et al. 1998; Maddox & Ashby 2004). This work incorporates research from neural systems involved in memory to make predictions for performance in category learning experiments. Research on the kinds of categories that amnesics can learn is also inspired by the desire to create parallels between memory and categorization (Knowlton et al. 1994). Research on the influences of learning tasks on category learning also forms parallels between categorization and memory

Commentary/Machery: Pre´cis of Doing without Concepts (Markman & Ross 2003). A growing body of research suggests that the tasks people perform while learning categories influences what people learn about those categories. For example, Yamauchi and Markman (Yamauchi & Markman 1998; Yamauchi et al. 2002) found that people tend to learn about features that distinguish one category from another when learning by classifying examplars. In contrast, people tend to acquire category prototypes when learning by making predictive inferences about new category members that are missing some features (see also Chin-Parker & Ross 2002). Markman and Ross (2003) drew on the memory literature and argued that this type of category acquisition involves transfer appropriate processing, which is also observed in memory (e.g., Morris et al. 1977). The relationship between categorization and memory is also being driven forward by work on the influences of communication on category acquisition. Garrod and colleagues (Garrod & Anderson 1987; Garrod & Doherty 1994) found that people tend to arrive at a common method for referring to information in the environment over the course of conversing with others about those objects. Markman and Makin (1998) observed that the similarity of categories across people is made more uniform by communicating with others. The act of establishing a common reference influences memory by ensuring that category labels refer to a common set of properties. These communicative factors have a significant influence on the ability to extract information about categories from memory. For example, Malt et al. (2003) found that it is hard to predict the labels people give to a common set of objects (such as jars, bottles, boxes, and containers) based solely on similarities among the items. Furthermore, there is no clear relationship between the labels given to objects in different languages. Instead, the labels given to objects reflect communicative history of those objects in the language. The labels people learn for objects, then, are determined in part by the utility of those labels for communication. We use a particular word for an object, because we know that others will know what we are talking about when we use that label. I am not suggesting that we abandon theoretical devices like prototypes, exemplars, and theories as constructs for helping us to understand categorization behavior. Instead, the theoretical basis of research on categorization needs to be modified. We must recognize that the tasks that we use to study psychology in the lab do not cut nature at its joints. Instead, we must look for the cross-cutting psychological mechanisms that are involved in a variety of different tasks. That means that theoretical constructs from memory, communication, attention, and motivation are all relevant to the study of category acquisition. In addition, the valuable constructs from research on categorization must be exported to the study of other cognitive processes where they may be valuable. Undoubtedly, prototypes, exemplars, and theories have value in understanding a variety of aspects of cognitive processing beyond categorization. For example, exemplars play an increasingly important role in theories of automaticity (Logan 1988; 2002). Ultimately, we must transition from a cognitive science in which psychological tasks organize our understanding of psychological mechanisms to one based on an understanding of the way a core set of psychological mechanisms permit us to perform those tasks.

Concepts versus conceptions (again)1 doi:10.1017/S0140525X10000440 Georges Rey Department of Philosophy, University of Maryland, College Park, MD 20742. [email protected] http://sites.google.com/site/georgesrey

Abstract: Machery neglects the crucial role of concepts in psychological explanation, as well as the efforts of numerous “externalists” of the last 40 years to provide an account of that role. He rightly calls attention to the wide variation in people’s epistemic relations to concepts – people’s conceptions of things – but fails to appreciate how externalist and kindred proposals offer the needed stability in concepts themselves that underlies that variation.

In proposing to dispense with concepts in Doing without Concepts, Machery (2009) neglects to notice how psychological explanation presupposes them. How could we begin even to describe, for example, the Mu¨ller-Lyer illusion unless people share a concept of longer than; or the gambler’s fallacy, without them sharing more likely? Concepts seem to be natural kinds at least to the extent that they are the entities over which psychology generalizes. But what is a concept that can play this role? Many recent philosophers (other than the atypical Peacocke [1992]) have offered proposals that Machery amazingly ignores, such as the various forms of “externalist” views that have been proposed by, for example, Kripke (1972/1980), Putnam (1975), Dretske (1981), Devitt (1981; 1996), Millikan (1984), Burge (1986), and Fodor (1990; 1998), according to which the identity conditions for a concept are provided in part by historical and counterfactual relations the thinker bears to phenomena in the external world. At most, Machery considers some of the “intuitions” that motivate externalism, only to dismiss them as too cross-culturally variable and unreliable.2 Machery correctly notes that a problem with many traditional philosophical accounts like Peacocke’s is that they fall afoul of Quine’s attack on the analytic/synthetic distinction (Machery 2009, p. 39). But he misses how that attack threatens any purely epistemic proposal, including his own appeal to “bodies of knowledge” (p. 12). This is puzzling, given Fodor’s extensive discussion of many of these issues in at least five of his recent books, a discussion quite often directed explicitly at the psychological theories Machery considers. The problem is that “bodies of knowledge” vary between people and stages in a person’s life. Unless one restricts the relevant knowledge in some principled way, no two people (or stages) will share a concept, since, short of coincidence, no two people (or stages) will bring exactly the same knowledge or procedures to bear in making many of their judgments.3 Call this the problem of epistemic variability. Rather than inviting us to abandon the notion of concept, perhaps this variability is simply a reason to abandon an epistemic conception of it. Machery would probably reply that I’m merely pressing here the philosophical notion of concept, which is concerned with the individuation of propositional attitudes (2009, pp. 32 – 33), a topic that, surprisingly, he claims is not the concern of psychological theory (p. 34). Indeed, he claims that when the two tasks are properly distinguished, “most philosophical attacks against the psychological theories of concepts are decisively undermined” (p. 51). “Decisively”? There are a number of important reasons to think not: In the first place, as Machery himself notes (2009, pp. 35 – 37), many psychologists themselves are hardly clear about the difference between the two concerns, often presenting their work as refuting the Classical View of traditional philosophy and presupposing some philosophical, usually verificationist alternative (see Rey 1983). Secondly, concept identification would seem to be an issue not about how people do think under pressure, but how they could think if they were to reflect – what they could understand – and, pace Machery (p. 34), this seems as apt a topic as any for psychological research. It may well be that peoples’ prototype of a doctor is of a man in a white coat; but if they found the thought of young woman doctor in a dark one as unintelligible as a round square, that would be a reason to think they didn’t have the concept doctor. It’s because people have a concept doctor that transcends their prototypes that it’s worth reasoning with them, that is, modifying their epistemic position by citing BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts evidence or argument, but keeping the concept the same. As I emphasized in Rey (1985), we need to distinguish the concept of something from merely the (epistemic) conceptions of it that have been too much the focus of the psychological research Machery reviews. Concepts are what remain stable across variability in conceptions, and so give argument a point, framing the questions of what people could learn and what might be the limits of reason and thought. Lastly, as Machery notes, there is a need of a “coherent framework” (2009, p. 247) for bringing together the psychologists’ different proposals. Something like the externalist strategies may be just the sort of thing for the purpose. They at least address the problem of epistemic variability in a promising way, proposing that concepts are constituted by what is “explanatorily basic.”4 A crucial feature of this strategy is that it makes no commitment to the character of the representations people use in ordinary circumstances requiring rapid reasoning, or even in “acquiring” the concept (thereby also allowing for many concepts to be innate). Nothing I have said here is meant to suggest that any externalist (or other) proposals are yet satisfactory (see Segal [2000] and Rey [2009a] for serious qualms). It’s not that Machery should have endorsed externalist or related strategies; my point is simply that he should have discussed them, particularly before giving up on the concept of concept entirely.

What is a natural kind? . Though the notion of a natural kind has been characterized many times over, philosophers of science have, in recent years, reached a consensus – or as close to consensus as philosophers ever get – according to which natural kinds are Homeostatic Property Clusters (HPC; Boyd 1991; Machery 2005). According to the HPC account, a kind K is natural if:

NOTES

Machery endorses the HPC account, yet he denies that concepts constitute a natural kind We maintain, however, that if his claims about the cognitive scientist’s notion of a concept are correct, then concepts are plausibly a natural kind in the HPC sense. First, consider some of the characteristics that, according to Machery, concepts possess:

This conception of natural kinds is intended to capture the central features of those kinds paradigmatically studied by science. Most importantly, the fact that the existence of property-clusters depends on the operation of an underlying causal essence helps explain why such kinds are typically subsumed by many nonaccidental, empirical generalizations (Griffiths 1997; Machery 2005). Why suppose that concepts constitute a natural kind?

1. This commentary summarizes my longer review of Machery’s book available at: http://ndpr.nd.edu, http://ndpr.nd.edu/ review.cfm?id¼16608, which readers should consult for more detail. It and other relevant material of mine are also available at: http://sites.google.com/site/georgesrey. 2. See Devitt (forthcoming) for reasons this dismissal is rash. Note that Machery fails to notice that, for example, Burge’s (1986) arguments for externalism don’t rest on ordinary intuitions alone, but on explanatory features of a Marrian theory of vision. 3. Of course, there may be similarities and overlap in many people’s knowledge and procedures (at least relative to the stability of their other concepts); but mere similarity and overlap aren’t identity, and it is identity in concepts that is needed to sustain serious explanations, such as ones about cognitive development, vision, or language; cf. Fodor (1998, pp. 30ff). 4. Devitt (1996) and Horwich (1998a, p. 41) propose that treating the meaning constitutive conditions as the ones on which all other uses of a symbol explanatorily depend, can be seen as a mixed internalist/externalist variant of Fodor’s (1990) “asymmetric dependency” that achieves some of its same effects, but without its strong externalist commitments; see Rey (2009a) for discussion.

Why don’t concepts constitute a natural kind? doi:10.1017/S0140525X10000452 Richard Samuels and Michael Ferreira Department of Philosophy, The Ohio State University, Columbus, OH 43210-1365. [email protected] [email protected]

Abstract: Machery argues that concepts do not constitute a natural kind. We argue that this is a mistake. When appropriately construed, his discussion in fact bolsters the claim that concepts are a natural kind. Introduction. A central claim of Machery’s Pre´cis (target article) – and of his book, Doing without Concepts (Machery 2009) – is that concepts do not constitute a natural kind. Until reading his work on the topic, we would have been inclined to agree. But he has changed our minds. Machery’s discussion, when appropriately construed, provides grounds to suppose that concepts do constitute a natural kind.

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H1. It is associated with a contingent property cluster: a range of characteristics or symptoms which tend to be co-instantiated by instances of the kind, but need not be genuine necessary conditions for membership. H2. There is some set of empirically discoverable causal mechanisms or structures – a causal essence – that explains the covariation of these symptoms. H3. To the extent that there is any real definition of what it is for something to be a member of the kind, it is not the symptoms but the presence of the causal essence producing the symptoms that are definitive of kind membership (Boyd 1989).

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1. Concepts consist in bodies of information. 2. Concepts are stored in long-term memory, and persist even when not actively deployed. 3. Concepts are non-proprietary. 4. Concepts are default representations automatically retrieved from long-term memory. 5. Concepts are subject to temporal and inter-subjective variation. 6. Concepts are internally connected. 7. Concepts are internally coherent. No doubt other generalizations hold of concepts as such. But even the characteristics 1 – 7 just listed provide grounds to suppose that concepts are a natural kind. If true, they suggest that concepts satisfy the main conditions imposed by the HPC account. Specifically: C1: Concepts exhibit a reliably covarying property cluster, including internal coherence, connectedness, persistence, and so on. C2: The existence of the property cluster depends largely on the operation of an abstractly, functionally characterizable kind of process. Roughly: Most of the properties depend on facts about long-term memory, the operations that it performs, and its relations to other higher cognitive processes. C3: To the extent that inclusion in the kind, concept, can be defined, it should be the condition of being subject (in the right kind of way) to the relevant causal mechanism – i.e., long-term memory – that defines kind membership. The condition that Machery labels “Default” partially captures this idea. In short, Machery’s comments, appropriately construed, provide reason to suppose that concepts are a natural kind in the HPC sense. Why would one think otherwise? Clearly, Machery is not inclined to accept this conclusion Why not? Here are some brief comments on the most plausible arguments that we could extract from his discussion. Argument 1: Different kinds – for example, prototypes and theories – satisfy the conditions on concepthood. So, while

Commentary/Machery: Pre´cis of Doing without Concepts prototypes and theories might be natural kinds, concepts as such are not. Response: There is nothing wrong with superordinate natural kinds. For example, metal is plausibly a natural kind, even though it contains subordinate kinds such as gold and lead. For all the present argument shows, the same is true of concepts. Argument 2: Characteristics 1– 7 are intended to explicate the cognitive scientist’s notion of a concept, not to characterize the kind as such. If so, treating them as parts of the kind-syndrome is tantamount to confusing issues about the semantics of the word “concept” with issues about the nature of the kind. Response: Even if 1– 7 are parts of the cognitive scientist’s notion of a concept, there is no inconsistency between this claim and the claim that they are also parts of the property cluster exhibited by members of the kind. Argument 3: Members of a natural kind are supposed to share many covarying properties and, hence, be subsumed by many empirical generalizations. But while concepts share some properties, and are subsumed by some empirical generalizations, there are too few to merit natural kind status. Response: How many properties must members of a kind share in order for the kind to be natural? This is a silly question because there is no reason to suppose any precise cut-off point. Presumably some kinds manifest more common properties than concepts do, and, in that regard, are better examples of natural kindhood than concepts. But this is a very unsurprising conclusion. (Who would have supposed otherwise?) Moreover, it surely does not follow from this that concepts are not a natural kind simpliciter. On the contrary, there are two obvious considerations that explain the relative lack of properties and generalizations associated with concepts without impugning their natural kind status: 1. Concept is a psychological kind; and psychological kinds quite generally appear to underwrite fewer empirically rich generalizations than the kinds cited by many other disciplines. (Exercise: Compare chemistry or molecular biology with psychology.) 2. Concept is a superordinate kind. So, in point of logic, the kinds it subordinates –for example, prototypes and exemplars – will have the characteristics of concepts as such, plus their own specific characteristics. Still, it would be wrong to suppose that only subordinate kinds are natural, while kinds like concept (or metal, or alkali, etc.) are not. Conclusion. Concepts plausibly constitute a natural kind, in the HPC sense. So, pace Machery, we do not advocate their elimination from cognitive science. Instead, we think that Machery’s discussion supports a rather more banal conclusion, namely: Concepts are, in some regards, a less good example of natural kindhood than many other kinds studied by science. But this is very unsurprising, and is largely explained by two facts: Psychology appears to generate fewer robust empirical generalizations than many other sciences; and in point of logic, superordinate kinds manifest fewer regularities than the kinds they subordinate.

Evidence of coordination as a cure for concept eliminativism doi:10.1017/S0140525X10000464 Andrea Scarantino Department of Philosophy, Georgia State University, Atlanta, GA 30302. [email protected]

Abstract: I argue that Machery stacks the deck against hybrid theories of concepts by relying on an unduly restrictive understanding of coordination between concept parts. Once a less restrictive notion of coordination is introduced, the empirical case for hybrid theories of concepts becomes stronger, and the appeal of concept eliminativism weaker.

In Doing without Concepts, Machery (2009) makes a persuasive case that there is no unique body of information that plays the concept-role. An important methodological consequence follows: Psychologists of concepts should stop asking whether the realizer of the concept-role is a prototype, an exemplar, or a theory. None of these bodies of information alone can explain all phenomena pertaining to higher cognitive competences. What I reject is the additional thesis that the term “concept” should be eliminated from the vocabulary of psychology because it does not designate a natural kind, roughly a maximal set of entities about which many scientifically interesting generalizations can be formulated. Machery is inspired by Griffiths’ (1997) proposal that we split emotion theory into the study of several heterogeneous kinds of emotions, such as affect programs (e.g., fear of a suddenly looming object), higher cognitive emotions (e.g., guilt about having missed a friend’s birthday) and socially sustained pretenses (e.g., going postal after having been fired). Mere evidence of heterogeneity, however, isn’t a sufficient reason for eliminating a kind (Piccinini & Scott 2006). If a set of heterogeneous entities is part of a higher-level structure about which scientifically interesting generalizations can be issued, the heterogeneity of the parts is compatible with the existence of a higher-level natural kind to which they jointly belong. We do not think that “elephant” does not designate a natural kind just because elephants have heterogeneous parts: these parts are integrated in a way that allows biologists to formulate many scientifically interesting generalizations about elephants. Now, in the case of emotions, it is quite clear not only that affect programs, higher cognitive emotions, and socially sustained pretenses are heterogeneous, but also that there is no overarching higher-level entity of which they are parts. Several distinct causal mechanisms are responsible for the occurrence and unfolding of emotion episodes of different kinds, and this prevents the emergence of a unified scientific psychology of emotions. In the case of concepts, the situation is considerably murkier, because we do have at least preliminary evidence that co-referential prototypes, exemplars, and theories are integrated parts of a larger whole (the Hybrid Hypothesis). To get clear on this topic, we must determine when bodies of information are parts of a larger whole. Machery presents two individually necessary and jointly sufficient conditions: Connection and Coordination (Machery 2009, p. 64). Connection between bodies of information requires that the retrieval and use of one body of information in a given cognitive process facilitates the retrieval and use of the remaining bodies of information in other cognitive processes. Coordination between bodies of information requires that they never produce inconsistent outcomes, for example, inconsistent categorization judgments. Armed with this understanding of the part – whole relation, Machery proceeds to present evidence against Coordination. Language users, he argues, can judge that some liquid is water because it fits the water prototype (water is transparent, drinkable, found in lakes, etc.), but at the same time that it is not water because it does not fit the water definition (water is H2O). Since neither judgment is allegedly taken by the folk to be authoritative over the other, Machery concludes that the Coordination condition is violated, and that this counts as a strike against the Hybrid Hypothesis. My main problem with this line of reasoning is that the Coordination condition is inadequate. In general terms, the parts of a given concept are coordinated just in case they work together, in ways to be empirically discovered, in at least some of the processes underlying higher cognitive competences. Machery’s coordination condition offers a very narrow interpretation of how such bodies of information are supposed to work together, namely, by avoiding conflict in all circumstances in which they are jointly activated. But the fact that this very specific principle of organization is not empirically supported BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts does not constitute evidence that bodies of information are not coordinated in some other, theoretically interesting way. First, there are forms of coordination that have nothing to do with joint activation. For instance, bodies of information can be acquired, rather than deployed, in a coordinated fashion. Finding out how they are acquired demands unveiling what we may call generalizations of coordinated acquisition. For example, some have argued that the statistical information contained in prototypes and the causal, functional, and nomological information contained in theories are acquired in part through a process of abstraction from exemplars (Heit 1994b). This would represent an important aspect of integration between bodies of information. A further aspect of integration is that the specific features that are abstracted in a prototype appear to be heavily influenced by the subject’s background theory (Wisniewski & Medin 1994). Some consider the integration between statistical and theoretical bodies of information to be so tight that they have proposed representing prototypes not as simple feature lists, but rather as schemata which make explicit the theory-based relations between statistically common features (Cohen & Murphy 1984). Second, there are forms of integrated activation that do not aim to avoid conflict under all circumstances. Finding out about such alternative forms of coordination demands unveiling what we may call generalizations of coordinated deployment. Some have argued, for instance, that whether theories are activated or prototypes are activated in a categorization task depends on the perceptual richness of the input: perceptually rich inputs activate prototypes, and perceptually poor inputs activate theories (Murphy 2002, p. 168). This would be a way in which different bodies of information can work together towards a cognitive end by being differentially, as opposed to jointly, activated. There is also evidence that in some circumstances in which jointly activated bodies of information lead to conflict, the conflict is resolved according to a general trumping principle. For instance, Keil (1989) has argued that conflicts between prototype-based and theory-based categorizations of biological categories tend to be systematically resolved in favor of the theory-based categorization (e.g., a raccoon that is made to look exactly like a skunk while preserving its internal properties is judged to be a raccoon even it if fits perfectly the skunk prototype). This particular form of coordination is lost, on the other hand, when biological categories are substituted with artifact categories. My central point is that if enough empirical generalizations of coordination can be unveiled, both of the acquisition and of the deployment variety, a case can be made that “concept,” pace Machery, designates a higher-level natural kind for the purposes of scientific psychology. Notice that this strategy for preserving the scientific integrity of the notion of concept differs from the strategy of unveiling empirical generalizations that are insensitive to the differences between prototypes, exemplars, and theories (e.g., Weiskopf 2009b). Generalizations of coordination are eminently sensitive to the differences between prototypes, exemplars, and theories, but they can provide evidence that they are components of an integrated higher-level entity. ACKNOWLEDGMENT Thanks to Gualtiero Piccinini for helpful comments on a previous draft.

Conceptual atomism rethought doi:10.1017/S0140525X10000579 Susan Schneider Department of Philosophy, Center for Cognitive Neuroscience, Institute for Research in Cognitive Science, University of Pennsylvania, Philadelphia, PA 19104-6304. [email protected]

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Abstract: Focusing on Machery’s claim that concepts play entirely different roles in philosophy and psychology, I explain how one wellknown philosophical theory of concepts, Conceptual Atomism (CA), when properly understood, takes into account both kinds of roles.

In Doing without Concepts, Machery (2009) argues that concepts should be eliminated from psychology. He further claims that psychological and philosophical theories of concepts talk past each other: “when philosophers and psychologists develop theories of concepts, they are really theorizing about different things” (p. 4). I am not convinced that concepts should be eliminated from psychological theorizing, but today, I focus on Machery’s claim that concepts play entirely different roles in philosophy and psychology: I shall aim for a partial reconciliation in the context of one well-known philosophical theory of concepts, Conceptual Atomism (CA), a view pioneered by Jerry Fodor (see Fodor 1998; Laurence & Margolis 2002). According to Machery (2009), philosophical theories are mainly interested in concepts as they figure as constituents in propositional attitudes and hence find the matter of reference determination to be of key import. Psychological theories, in contrast, focus on topics such as categorization, analogical reasoning, and induction (p. 34). As an example of how divorced philosophical and psychological interests are, Machery raises CA: There is little point in blaming some philosophical theories of concepts, such as Fodor’s theory, for being unable to explain how we reason, how we categorize, how we draw analogies, or how we induce (as does, e.g., Prinz 2002). For, simply, a philosophical theory of concepts is not in the business of providing such explanations. (p. 37)

CA claims that the nature of a concept is determined (or, as philosophers say, “individuated”), at least in part, by the information that the symbol carries about the world. It further holds that lexical concepts are primitive, being semantically unstructured: that is, they are not comprised of further concepts (Fodor 1998; Margolis & Laurence 1999, p. 62; Prinz 2002, p. 89). Many would agree with Machery’s claim that CA ignores the role that concepts play in thought (i.e., categorization, induction, etc.). Still, Machery’s conclusion is premature. Surprisingly, CA has a neglected resource for capturing the role the concept plays in thought; this is because, as we’ll see, according to CA, a concept is defined by its symbol type. For, consider that CA defines primitive concepts in the following manner: Existence condition: A primitive concept exists if and only if a primitive symbol in the language of thought (LOT) has a broad content Identity condition: Primitive concepts are identical if and only if they are of the same symbol type and have the same broad content (Fodor 1998, p. 37). (Where the broad content of a symbol is, roughly, what the symbol refers to.) The reader may be surprised that I construe CA as saying that symbols individuate concepts. But observe that Fodor himself acknowledges that broad content alone is inadequate for the purpose of individuating primitive concepts because it fails to distinguish co-referring concepts (e.g., groundhog/woodchuck; Cicero/Tully) (Fodor 1998; 2008). He explains that it is for this reason that he distinguishes concepts in terms of their mode of presentation (MOP) types, as well as their broad contents (Fodor 1998, Chs. 1 and 2; 2008, Ch. 3, especially p. 70). And, as philosophers know, Fodor regards MOPs as being symbols. Hence, even working within Fodor’s original framework, CA has the resources to individuate concepts along two dimensions: a symbolic dimension and a referential one. Now let us see how CA’s symbolic element captures the role the concept plays in thought. I have argued that symbols are defined by the role they play in computation (Schneider 2009). Although Fodor, ironically, challenges aspects of my view, notice that even Fodor himself writes that MOPs (i.e., symbols)

Commentary/Machery: Pre´cis of Doing without Concepts are individuated by their role in mental processing: “If MOPs are both in the head and functionally individuated, then a MOP’s identity can be constituted by what happens when you entertain it” (Fodor 1998, p. 20; see also Fodor 2008, p. 92). Piecing these observations together, we are now ready for the payoff. When CA is properly understood, both psychological and philosophical interests are brought together into a singular package: A lexical concept is a semantically unstructured “atom” that is individuated by both its meaning (in particular, its broad content) and its symbol type, where the symbol, in turn, is individuated by the computational role that it plays in one’s cognitive economy (including, importantly, its role in mental processes such as categorization, induction, and analogical reasoning) (Schneider 2009; forthcoming). So CA can say that the features of concepts that psychologists are traditionally interested in are built into concepts’ very natures. For example, consider the prototype view. In the eyes of CA the experimental results in the literature on prototypes are indications of features of certain symbols’ underlying computational roles, and these roles determine the relevant concept’s natures. CA is ecumenical. For now consider the competing theorytheory, which suggests that concepts are mini-theories of the categories that they represent, encompassing our beliefs about hidden features, underlying causal mechanisms, and ontological divisions. Advocates of the theory-theory suggest that it captures explanatory relations between features while the prototype theory does not. For instance, in a well-known criticism of the prototype view, children appear to use beliefs about a creature or thing’s underlying essence to override categorization judgments based on superficial, sensory features (Keil 1989). In the eyes of the conceptual atomist, this debate provides insights regarding concepts’ underlying computational roles. But no matter how the debate plays out, concepts’ natures are nevertheless determined by their broad contents and symbol types. Indeed, perhaps some concepts have computational roles that are explained by the prototype view, while others have roles that are illuminated by the theories view. So the conceptual atomist who pays attention to the neglected, symbolic element of concepts can offer a more comprehensive theory of concepts than is normally supposed. CA is ecumenical enough to incorporate different sorts of concepts (prototypes, theories, etc.), and it also speaks to philosophers’ traditional interest in reference determination. Finally, although I have focused on the LOT approach, my remarks can apply to philosophical approaches to concepts more generally, insofar as they individuate concepts by both meaning and computational (or functional) role.

Banishing the thought doi:10.1017/S0140525X10000476 Nina Strohminger and Bradley W. Moore Department of Psychology, University of Michigan, Ann Arbor, MI 48109-1043. [email protected] [email protected]

Abstract: The first seven chapters of Doing without Concepts offer a perfectly reasonable view of current research on concepts. The last chapter, on which the central thesis of the book rests, provides little actual evidence that using the term “concept” impedes scientific progress. It thus fails to demonstrate that this term should be eliminated from the scientific vernacular.

The newly minted cognitive psychology student is likely to have been taught that there are three major models of categorization – prototype, exemplar, and theory – and that they are vying with one another for the title of one true theory. The better part of

Doing without Concepts (Machery 2009) is spent revising this doctrine: rather than being mutually exclusive, the overwhelming evidence suggests that each of these strategies is used for determining category membership, depending on context. From these modest conclusions Machery derives a fairly radical idea: What we have been referring to as “conceptual processing” is actually several distinct processes, so distinct that no scientifically meaningful relationship exists between them. Because these processes do not form a natural kind – they do not overlap at the cognitive or neural level – any further discussion of “concepts” as a unit is both incoherent and detrimental to the study of these processes. Thus Machery advances, in the final chapter, a proposal for concept eliminativism, where the notion of a concept must be banished from science entirely. One would think that such a proposal requires some kind of evidence that these heuristic groupings generally prevent scientific advancement. Curiously, Machery provides virtually no support to this effect. He does mention, in passing, a few instances where scientific taxonomies have undergone restructuring based on deference to natural kinds (as when chemical classifications shifted to the periodic table), but this is quite separate from demonstrating that theories reflecting natural kinds are requisite for scientific progress. In any case, examples of such sea changes in psychology are rare. Machery cites “memory” and “emotion” as two terms which psychologists have successfully done away with, a claim we find puzzling. The idea that emotion contains “few scientifically relevant properties that are common to all emotions” (2009, p. 238) is far from the standard view – if anything, it is the division of emotions into ever-smaller categories that meets with resistance (Nesse & Ellsworth 2009). Memory is an even worse example. Machery asserts that the umbrella term “memory” has been tidily replaced with a suite of finer-grained labels like “working memory” and “explicit memory.” In fact, psychologists debate today not only where to carve memory into its constituent parts, but also whether to disambiguate memory at all. In one recent review, Jonides and colleagues concluded that the longstanding division between long-term and short-term memory is artificial, owing to the fact that the same mechanisms are involved in encoding, maintenance, and retrieval in both systems (Jonides et al. 2008). Here is a case where overzealous compartmentalizing has led scientists to overlook alternate possibilities that were evident in the available data. Of course, the memory case cannot directly inform whether “concept” should be discarded in favor of exemplar, prototype, and theory theories. But if a decades-long distinction between short-term memory and long-term memory has been overstated, then even the extensive evidence that Machery gathers in support of dividing concept into subcategories may erode after further study and review. What of those psychological terms which were never divvied up into natural kinds – how have they fared? It may be instructive to look at research on concepts, which, by Machery’s account, has been hobbled with an imperfect taxonomy for years. Fortunately, a survey of this research is conveniently located in the earlier chapters of his book, which is rich with experiments probing the nature of conceptual cognition. The arms race between camps of what were once considered competing theories appears to have led to a flourishing of work on this topic. If the unnatural kind-ness of “concept” has hindered research on conceptual processing, it certainly doesn’t show in Machery’s book. There is another hurdle facing would-be converts to the eliminativist project, which is the delicate matter of what to do once the word “concept” has been cast from the psychological vernacular. Machery is happy to supply us with a more technically accurate replacement: “bodies of knowledge used by default in the processes underlying most higher cognitive competencies” (p. 239). It is not clear why Machery endorses this ungainly locution, which would seem to retain all the scientific baggage of “concept.” And it is probably not a good sign that Machery BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts himself can hardly go more than a page without resorting to the forsaken term. Although Machery presents his project as a fundamentally pragmatic one, concept eliminativism does not seem particularly practical. It requires that we not only convolute our vocabulary, but also renounce our natural inclination to think of these processes as related on a functional level. A truly pragmatic revolution can hardly fail to take into account the minds that must work within the confines of the new paradigm. In this sense, concept eliminativism and old-fashioned eliminativism (which Machery takes pains to distance himself from) bear quite a bit in common: The proposals to eradicate inaccurate terminology, even if technically more precise, are so unwieldy that they would be unrealistic to adopt. If science were performed by robots, eliminating concepts might well be a reasonable prescription. Of course, the degree to which these rough intuitions hamper scientific progress would still need to be demonstrated. In considering proposals such as Machery’s, we must weigh the inconvenience of the newer, less fluent way of thinking against the advantages of doing so. In this case, the inelegance of the project is evident, and the benefits of adopting it are, as yet, untested and unknown. We therefore see no reason to jettison “concept” from the scientific discourse.

Are prototypes and exemplars used in distinct cognitive processes? doi:10.1017/S0140525X10000488 James Virtel and Gualtiero Piccinini Department of Philosophy, University of Missouri –St. Louis, St. Louis, MO 63121-4400. [email protected] [email protected] www.umsl.edu/ piccininig/

Abstract: We argue that Machery provides no convincing evidence that prototypes and exemplars are typically used in distinct cognitive processes. This partially undermines the fourth tenet of the Heterogeneity Hypothesis and thus casts doubts on Machery’s way of splitting concepts into different kinds. Although Machery may be right that concepts split into different kinds, such kinds may be different from those countenanced by the Heterogeneity Hypothesis.

In Doing without Concepts (2009), Machery’s argument that concepts split into different kinds is bold and inspiring but not fully persuasive. We will focus on the lack of evidence for the fourth tenet of Machery’s Heterogeneity Hypothesis (HH), according to which, “prototypes, exemplars, and theories are typically used in distinct cognitive processes” (Machery 2009, p. 4). Machery proposes three types of evidence that may support his fourth tenet (p. 124). If any of the following is shown for two kinds of concept, then it is likely that the two kinds of concept are used in distinct cognitive processes: 1. The neural systems implementing the cognitive processes that use the two kinds of concept are doubly dissociable. 2. The cognitive processes that use the two kinds of concept exhibit a difference in their input-output functions. 3. The cognitive processes that use the two kinds of concept do so by means of different algorithms. We accept these three criteria with one exception pertaining to Criterion 2: While we agree that a difference in outputs is evidence of distinct processes, we deny that a difference in the inputs alone is good evidence of distinct processes. Machery maintains that an input difference in categorization, for example, categorizing some items by means of prototypes and other items by means of exemplars, is evidence enough for his fourth tenet (Machery 2009, p. 124). Assuming for the sake

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of the argument that some items are indeed categorized by means of prototypes and others by means of exemplars, this shows only that we possess both prototypes and exemplars and use both in categorizing. Given this evidence, it may be that prototypes and exemplars are used in distinct processes, but it may also be that both prototypes and exemplars are used in the same process. Neither possibility is favored by an input difference. With this caveat in place, we argue that Machery does not fulfill any of his three criteria with respect to prototypes and exemplars. Some of our considerations go even further and raise doubts about Machery’s splitting of concepts into prototypes, exemplars, and theories. As to Criterion 1, Machery presents no evidence of doubly dissociable neural systems involving prototypes and exemplars. The only evidence he describes is of a single dissociation in amnesic patient E.P. (Machery 2009, p. 214). Since E.P. could not recognize previously seen items, E.P. was unable to add new exemplars to his long-term memory. But E.P. could still correctly categorize simple dot patterns in a way that suggests he used prototypes (Machery 2009, p. 215). After citing E.P.’s case, however, Machery cites evidence that previous exposure to category members is not necessary to perform well in the dot pattern task used to test E.P. A similar performance may be obtained by relying solely on short-term memory (Palmeri & Flanery 1999). Thus, as Machery points out (2009, p. 217), E.P.’s performance does not show that E.P. categorized by means of prototypes in the absence of exemplars (a single dissociation). And even if that were shown, a double dissociation would also require an additional case in which exemplars are used without prototypes. As to Criterion 2, Machery’s best evidence comes from experiments in which subjects learn some new categories A and B and then categorize some new stimuli as either A or B (Malt 1989). Under some conditions, subjects appeared to categorize a new stimulus by comparing it to the old stimulus most closely resembling it. Malt interpreted this as exemplar-based categorization. Under other conditions, subjects appeared to categorize a new stimulus by determining which features it possessed among those that were typical of a category. Malt interpreted this as prototype-based categorization. Based on Malt’s experiments, Machery concludes that people categorize some items using prototypes and others using exemplars, and he implies that the processes involved are distinct (Machery 2009, pp. 180 – 82). We reject Machery’s conclusion for two reasons. First, as we pointed out earlier, input differences alone are not good evidence of distinct processes. Hence, experiments such as Malt’s do not support the fourth tenet of HH unless there are also output differences. Malt (1989) reports no output differences. She does report a priming effect that occurs only under the allegedly exemplar-based strategy. But the priming effect changes a subject’s response time, not the output. Second, Malt’s experiments do not even show that, at least in the wild, subjects store both prototypes and exemplars properly so called (i.e., representations of particular objects) and use both in categorization. The stimuli in Experiments 1 –3 were drawings of real animals from different species (see Malt 1989, Fig. 1). They depict what a typical member of a species looks like, without any features that would distinguish one particular animal from another. Such stimuli are too generic to provide convincing evidence that subjects store exemplars properly so called. By contrast, the stimuli in Experiments 4 – 6 were artificial categories (Malt 1989, Figs. 2 and 3) whose structure is too different from natural categories to warrant any firm conclusion about the ordinary process of categorization. Machery himself worries that “these experiments tap into ad hoc strategies only used by subjects to deal with abnormal learning and categorization conditions” (Machery 2009, p. 183). As to Criterion 3, Machery points out that while both prototype-based models and exemplar-based models postulate that

Commentary/Machery: Pre´cis of Doing without Concepts categorization involves a computation of similarity, the two classes of models are different in one respect. Prototype-based models usually employ a linear similarity measure, while exemplar-based models usually employ a nonlinear similarity measure. This may suggest that prototypes are processed following an algorithm that uses a linear similarity measure, while exemplars are processed following an algorithm that uses a nonlinear similarity measure. But as Machery also points out, linear measures of similarity are not required for prototype-based models (2009, p. 90), and nonlinear measures of similarity are not required for exemplarbased models (2009, p. 98). Thus, the use of linear versus nonlinear measures does not determine whether an algorithm is prototype-based or exemplar-based. Therefore, there is no clear evidence that prototypes and exemplars are used in processes that follow different algorithms. In conclusion, Machery has provided no convincing evidence that prototypes and exemplars are typically used in distinct cognitive processes. This lack of evidence is enough to at least partly undermine the fourth tenet of HH. The considerations we have presented are part of a larger set of doubts on Machery’s way of splitting concepts into prototypes, exemplars, and theories. Machery may yet be right that there are different kinds of concept, but there might be a more fruitful way to split concepts into kinds than that postulated by HH. Specifically, one of us (Piccinini) has argued that the two main kinds of concept are implicit concepts and explicit concepts. Implicit concepts encode information about a category in an implicit form that cannot be accessed directly by the language faculty, whereas explicit concepts encode information in an explicit form that can be manipulated by the language faculty (Piccinini, forthcoming; Piccinini & Scott 2006). This proposal fits with and may contribute to explain a wide range of evidence about implicit versus explicit cognition (Evans & Frankish 2009). ACKNOWLEDGMENTS Thanks to Edouard Machery and Andrea Scarantino for helpful comments.

Doing with development: Moving toward a complete theory of concepts doi:10.1017/S0140525X1000049X Haley A. Vlach, Lauren Krogh, Emily E. Thom, and Catherine M. Sandhofer Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095-1563. [email protected] [email protected] [email protected] [email protected]

Abstract: Machery proposes that the construct of “concept” detracts from research progress. However, ignoring development also detracts from research progress. Developmental research has advanced our understanding of how concepts are acquired and thus is essential to a complete theory. We propose a framework that both accounts for development and holds great promise as a new direction for thinking about concepts.

In Doing without Concepts, Machery (2009) provides a solid argument for how the current construct of “concept” has led to useless controversies. While agreeing that research on concepts needs to be refocused, we contend that Machery’s proposal is only a small step towards a new framework for thinking about concepts. We suggest that a promising direction for concepts

exists in research that is too often ignored – the acquisition, formation, and development of concepts. Unfortunately, like many theories in cognitive science and philosophy, Machery’s proposal largely ignores development. Doing without Concepts avoids issues that are central to a theory of concepts: how concepts are acquired, how conceptual structures change across development, and how concepts that are coordinated early in development become uncoordinated over time. In fact, Machery’s proposal acknowledges the sentiment that development is less important than other areas of research on concepts (2009, p. 18). This blanket rejection of development is erroneous and dangerous – identifying the mechanisms by which concepts are acquired, how knowledge changes over time, and how cognitive processes give rise to such changes is essential to our understanding of how concepts operate and are organized. Because of the focus on such issues, many developmental researchers have taken a step back from the assumption that concepts originate from existing mental structures or representations. One way that developmental research has advanced our understanding of concepts is by introducing the idea that concepts are formed in the moment (e.g., Gibson 1969; Samuelson et al. 2009; Smith et al. 1999; Spencer & Scho¨ner 2003; Thelen & Smith 1994). By this account, performance on tasks is a reflection of the dynamic interaction between the learner and the learning environment – prior experiences recalled from long-term memory act together with task dynamics, perception, and action, to generate behavior. For example, one study (Samuelson et al. 2009) had children generalize novel nouns for rigid and deformable objects in two different tasks: a forced-choice task, in which children had to choose one of three objects that shared the same name with an exemplar, and a yes/no task, in which children had to respond whether each of the three objects shared the same name with an exemplar. Children’s performance in the two tasks suggested conflicting conclusions about how children organized categories. Performance on the forcedchoice task suggested that children treat rigid and deformable things differently when assigning labels: Rigid things are named by similarity in shape, whereas deformable things are named by similarity in material substance. However, performance on the yes/no task suggested that children did not distinguish between rigid and deformable things in naming and generalized names for both kinds by shape similarity. Given that children were all at the same developmental level and were presented with the exact same stimuli, differences in performance between the two tasks were likely not attributable to possessing different object concepts. Instead, children’s object concepts were formed in the moment given the demands of the different tasks. Machery notes that the feasibility of concepts being formed in the moment fails because there is not a significant body of research suggesting a “variability across contexts of the knowledge brought to bear on tasks” (2009, p. 22). However, Machery ignores countless examples of developmental research demonstrating great variability in the knowledge that is brought to bear in a particular task (e.g., Plumert 2008; Samuelson et al. 2009; Sandhofer & Doumas 2008; Siegler 1994; Smith et al. 1999; Thelen & Smith 1994; van Geert & van Dijk 2002; Vlach et al. 2008). This literature has demonstrated that performance on tasks is flexible (e.g., Sloutsky & Fisher 2008), context dependent (e.g., Samuelson & Smith 1998), and altered by seemingly minor changes in the conditions of the task (e.g., Sandhofer & Doumas 2008; Sandhofer & Thom 2006; Vlach et al. 2008). For example, altering the timing of exemplar presentation by a matter of seconds can lead to marked differences in children’s performance on a generalization task (e.g., Vlach et al. 2008). Machery also rejects the idea that concepts are formed in the moment, too quickly dismissing the proposal that there are not enduring mental structures for prototypes, exemplars, and theories. The book refers to this perspective as the “anti-representationalist argument” (2009, p. 222). However, developmental BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts research has provided substantial evidence suggesting that variability and behavior can be explained without the presence of a mental structure or representation (e.g., Samuelson et al. 2009; Smith et al. 1999; Spencer & Scho¨ner 2003; Thelen & Smith 1994). For example, Piaget (1954) originally attributed errors in the A-not-B task to infants’ lack of an “object concept” (i.e., an enduring mental structure about the spatial and temporal consistencies of objects). Over the last few decades, research has demonstrated that errors on this task result from factors other than a lack of an object concept (e.g., Smith et al. 1999), such as visual input (e.g., Butterworth et al. 1982), direction of gaze (e.g., Horobin & Acredolo 1986), and memory (e.g., Diamond 1985). Moreover, performance on this task can be explained by the interaction of these processes alone (e.g., Smith et al. 1999). Altogether, this work has provided a compelling argument for how concepts and performance are shaped in the moment from a confluence of factors, rather than being accessed from an enduring mental structure. To conclude, thinking about concepts needs a better direction. However, any theory of concepts would be remiss if it did not account for development. We propose that thinking about concepts should be situated within a dynamic framework that includes the learner and learning environment. Future research should explore the mechanisms by which concepts emerge in the moment – perception, action, and memory, interacting with properties of the environment, will reveal how this process works. This framework accounts for how concepts arise and change over time and thus holds great promise as the new direction for thinking about concepts.

The theoretical indispensability of concepts doi:10.1017/S0140525X10000506 Daniel A. Weiskopf Department of Philosophy, Georgia State University, Atlanta, GA 30302. [email protected] http://www2.gsu.edu/phldaw/

Abstract: Machery denies the traditional view that concepts are constituents of thoughts, and he more provocatively argues that concepts should be eliminated from our best psychological taxonomy. I argue that the constituency view has much to recommend it (and is presupposed by much of his own theory), and that the evidence gives us grounds for pluralism, rather than eliminativism, about concepts.

What are concepts? A long tradition in philosophy and psychology takes them to be the constituents of thoughts. In Doing without Concepts, Machery rejects this, defining them instead as bodies of knowledge stored in long-term memory and used by default in a range of higher cognitive processes (Machery 2009, p. 12). Machery’s arguments against the constituency view, however, are not compelling. He suggests that the notion of a constituent is ill-understood (2009, p. 26). But he also notes that the language of thought (LOT) hypothesis (Fodor 1975; 2008) gives us one fairly clear sense of what this might mean (Machery 2009, p. 27). So why shouldn’t we adopt precisely this sense? Moreover, it is hard to understand many of his own claims about conceptual processing without appealing to constituency. Prototypes, exemplars, and theories are all complex representations that bear structural relations to their parts, over which inferences, similarity computations, and the like, might operate. He may wish to remain neutral on issues of what the vehicles of thought are, but connectionist and dynamical systems models of these phenomena are notably unpromising. The best candidates for bodies of knowledge that can fill the role he posits are ones organized, inter alia, by relations of constituency.

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Constituency also plays a role in psychological explanation. Not everything about a category in long-term memory is or can be accessed in a single task; only some packets are extracted and used at once. Tokening a complex representation makes its constituents available to working memory for processing. And complex representations may make greater processing demands than simpler ones. The notion of a representational constituent is needed in describing what packets are retrieved from the vast reserves of long-term memory, and how these copies in working memory affect task performance. So we can safely embrace the notion that concepts are constituents of thoughts. But I agree with Machery that these constituents are heterogeneous (non-uniform, in my terms). My own provisional list of types of concepts includes prototypes, exemplars, causal models, ideals and norms, and some perceptual and linguistic representations. This is central to the pluralist view of concepts I defend (Weiskopf 2009a; 2009b). While Machery and I agree on much of the empirical data, we disagree on its import. Where I see evidence for pluralism, he favors eliminativism. I suggest that we should be optimistic about the study of concepts as such. Concepts are a functional kind, like most in psychology and neuroscience (Weiskopf, forthcoming). Consider how functional explanation proceeds. We decompose cognitive systems into a host of nested and interconnected subsystems, and populate them with representations, processes, and resources such as memory stores. This is obviously true in explaining competencies such as visual perception and numerical cognition, and it is no less true for concepts. Inductive and deductive reasoning, decision making, long-term planning, theory construction and testing, language use, and a host of “higher” capacities require explanation, and concepts are the representations, whatever they may be, that are proprietary to the system that underlies these capacities. Machery argues that concepts have nothing in common beyond this functional description, and hence are not a “natural kind” in his sense (2009, pp. 243– 44). But functional kinds are empirically discovered, and are posited in order to explain a (possibly open-ended) range of capacities that creatures possess. And concept possessors are strikingly different from creatures lacking concepts. They have a cognitive repertoire that is flexible – that is, sensitive to, but substantially independent of, ongoing perceptual input in terms of both content and processing – and that displays integration of information freely across domains (Weiskopf 2010). Concepts also explain the productive character of human thought in virtue of being able to combine open-endedly; Machery does not discuss productivity, but it is widely taken to be a central property of conceptual thought, and one that separates concepts from other types of representation. The fact that a separate cognitive system is needed to explain these capacities is a discovery, not an a priori deliverance. Otherwise we could have predicted from the armchair that Skinnerian behaviorism and its modern descendents (e.g., Brooksian robotics) were doomed to fail. Instead, the limits of these models are demonstrated by their failure to capture the relevant phenomena. Concepts constitute a kind because positing them gives us the needed explanatory leverage over a wide range of creatures and their capacities. If we posit them, we simultaneously gain the ability to account for phenomena that would otherwise have been inexplicable, and to capture similarities among otherwise dissimilar creatures. This is how the functional kinds posited in models of cognitive systems typically earn their distinctive status. If a model containing a functional category F has greater explanatory and unifying power than ones that lack it, then F is prima facie a kind. The failure of models of human cognition that lack anything corresponding to concepts shows that they satisfy this condition. But suppose we followed Machery’s lead and eliminated the term “concept,” talking only of prototypes, exemplars, and so

Commentary/Machery: Pre´cis of Doing without Concepts on. These representations may occur in many cognitive subsystems. Visual perception may involve generating and storing such representations; hence we often find talk of “perceptual prototypes” in the psychological literature. Without distinguishing concepts as such, we would be unable to state the ways in which perceptual prototypes differ from their conceptual kin. Conceptual prototypes, as opposed to perceptual ones, are capable of free recombination with other conceptual representations, are capable of being generated for non-perceptual categories, and so forth. Talk of prototypes alone will not do this crucial taxonomic work for us. For this we need the theoretical notion of a concept. Indeed, this explanatory need can be seen even in Machery’s own definition of concepts as being involved in “higher” cognitive capacities. For what makes one capacity “higher” than another? A tempting answer is that the “higher” ones are just the conceptinvolving ones. The fact that we need to appeal to concepts even to isolate these various types of representations in the first place suggests that concepts will be an essential part of our taxonomy of psychological kinds. Happy news for pluralists, but not for eliminativists.

Developing without concepts doi:10.1017/S0140525X10000518 Yevdokiya Yermolayeva and David H. Rakison Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213. [email protected] [email protected] http://www.psy.cmu.edu/people/yermolayeva.html http://www.psy.cmu.edu/people/rakison.html

Abstract: We evaluate the heterogeneity hypothesis by considering the developmental time course and the mechanism of acquisition of exemplars, prototypes, and theories. We argue that behavioral and modeling data point to a sequential emergence of these three types of concepts within a single system. This suggests that similar or identical underlying cognitive processes – rather than separate ones – underpin representation acquisition.

Doing without Concepts (Machery 2009) proposes an interesting solution to the problem of applying the term “concept” to prototypes, exemplars, and theories, which, according to the author, are unrelated. Each type of concept engages a distinct cognitive process – such as similarity comparison or causal inference – so that a unified label is inappropriate. The book synthesizes an impressive amount of literature in psychology and philosophy to provide evidence for this heterogeneity hypothesis. From the point of view of developmental psychology, however, two key questions remain unanswered. First, what is the time course for the emergence of prototypes, exemplars, and theories? Second, and more importantly, what is the mechanism behind their formation? Specifically, does each require a dedicated mechanism, or is a single system sufficient? In our view, an answer to the second question is particularly important for our ability to evaluate the proposal that distinct cognitive processes underlie the use of prototypes, exemplars, and theories. Answering the first question is an important component to answering the second question. If exemplars, prototypes, and theories emerge in succession and not simultaneously, then it is possible that they build upon each other. This could suggest the development of a single mechanism, or, at the very least, the development of three related mechanisms. While no single study provides definitive evidence, a pattern of successive emergence can be observed across studies. As an example, we can examine infants’ knowledge about individuals. Three-month-old infants can discriminate an image of their mother’s face from that of a stranger (Barrera & Maurer 1981), which suggests

that they have stored an exemplar of their mother’s appearance. By 6 months of age, infants can extract a prototype from a series of faces and display a preference for a novel face when it is presented with either a familiar face or the previously unseen prototype (Rubenstein et al. 1999). By 10 months, infants display more theory-like knowledge about individuals in that they do not generalize goal-directed actions, such as reaching for an object, from one individual to another (Buresh & Woodward 2007). This task requires not only theoretical knowledge about the properties of goals, but also the ability to store exemplars of the individuals so that goals may be matched correctly. Taken together, these studies provide some support for the sequential emergence of exemplars, prototypes, and theories. In addition to determining the time course for these processes, the most important developmental question with respect to the heterogeneity hypothesis relates to the mechanism of acquisition. Do dedicated mechanisms exist for of prototypes, exemplars, and theories, or are all three acquired within the same system? In our view, the literature points to the latter. We suggest that if the underlying mechanism of acquisition is shared, then entirely distinct cognitive processes do not underlie different types of concepts. According to Machery, prototypes and theories have little in common: the former involves extraction of the statistics of a category, while the latter involves causal inference. However, as has been suggested by Sobel and Kirkham (2007), statistical learning is involved in the emergence of causal reasoning. In the backwards blocking paradigm, children’s and adults’ judgments about an object’s causal effectiveness are influenced by prior knowledge about frequencies with which causal and noncausal objects are present in the environment. Sobel and Kirkham argue that causal knowledge involves probability distributions and likelihoods of particular hypotheses – to reason causally, children must have the ability to extract statistical regularities from the environment. The formation of prototypes and theories, then, relies on processing of statistical information. Work in computational modeling provides additional evidence for a shared mechanism by demonstrating that reasoning based on similarities (using exemplars or prototypes) and reasoning based on theories does not require separate architectures. Chaput and Cohen (2001) used hierarchical self-organizing maps to model changes in infants’ perception of simple collision events in which one ball causes another to move. Studies have shown that younger infants respond to such events based on temporal or spatial similarity. Older infants respond based on causal features of events: Noncausal events with a temporal gap in the sequence are perceived to be the same as those with a spatial gap, and different from continuous causal events. Chaput and Cohen (2001) produced a model in which the intermediate layers responded based on temporal and spatial components early in training, much like younger infants. As training progressed, the top layer integrated these components and began to respond based on causal information. Similarly, Verguts and Fias (2009) used modeling to demonstrate that similarity and rule-based responding can be thought of as lying on the same continuum. Similarity judgments are made based on many readily perceivable features; rule judgments are made based on fewer internally generated features. The model replicated human performance on a prediction task in which participants who received little training used similarity to observed cases to make predictions, and those who received more training used rules. With an increased number of training trials, the model progressed from making similarity judgments using the components of the input to making rule judgments by extracting regularities among components. Taken together with Chaput and Cohen’s (2001) work, these findings suggest that separate mechanisms are not necessary for the emergence of prototypes, exemplars, and theories, and that theories can emerge through the reorganization of similarity information within the same system. BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Commentary/Machery: Pre´cis of Doing without Concepts The proposal that exemplars, prototypes, and theories are underwritten by distinct processes is a convenient way to account for the conflicting psychological data on concepts. However, an examination of the developmental literature is necessary for the evaluation of this proposal. Behavioral and modeling studies suggest that exemplars, prototypes, and theories develop sequentially, and can do so within the same system without the need for three dedicated mechanisms. In our view, if the mechanism of acquisition is shared, then the cognitive processes underlying prototypes, exemplars, and theories must be partially, if not completely, overlapping, casting doubt on the heterogeneity hypothesis. From the developmental perspective, the three are not so distinct, and “doing without concepts” may be unnecessary.

Parsimony and the triple-system model of concepts doi:10.1017/S0140525X10000531 Safa Zakia and Joe Cruzb a Department of Psychology and Program in Cognitive Science, Williams College, Williamstown, MA 01267; bDepartment of Philosophy and Program in Cognitive Science, Williamstown, MA 01267. [email protected] http://www.williams.edu/Psychology/Faculty/Zaki/zaki.html [email protected] http://www.williams.edu/philosophy/fourth_layer/faculty_pages/ jcruz/jcruz.html

Abstract: Machery’s dismissive position on parsimony requires that we examine especially carefully the data he provides as evidence for his complex triple-system account. We use the prototype-exemplar debate as an example of empirical findings which may not, in fact, support a multiple-systems account. We discuss the importance of considering complexity in scientific theory.

In Doing without Concepts, Machery (2009) postulates a novel multiple-system theory of concepts: a triple-system theory. That is, he proposes a model that has a prototype process, a theory process, and an exemplar process. He cites experimental evidence in favor of each of these component systems and argues that none of them alone is sufficient to account for extant experimental data. In defense of the manifest complexity of the model, Machery argues that parsimonious theories are only to be preferred if they have historically “won out” in a particular domain: [O]ne can take parsimonious theories as more likely to be empirically supported than less parsimonious theories if and only if in the past, parsimonious theories have been better supported than less parsimonious theories. (Machery 2009, p. 126)

In his view, the unified, parsimonious view of cognition does not possess this historical advantage, so a lack of parsimony is no challenge to his multi-system theory. We find Machery’s treatment of parsimony odd. If a singleprocess theory does a credible job in matching a multiplesystems theory, it ought to be favored on the basis of parsimony. Machery, however, has closed off this appeal. His historical presumption makes it impossible for scientifically meritorious simple theories to triumph if they are late-comers, because by then, presumably, there would exist data sets that at least equally favor complex models. After all, by their nature, complex theories will be able to accommodate more of these data points than less complex models. Therefore, all that the complex models have to do is get there first. Of course, the serious downside to the ease with which a complex system can accommodate data is the loss of predictive ability. In our view, this is the point of privileging parsimony.

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Evidently Machery maintains that a multiple-systems theory is to be assessed solely in terms of data in its favor, regardless of complexity. To his credit, he canvases an impressive number of studies in the field. But because there is no presumption in favor of parsimony and therefore in favor of single-system accounts, we are inclined to look closely at the data that he uses as evidence of the several component processes in category learning. In at least one area, Machery erroneously concludes that the data favor his triple-system theory over a single-system account. Machery cites a series of articles by Smith and Minda to support the idea that exemplars are insufficient (Minda & Smith 2001; Smith & Minda 1998; Smith et al. 1997). In these articles, Smith and Minda presented evidence that they claimed challenged the predictions of the exemplar-based models of classification and that supported predictions of prototype models. The basis for these researchers’ claims was that the prototype models provided better quantitative fits to certain sets of individual subject categorization data than did the context model. However, Nosofsky and Zaki (2002) subsequently noted that in all of these quantitative-fit comparisons, Smith and colleagues considered the predictions from only a constrained version of the exemplar model which did not allow the model to capture the levels of deterministic responding that are often evidenced by individual subjects (e.g., Maddox & Ashby 1993). When a response-rule parameter was allowed to vary in the model, an exemplar model consistently outperformed the prototype model. Machery does not cite the debate that followed the original Smith and Minda articles and therefore leaves readers with the incorrect impression that the exemplar-prototype debate was settled in favor of a model that required both representations. Similarly, in Chapter 6, Machery cites a series of dot-pattern studies (Smith 2002; Smith & Minda 2001; 2002) that he claims provide support for the existence of a prototype process. In these studies, Smith and Minda fit models to data from a particular version of the classic dot-pattern paradigm (Knowlton & Squire 1993). In the Knowlton-Squire version of the dotpattern paradigm, during a study phase, participants are shown high-level distortions of a single pattern of nine dots arranged in a fixed shape. In a test that follows, participants judge the category membership of various new dot patterns that were derived from this category prototype but distorted at different levels (i.e., the prototype itself, low-level distortion, high-level distortions, and random patterns). The classic result is that the prototype is classified as a member of the category with the highest probability, followed by the low distortions, high distortions, and random patterns. Although both exemplar and prototype models predict the ordering of this typicality gradient, Smith and Minda claimed that only the prototype model could capture that steepness of the gradient. However, Zaki and Nosofsky (2004; 2007) demonstrated that this steepness was at least in part attributable to confounded properties of the stimulus set. This paradigm was simply not diagnostic in terms of telling apart the models. Machery’s claim that these dot-pattern studies in some way provided evidence for the existence of a prototype system and an exemplar system is simply not warranted. We do not claim that a single-system exemplar account of the data is the correct account of concept learning. Indeed, the idea that observers might use different systems to represent categories is highly plausible. And we note that we have only addressed a small portion of the data in Machery’s book. In evaluating evidence, we would, however, prefer to see a more careful treatment of the cost of complexity. Is a more complex model warranted by the data? We have no doubt that a triple-process or even a quadruple theory of categorization (see Machery 2009, p. 118, for a candidate fourth system) would predict a vast number of phenomena in the field. After a certain point, however, the important question is what would a complex model not predict? Many researchers are moving in a direction where models are penalized for being more complex (e.g.,

Response/Machery: Pre´cis of Doing without Concepts Myung et al. 2000; Myung & Pitt 2009; Navarro et al. 2004) by developing sophisticated metrics of complexity that go beyond traditional approaches of simply penalizing models for additional degrees of freedom. Machery seems to be pulling us in the other direction, and we should resist.

Author’s Response The heterogeneity of knowledge representation and the elimination of concept doi:10.1017/S0140525X10000932 Edouard Machery Department of History and Philosophy of Science, University of Pittsburgh, Pittsburgh PA 15260. [email protected] www.pitt.edu/machery/

Abstract: In this response, I begin by defending and clarifying the notion of concept proposed in Doing without Concepts (Machery 2009) against the alternatives proposed by several commentators. I then discuss whether psychologists and philosophers who theorize about concepts are talking about distinct phenomena or about different aspects of the same phenomenon, as argued in some commentaries. Next, I criticize the idea that the cognitivescientific findings about induction, categorization, concept combination, and so on, could be explained by positing a single kind of concept, and I insist that many categories (substances, types of events, etc.) are represented by distinct coreferential concepts that belong to very different kinds of concept. This is followed by an assessment of the hybrid theories of concepts offered by commentators, according to which categories, substances, and types of events are represented by hybrid concepts made of several parts. Finally, I defend the proposal that it may be useful to eliminate concept from the theoretical vocabulary of psychology.

R1. Introduction While writing Doing without Concepts (Machery 2009; henceforth, DwC), I had several goals. One of them was to clarify the notion of concept used in cognitive science and to regiment its use. I also wanted to put an end to useless controversies between philosophers and psychologists about what concepts are. Even more important, I wanted to make a case for a picture of knowledge representation that has emerged from 30 years of cognitivescientific research on the topic. According to that picture (which I called the Heterogeneity Hypothesis), cognitive competencies are often subserved by several distinct processes: There are many ways to categorize, draw inductions, and so forth. These processes are defined over distinct kinds of concepts, which have very little in common. Thus, the class of concepts divides into several heterogeneous kinds. Finally, I proposed a radical reform: It may be useful for cognitive scientists to eliminate the theoretical term concept from their theoretical vocabulary. Judging by the commentaries elicited by the book and the Pre´cis, it seems that the scientific community in cognitive science shares some of these views, although few seem

willing to accept the whole picture I proposed. In this response, I defend this picture. I am grateful for the thoughtful and challenging commentaries, which have highlighted some significant limits of the argument made in DwC, attracted my attention to some literature I unfortunately ignored while writing this book, and cast some doubts on the strength of particular arguments put forward in support of my views. Here is how I proceed in my response. After providing some clarifications in section R2, I turn to the characterization of the notion of concept used in cognitive science in section R3 and then examine the relation between the philosophy and the psychology of concepts in section R4. In section R5, I respond to the commentaries that defend the received view – that is, the view that there is only a single kind of concept (e.g., exemplars or prototypes). In section R6, I examine whether concepts that belong to different kinds (specifically, prototypes, sets of exemplars, and theories) can be coreferential. Section R7 focuses on the view that the class of concepts is even more heterogeneous than pictured in DwC. In section R8, I defend my criticism of hybrid theories of concepts, before examining in section R9 three responses to my proposal to eliminate concept from the theoretical vocabulary of cognitive science. R2. Clarifications Before addressing the substantive criticisms made by the commentators, I should briefly clarify a few misunderstandings. Hayes & Kearney misinterpret my concerns with the term concept. I do not recommend that concept be eliminated from cognitive scientists’ classificatory scheme because of the context-sensitivity of knowledge retrieval from long-term memory. (By contrast, Vlach, Krogh, Thom, & Sandhofer [Vlach et al.] recommend eliminating concept on precisely these grounds – more on this in section R3.) Indeed, I concur with Hayes & Kearney that behavioral and neuroimaging evidence shows that, while knowledge retrieval is indeed contextsensitive, some knowledge is also retrieved in a contextinsensitive manner (Machery, forthcoming; see discussion in sect. R3), as I explained in two distinct places of DwC (sects. 1.4.1 and 8.1.1). In fact, I propose to identify concepts with those bodies of knowledge that are retrieved in a context-insensitive manner. I also agree with Hayes & Kearney that models of cognitive processes that assume the existence of such context-insensitive bodies of knowledge are compatible with the contextual variation observed in experimental tasks. So, what are, for me, the grounds for eliminating concept? Instead of being concerned with flexibility and context-sensitivity, I suggest eliminating concept because, failing to pick out a natural kind, this term does not fulfill its classificatory function (see sects. R9.1 and R9.2), and because keeping this term would have some important drawbacks and very few benefits (see sects. R9.4 and R9.5). Vlach et al. object to my criticism of the anti-representationalist approaches in cognitive science, but they seem to misunderstand the claim made by these approaches (illustrated, e.g., by Rodney Brooks’s work and by some versions of the dynamical systems theory), namely, that cognitive science should explain behavior without BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Machery: Pre´cis of Doing without Concepts ascribing to organisms representations of their environment. Thus, contrary to what Vlach et al. seem to believe, anti-representationalist approaches to cognitive science do not merely reject the idea of enduring representations – namely, representations stored in longterm memory; they reject the idea that organisms have any kind of representations, including representations constructed on the fly. R3. What are concepts? One of the goals of DwC was to clarify and regiment the slippery notion of concept used in cognitive science. According to the characterization C (introduced and defended in Chapter 1), a concept of x is a body of knowledge about x that is used by default in the processes underlying our higher cognitive competencies when these result in judgments about x. Blanchard, Lalumera, Margolis & Laurence, Markman, Rey, Vlach et al., and Weiskopf raise some questions about this characterization of the notion of concept. Lalumera objects that C is an incomplete characterization of the notion of concept in cognitive science, but her own discussion belies this claim. Although the notion of a higher cognitive competence was left vague, it is clear that the examples of competencies that, according to her, I ignored (viz., generalization and induction) straightforwardly fall under this notion. Indeed, induction is one of the examples I have repeatedly used to illustrate what higher cognitive competencies are. Blanchard doubts that the notion of default is an appropriate way of cashing out the notion of concept used in cognitive science. Noting that many experiments meant to support the theory theories of concepts are not run under time pressure, he concludes that either theory theorists do not view their work as bearing on concepts – which, I agree with Blanchard, is dubious – or the notion of default fails to capture the notion of concept they use. Blanchard’s objection is an excellent occasion to clarify the notion of default (see also Machery, forthcoming). On my view, three properties are characteristic of the bodies of knowledge retrieved by default: speed, automaticity, and context-independence. The bodies of knowledge retrieved by default come to mind quickly; their retrieval does not depend on one’s intentional control (although they may also be intentionally retrieved); and they are retrieved in all contexts. Of these three properties context-independence is the essential one, while speed and automaticity are likely effects of context-independence: Because a body of knowledge is retrieved in a context-insensitive manner, its retrieval from memory might be simpler and thus faster, and it can be automatized. On my view, being retrieved quickly – for example, being retrieved under time pressure in an experimental context – is evidence, but not a necessary condition, for being retrieved by default. Thus, experimental tasks that do not involve time pressure can be used to examine people’s concepts. Rey criticizes my alleged identification of concepts with the bodies of knowledge used under time pressure (see my response to Blanchard above), and he proposes to identify concepts with those bodies of knowledge we would use 232

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if we were to reflect. However, if we were to follow this proposal, psychologists working on concepts would be unable to explain how people categorize, make inductions, understand words, and so on, in most circumstances. Like Barsalou (1993) and Prinz (2002), Vlach et al. hold that concepts are temporary constructs in working memory on the grounds that there is “a great variability of the knowledge that is brought to bear in a particular task.” I concur with Vlach et al. that the knowledge brought to bear on a given task is influenced by context; indeed, I explicitly acknowledge this variability in DwC, and the useful references they give in their commentary provide further evidence of this variability. However, as shown in DwC and in the Pre´cis, several remarkable behavioral and neuroimaging studies also support the claim that some bodies of knowledge are retrieved by default (Barsalou 1982; Weisberg et al. 2007; Whitney et al. 1985). Vlach et al. do not address this body of evidence and provide no reason to doubt the conclusion it seems to support. Other findings provide further evidence for the claim that some bodies of knowledge are retrieved by default. Although their goal was to highlight the context-sensitivity of knowledge retrieval, Hoenig et al. (2008) have shown that brain areas involved in representing actions or physical manipulations are automatically activated in a feature verification task when the question concerns the appearance of a tool, while, conversely, the brain areas involved in representing visual appearances are automatically activated when the question concerns the movement associated with a natural object. James and Gauthier (2003) have similarly shown that auditory areas and areas dedicated to the processing of movements are activated by the mere visual presentation of novel objects (greebles) when participants have respectively been trained to recognize the sounds of these objects and their movements. In both experiments, knowledge that is not relevant to the tasks at hand is accessed in a context-insensitive manner. Furthermore, the variability of the knowledge used in different contexts is consistent with the fact that knowledge retrieval from long-term memory is partly contextinsensitive. Indeed, Hayes & Kearney discuss several models that account for the variability while assuming that some bodies of knowledge are retrieved by default (see also sect. 1.4.1 of DwC). Both Margolis & Laurence and Weiskopf concur that defining concepts as the constituents of thoughts provides a better characterization of the notion of concept than C. Before discussing this proposal, note that, contrary to what Weiskopf claims, I do not deny that concepts are the constituents of thoughts. Rather, I insist that concepts should not be defined as the constituents of thoughts (sect. 1.4.3 of DwC). Margolis & Laurence and Weiskopf propose to clarify the notion of a constituent of thought by appealing to the language-of-thought hypothesis. However, it would be a mistake to build an empirical hypothesis as controversial as this in the very notion of concepts since the validity of the work of cognitive scientists working on concepts (e.g., their account of the bodies of knowledge retrieved when we categorize or draw an induction as well as their accounts of the categorization or induction processes)

Response/Machery: Pre´cis of Doing without Concepts does not hang on the truth of the language-of-thought hypothesis. Finally, there is little benefit to defining concepts as constituents of thoughts since this definition does not cast light on cognitive scientists’ research. While Margolis & Laurence mention numerous psychologists who do characterize concepts as constituents of thoughts (Carey, etc.), they do not show that this characterization plays any role in these psychologists’ actual empirical work on concepts. Weiskopf mentions the need to explain the productivity of human thought (see also Hill 2010). This suggests that he does not simply define concepts as constituents of thoughts. Instead, for him, a body of knowledge is a concept only if it can combine freely with any other body of knowledge (i.e., only if it meets Evans’s [1982] generality constraint). First, it is not clear that productivity is among the explananda of a psychological theory of concepts, but I will not press this point here (Machery 2010). Rather, I propose that Weiskopf’s definition is arbitrary. If concepts are defined this way, organisms that are able to combine all bodies of knowledge freely (as humans do according to Weiskopf) have concepts, whereas organisms that are able to combine many such bodies, but not all, do not, even if the bodies of knowledge in the former and latter kinds of organism are very similar. Here is another way of illustrating the arbitrariness of Weiskopf’s proposal. Suppose we humans have some bodies of knowledge that are used in categorization, induction, and the like, in almost the same way as our other bodies of knowledge. For example, they could be prototypes, and they could be used in similarity-based processes. But, while the other bodies of knowledge can be combined freely, these prototypes can only be combined with a limited number of other prototypes (perhaps because they belong to a modular cognitive system). Weiskopf would conclude that these prototypes are not concepts since they cannot combine freely with all the other bodies of knowledge, but this is the wrong conclusion to draw. Instead, the similarities between these prototypes and the other bodies of knowledge should lead us to conclude that they are concepts and to reject Weiskopf’s proposal that a body of knowledge counts as a concept only if it can combine with every other body of knowledge. Rey argues that my characterization of concept entails that two individuals or even a single individual at two different times cannot have the same concept. However, it is incorrect that a single individual at two different times cannot have the same concept, since a given concept can remain the same although its parts (viz., the elements of knowledge that are by default retrieved from long-term memory) change, exactly as objects remain the same despite their parts changing (Machery 2010). It is true that, given my characterization of concept, different individuals are likely to have different concepts (e.g., different concepts of dogs), but I think that this is a clear virtue, since this explains why they categorize differently and make different inductions. Finally, Markman argues that it is a mistake to characterize psychological constructs, such as the notion of concept, by means of particular cognitive competencies and experimental tasks since adequate psychological constructs should explain performance in a range of tasks;

and he regrets that I did not pay attention to a broader range of competences and tasks. I agree with Markman’s argument. Although DwC examined only three cognitive competencies and focused on a few psychological tasks, the proposed characterization of concept – namely, C – is consistent with Markman’s point.

R4. Philosophers and psychologists on concepts Another goal of DwC was to show that, although philosophers and psychologists use the term concept and develop theories of concepts, psychological and philosophical theories of concepts are really about different things. As a result, many criticisms of psychological theories of concepts by philosophers (and vice versa) are empty. Edwards, Margolis & Laurence, Rey, and Schneider disagree with this claim. Keil also seems to assume that philosophical and psychological theories of concepts are about the same thing, but does not press this point. Edwards and Margolis & Laurence are right to claim that the fact that psychologists and philosophers have different explanatory interests does not entail that they are theorizing about different things, because they could be theorizing about different aspects of the same phenomenon. I deny the univocity of concept across philosophy and psychology on the following grounds. Generally, in philosophy of science, a candidate explication of a scientific term (e.g., ether, force) is taken to be a failure if it entails that what scientists say when they use this term is typically mistaken. This is in fact a commonsense idea: If my interpretation of what someone means by a particular word entails that most what she says when she uses this word is false, my interpretation is probably mistaken. Applied to the notion of concept in cognitive science, a satisfactory explication of this notion should not entail that psychologists’ claims about concepts are literally false. C, but not Margolis & Laurence’s characterization, meets this constraint. (A similar objection applies to Rey’s and Edwards’s characterizations.) Since Margolis & Laurence propose that the bodies of knowledge psychologists have been focusing on (viz., prototypes, exemplars, etc.) belong to the processing structure of concepts (whatever that is), they are bound to claim that psychologists are literally mistaken when they say that concepts are prototypes or exemplars (etc.). Margolis & Laurence take the mutual influence of philosophers and psychologists to be evidence that psychologists and philosophers are talking about the same thing when they theorize about concepts, but I am not swayed by this argument. First, I am more impressed by how often psychologists and philosophers talk past each other when they exchange arguments about concepts than by how useful these exchanges have been. Second, the extent to which philosophers and psychologists have fruitfully influenced each other is perfectly consistent with the idea that they do not theorize about the same thing when theorizing about concepts. Cell biologists and physicists working on quantum mechanics do not theorize about the same thing, but the former are usefully appealing to the theories and findings of the latter (e.g., Collini et al. 2010). BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Machery: Pre´cis of Doing without Concepts Because I hold that philosophers and psychologists who theorize about concepts are not talking about the same thing, I find Edwards’s and Rey’s critiques puzzling. Clearly, it is important to understand better how concepts refer, but this is not one of the explananda of the theories developed by psychologists theorizing about concepts. Furthermore, as argued in section 2.4 of DwC, the kind of arguments exchanged by philosophers, which often rely on intuitions about what concept an individual in a particular circumstance possesses or fails to possess, or about whether two individuals have the same belief (e.g., Burge 1979), seems unlikely to be appropriate for determining how concepts refer. Schneider notes that psychologists’ findings about how we categorize, draw inductions, and so forth, can be coopted by some philosophical theories of concepts – those that individuate concepts by means of the functional role of mental symbols. While correct, this observation does not undermine the claim that psychologists and philosophers tend to theorize about different things when they theorize about concepts. Similarly, while cell biologists use biochemists’ findings, biochemists and cell biologists are not developing theories about the same processes.

R5. Rejecting the heterogeneity of the class of concepts To my surprise, few commentaries objected to the claims that there are several kinds of concepts, and that prototypes, exemplars, and theories are among these kinds. Strohminger & Moore and Rey seem even to have found this claim entirely unsurprising. However, I worry that this lack of resistance is due to a confusion between two different claims: (1) that prototypes, exemplars, and theories are three distinct kinds of concept; and (2) that our long-term memory includes prototypes, exemplars, and theories. These two claims are not identical since one could grant that we have, say, exemplars and theories, but insist that concepts are prototypes (e.g., Hampton 2001) or that we have prototypes and exemplars, but insist that concepts are really theories. While Claim 2 is indeed not very controversial, Claim 1 is less obviously correct. Hayes & Kearney, Harnad, and Hampton are the only ones to raise doubts about the heterogeneity of concepts. Harnad rejects the idea that the bodies of knowledge we use in higher cognitive tasks are prototypes or exemplars. Rather, sensorimotor processes play a central role in cognition: They enable living creatures to navigate the world by making object identification and appropriate action possible. There is no doubt that such sensorimotor processes exist since recognition must involve them, but these processes cannot underwrite the bulk of cognition. Like behaviorists and like some roboticists (see Machery 2006b, on Harvey), Harnad underestimates the complexity of higher cognition when he proposes that the processes that can explain perceptual and motor processing can scale up to higher cognition. Appealing to words, as Harnad does, does not help much since words have to be understood and mapped onto bodies of knowledge in long-term memory. 234

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Hampton contends that the distinction between exemplars and prototypes breaks down for at least some categories since the exemplars of superordinate categories, such as the category of vehicles, are prototypes of subordinate categories, such as the category of cars (see also Malt, forthcoming). However, superordinate categories could be represented by representations of particular cars, planes, bikes, and so on, rather than by prototypes of cars, plans, bikes, et cetera. This is naturally an empirical question, and Hampton might well be right. Supposing he is right, it would nonetheless be a mistake to say, as he does, that the exemplars of superordinate categories are prototypes of the subordinate categories. Rather, using the term exemplar to refer only to representations of individuals, one should say that superordinate categories are represented by sets of prototypes of subordinate categories. In any case, Hampton’s interesting comment brings to the fore a shortcoming of DwC – the neglect of superordinate concepts and the focus on basic-level categories (e.g., dogs). It might be that the heterogeneity of conceptual representations is even larger than proposed in DwC, with superordinate categories being represented differently from basic-level and subordinate categories. Importantly, far from undermining the crucial message of the book, this outcome would reinforce it: It is hopeless to look for a theory of conceptual representations that applies to all default bodies of knowledge. Hayes & Kearney defend the received view. They contend that exemplar models of a range of phenomena outperform prototypes models (see also Zaki & Cruz), and that the role of theories can naturally be included in exemplar models (on this latter point, see also Yermolayeva & Rakison). But some of the studies they themselves cite in fact undermine this claim. Far from providing evidence that categorization can be explained by means of a single kind of default bodies of knowledge, Allen and Brooks (1991) provided evidence that, in at least some circumstances, we have two distinct representations of a single category (a rule and a set of exemplars) and that these representations can lead to conflicting categorization judgments. Smith et al. (1998) have replicated these findings, and provided evidence that two neural networks are involved in each categorization judgment. Furthermore, exemplar models seem able to explain the empirical findings about concepts only if one takes exclusively into consideration the category-learning studies that involve artificial stimuli such as patterns of points. Research on the knowledge of expert physicians (reviewed in Norman et al. 2006) shows, for instance, that during their training in medical school, physicians acquire different types of bodies of knowledge that are largely independent from one another. Further, for Hayes & Kearney, the role of theories in cognition is indirect: They influence which exemplars are learned in category-learning tasks or which exemplars are retrieved from long-term memory (which explains perhaps why Hayes & Kearney prefer to speak of prior knowledge instead of theories). However, a less partial review of the literature suggests that the use of theories is not so limited, and that causal theories are directly used to categorize and to make inductions (Chs. 6 and 7 of DwC). Finally, in DwC, I argued that simplicity can be used to choose between scientific hypotheses in a domain of inquiry (e.g., in psychology) only if past evidence

Response/Machery: Pre´cis of Doing without Concepts inductively supports the belief that in this domain simpler hypotheses tend to be better supported than more complex hypotheses. By contrast, Zaki & Cruz argue that simplicity should always be preferred on the grounds that models with more free parameters fit better a given set of data points than models with less free parameters; and they conclude that, everything else being equal, we should prefer a theory that posits a single kind of concept to a theory that posits several distinct kinds of concept. Although the question cannot be resolved in a few words, their argument should be resisted for two reasons. First, it is not necessarily the case that simpler models fit better than more complex ones when fit is evaluated by cross-validation since models with more parameters can overfit. Second, model fitting provides a poor, albeit common (e.g., Forster & Sober 1994), analogy for understanding the use of simplicity as a criterion for theory choice. More complex theories are not necessarily better supported than simpler theories since they often have empirical implications that simpler theories simply do not have. For instance, the heterogeneity hypothesis – but not (e.g.) prototypes theories – predicts that in at least some cases people’s categorization (or induction) judgments are going to be slower or less reliable because, for example, the prototype-based and theory-based categorization (or induction) processes conflict with one another (for consistent evidence, see Allen & Brooks 1991; Kulatanga-Moruzi et al. 2001; Regehr et al. 1994; Smith et al. 1998; and see sects 5.1.5, 6.6, and 7.1.5 of DwC). R6. Can prototypes, exemplars, and theories be coreferential? While I proposed that we often have several coreferential concepts – for example, we might have a prototype of dogs, a set of exemplars about dogs, and a theory of dogs – Hampton, Jacobson, and Margolis & Laurence doubt that prototypes, exemplars, and theories can corefer. In DwC, I intentionally refrained from proposing a theory of how bodies of knowledge such as prototypes, sets of exemplars, and theories denote (see Edwards’s and Rey’s commentaries), and this is not the place to propose one. However, for present purposes, it is sufficient to note that, according to several influential theories of reference, a prototype, a set of exemplars, and a theory can be coreferential. Consider, for instance, Fodor’s informational semantics (e.g., Fodor 1990). According to this view, roughly, a concept refers to the property that it is nomologically linked to. As argued convincingly by Prinz (2002), informational semantics can naturally be combined with prototype theories: A prototype of dogs refers to dogs because its occurrence (i.e., its retrieval from long-term memory) is nomologically linked to the presence of dogs in the cognizer’s environment. Informational semantics can similarly be combined with theory theories and with exemplar theories. Thus, it is perfectly possible for a prototype, a set of exemplars, and a theory to be coreferential. Jacobson and Margolis & Laurence assert, mistakenly, that a prototype, a set of exemplars, and a theory cannot be coreferential because they fail to distinguish reference and categorization (on this distinction, see

sect. 2.2.2 of DwC). The fact that objects can be miscategorized (e.g., a wolf can be misclassified as a dog) shows that reference differs from categorization. Indeed, categorization judgments could not be mistaken if every object that is categorized as an x (e.g., as a dog) really belonged to the extension of the concept of x (e.g., the concept of dog). Because reference differs from categorization, the fact that prototype-based and theorybased categorization processes can occasionally classify some objects differently (see the examples in sect. 3.3 of DwC and in Machery & Seppa¨la¨, forthcoming) does not entail that a prototype and a theory cannot be coreferential. R7. Should the heterogeneity hypothesis be broadened? Couchman, Boomer, Coutinho, & Smith (Couchman et al.), Dove, and Khemlani & Goodwin argue that the heterogeneity hypothesis fails to capture the extent to which concepts form an heterogeneous kind, and they propose to extend the idea that concepts divide into very different kinds in two distinct directions. I am sympathetic to this kind of proposal: One of the important questions to be addressed by future research on knowledge representation is whether prototypes, exemplars, and theories exhaust the fundamental kinds of default bodies of knowledge (see the conclusion of DwC). Dove proposes that, in addition to distinguishing prototypes, exemplars, and theories, it is necessary to distinguish at least two types of format: amodal and modal (the Representational Heterogeneity Hypothesis). Contra Barsalou, Prinz, and others, Dove agrees with me that not all concepts have a perceptual format (Dove 2009; Machery 2006c; 2007; forthcoming). However, following the dual-coding tradition (Paivio 1991), he also holds that some concepts have such a format. While the evidence reviewed by Dove is surely suggestive, I remain to be convinced. It is again important to keep in mind the distinction (which is apparently not challenged by Dove) between concepts and the knowledge used in higher cognition: Concepts are just a subset of the knowledge used in higher cognition (see sect. 1.1 of DwC and sect. R3 here for a discussion of how to draw the distinction). There is no doubt that we use perceptual representations to solve some tasks, and it is plausible indeed, as Dove argues, that dedicated cognitive systems are used for this purpose. However, this does not entail that these representations are concepts since they might only be used in particular circumstances, in a contextsensitive manner. Khemlani & Goodwin propose to add two other kinds to the fundamental kinds of concepts: rules and generic representations. Goodwin’s work about conceptual illusions provides some striking evidence that people are able to learn rules that determine category membership. However, as I explained in section 4.1.4 of DwC, the concern with the rule-based approach to concepts is not that people are unable to learn and apply rules, but rather that natural categories, outside of contrived laboratory conditions, do not have the definitions that rule-based accounts assume. In contrast to rules, it is extremely plausible that people store some knowledge about generics, as BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Machery: Pre´cis of Doing without Concepts Prasada’s work (among others) suggests. In DwC, following many psychologists, I took knowledge of generics and knowledge of causal relations to be constitutive of theories, but, unfortunately, I did not defend this proposal. Khemlani & Goodwin want to distinguish representations of generic information from theories apparently on the grounds that generic knowledge cannot be identified with causal knowledge. However, because generic knowledge and causal knowledge about some x’s (e.g., dogs) are plausibly intertwined, it would be a mistake to hold that generic knowledge and theories form distinct kinds of concepts. In the terminology of DwC (sect. 3.3.1; see also sect. R8 here), they seem to be linked (or connected) and coordinated. As a consequence, they should be thought of as parts of the same concept, our theory of dogs, rather than as two distinct concepts. In this sense, theories are true hybrids: They are made of distinct types of knowledge, perhaps used in distinct processes, that are linked and coordinated. R8. Hybrid theories Proponents of hybrid theories of concepts agree with some, but not all, tenets of the heterogeneity hypothesis. They propose that the heterogeneous bodies of knowledge about a given category (e.g., dogs) are not distinct concepts, but rather the parts of a single concept. Keil, Gonnerman & Weinberg, Margolis & Laurence, and Scarantino defend hybrid theories against the attack mounted in section 3.3 of DwC. As I understood the notion of part, if two bodies of knowledge A and B are parts of the same concept, using A enables the use of B (A and B are linked or connected), and A and B must not lead to incompatible judgments that are taken to be equally authoritative (A and B are coordinated). Keil, Gonnerman & Weinberg, Margolis & Laurence, and Scarantino all reject Coordination as a necessary condition for two bodies of knowledge to be parts of the same concept. Although Coordination may finally turn out to be an inappropriate way to characterize the notion of conceptual parthood, I will defend it here. Note that, if Coordination is rejected, an alternative characterization of the notion of conceptual parthood should be provided (which Keil fails to do). Before discussing their key arguments, let me clarify Coordination a bit. Coordination does not state that the parts of a hybrid concept cannot underlie incompatible judgments. This would be an inaccurate way of cashing out the notion of a part of a concept since some wellknown hybrid theories of concepts (e.g., Osherson & Smith 1981) assume that the judgments underwritten by prototypical information can be defeated by (and can thus be incompatible with) the judgments underwritten by theoretical or definitional information. What Coordination excludes is that the parts of a concept give rise to incompatible judgments that are taken to be equally authoritative. Keil and Gonnerman & Weinberg propose two different arguments purporting to show that Coordination cannot be a necessary condition for distinct bodies of knowledge to count as parts of the same concept. Keil rightly notes that predicates such as “tall” can lead to apparent contradictions: Someone can be tall with 236

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respect to some standard and not tall with respect to another standard. He infers that, if Coordination were a necessary condition for conceptual parthood, we would have to conclude, absurdly, that all these judgments involve distinct concepts. However, what is going on in these cases is clearly quite different from what is going on when people both agree and disagree with “Tomatoes are vegetables.” In the former case, when one both agree and disagree with the claim that, say, John is tall, it is not because “tall” is ambiguous: The meaning of “tall” is the same in “John is tall” and “John is not tall.” Instead, one can both agree and disagree with the claim that John is tall because two distinct standards are applied (compared with Tom Cruise, John is tall, but, compared with Shaquille O’Neal, John is not tall). By contrast, one can both agree and disagree with the claim that tomatoes are vegetables because “vegetable” is ambiguous: The meaning of “vegetable” (i.e., the concept this word expresses) is not the same in “Tomatoes are vegetables” and in “Tomatoes are not vegetables.” Gonnerman & Weinberg note insightfully that the distinct exemplars of, for example, dogs (which, according to me, form a concept of dogs) seem to violate Coordination, which shows that Coordination cannot be a necessary condition for distinguishing concepts. Exemplars of, say, dogs can indeed underlie incompatible judgments – such as, inconsistent categorization judgments – since different exemplars might be retrieved from long-term memory in different circumstances, but it does not follow that these judgments are taken to be equally authoritative (although, to my knowledge, exemplar theorists have said little about this question). Perhaps the judgment underwritten by the larger number of exemplars is taken to defeat the judgment underwritten by the smaller number of exemplars. Although evidence is lacking to evaluate this proposal, it might thus be that exemplars do not violate Coordination. Scarantino proposes to replace Coordination with weaker conditions, which are satisfied by prototypes, exemplars, and theories, and concludes that these count as parts of concepts rather than as distinct concepts. The main problem with Scarantino’s conditions is that they are neither sufficient nor necessary for two bodies of knowledge to be parts of the same concept. Consider, for instance, his first proposal: Two bodies of knowledge are coordinated when the knowledge stored in one of them influences the acquisition of the knowledge stored in the other. A first problem with this proposal is that genuinely distinct concepts meet this condition. For instance, when one forms a body of knowledge about a new animal species, our theoretical body of knowledge about animals in general is likely to influence this acquisition process. A second problem is that the parts of a single concept need not meet this condition. For instance, the elements of knowledge about the typical properties of dogs are parts of the same prototype of dogs, but my knowledge about a typical property of dogs needs not influence the acquisition of my knowledge about another typical property. Finally, Keil argues that hybrid theories of concepts can account for the findings that I argued undermine them – namely, the fact that people are willing to endorse apparent contradictions such as “Tomatoes are vegetables” and “Tomatoes are not vegetables,” particularly when such

Response/Machery: Pre´cis of Doing without Concepts sentences are prefaced with hedges such as “in a sense” (see Machery & Seppa¨la¨ [forthcoming] for some relevant findings and discussion). There are two issues that need to be distinguished here. First, would people be willing to endorse apparent contradictions such as “Tomatoes are vegetables” and “Tomatoes are not vegetables” if they were not prefaced by hedges such as “in a sense”? I suspect that this is the case, but there is no clear evidence for this claim. Second, supposing that people would agree that tomatoes are vegetables and that they are not vegetables even without such edges, hybrid models would then be compatible with people’s judgments only if Coordination were rejected. But, if hybrid theorists reject Coordination, they then need to explain why distinct bodies of knowledge about, say, tomatoes are parts of the same concept of tomato instead of being distinct concepts of tomato. R9. Eliminativism Most commentators reject the eliminativist conclusion put forward in DwC, even when they agree with the existence of three different types of concepts. R9.1. Are there generalizations about concepts?

A key step in the eliminativist argument proposed in DwC consists in denying that the class of default bodies of knowledge forms a natural kind on the grounds that few scientifically interesting generalizations are true of this class. Blanchard, Danks, Lombrozo, Virtel & Piccinini, and Yermolayeva & Rakison challenge this claim. Inspired by her fascinating work on explanation, Lombrozo proposes that prototypes, exemplars, and theories (together with, perhaps, a host of other kinds of knowledge) are used to explain. While she takes this finding to unify concepts, I disagree. Ribosomes and transfer RNA are both involved in the production of proteins out of amino acids, but it does not follow that they form a single kind. In fact, I propose that a different conclusion follows from Lombrozo’s work. This work suggests that it might not be possible to characterize the notion of theory by means of the notion of explanation, as I did in DwC (following many psychologists), since being used to explain is not a distinctive property of theories. Just like induction, categorization, or concept combination, explanation might be one of the cognitive competencies that are subserved by distinct processes defined over different kinds of default body of knowledge. Blanchard, Virtel & Piccinini, and Yermolayeva & Rakison challenge the claim that prototypes, exemplars, and theories are used in distinct processes (e.g., distinct categorization processes). If they are right, then generalizations about cognitive processes are true of all concepts, and the class of concepts is a genuine natural kind. Blanchard notes that some evidence for the existence of theories (Luhmann et al. 2006) is compatible with theories being used in similarity-based processes just like exemplars and prototypes (see also Hayes & Kearney and Yermolayeva & Rakison). However, research on induction (reviewed in sect. 7.1 of DwC) shows that the process underlying theory-based induction and the

processes using prototypes and exemplars differ. The latter are similarity-based – representations are compared with one another, and their match is evaluated by some similarity measure – while phenomena like the causal asymmetry effect show that the inductive processes using causal knowledge are not based on similarity. Virtel & Piccinini hold that there is no evidence that exemplars and prototypes are used in different kinds of cognitive process. However, even if exemplars and prototypes were used in the same kind of process, it would still be the case that theories are used in a different kind of process, and thus that there are no generalizations about how concepts are used in cognitive processes. Yermolayeva & Rakison rightly bemoan the fact that DwC paid little attention to developmental psychology, including the acquisition pattern of prototypes, exemplars, and theories (but see the brief discussion of Smith & Minda [1998] in sect. 6.4.4 of the book), and further work on the developmental questions raised in their commentary is called for. However, I find the proposed developmental sequence unconvincing. First, I object to the way exemplar is used in Yermolayeva & Rakison’s commentary: An exemplar is not just any representation of an individual, it is a representation that is used by default in higher cognition. Thus, the fact that babies acquire representations of individuals (e.g., of their mother) early does not entail that they acquire genuine exemplars: Such representations are exemplars only if they can be used, for example, to categorize in addition to identify the individuals they are about. Second, in some category-learning experiments with adult participants, prototypes seem to be acquired before exemplars (Smith & Minda 1998). Research on medical expert knowledge also suggests that causal theories are acquired at the beginning of physicians’ training, before physicians acquire any knowledge of particular cases (in the second half of their training) and form prototypes (for review, see Norman et al. 2006). It would thus seem that there is no necessary acquisition sequence, which casts doubts on the idea that a single process underlies the acquisition of prototypes, exemplars, and theories. Danks puts forward a distinct criticism, based on his finding that prototype-, exemplar-, and theory-based formal models of categorization can be seen as distinct graphical models (Danks 2007). However, the fact that different formal models are instances of a more abstract formalism does not entail that the processes described by these models form a unified kind. Lokta-Volterra equations in ecology, Hodgkin and Huxley’s model of the action potential in neuroscience, and the Cagan monetary model in economics are all differential equations. Would Danks conclude that they form a unified kind of process that is the object of a unified empirical theory? R9.2. Do concepts form a natural kind?

Samuels & Ferreira acknowledge, perhaps for the sake of the argument, that the class of bodies of knowledge used by default in higher cognition includes several kinds that have little in common (viz., prototypes, exemplars, and theories), but they insist that the bodies of knowledge used by default in higher cognition are a natural kind on the grounds that they form a genuine BEHAVIORAL AND BRAIN SCIENCES (2010) 33:2/3

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Response/Machery: Pre´cis of Doing without Concepts homeostatic property cluster kind. However, being a homeostatic property cluster kind is not sufficient for being a natural kind. Natural kinds are those kinds that support a large number of scientifically relevant inductions. Because few generalizations are true of them, the bodies of knowledge used by default in higher cognition do not form a natural kind. Samuels & Ferreira rightly note that there is no precise cutting point for distinguishing those kinds that support, respectively, few and many generalizations. However, those kinds that support many generalizations are different from those kinds that support only few generalizations, exactly as white differs from black even if there is no precise cutting point when one moves from white to black through gray (Machery 2005). The bodies of knowledge used by default in higher cognition are an instance of the latter type of kinds and, for this reason, are not a natural kind.

While Couchman et al., Khemlani & Goodwin, Lalumera, and Weiskopf acknowledge that prototypes, exemplars, and theories form distinct kinds of concept and that there are few generalizations true of all of them, they reject the conclusion that the notion of concept should be eliminated from the theoretical vocabulary of psychology on the grounds that concepts form a functional kind. While concepts are indeed a functional kind – my own explication of the notion of concept, C, is functional – this alone does not settle the issue of whether concept should be eliminated. Being a functional kind is not sufficient for earning one’s keep in a scientific classification: Some functional kinds have their place, and others do not. Functional kinds have their place in scientific classificatory schemes either because they are natural kinds or because denoting them fulfills some useful function and does not have any drawback. So, what needs to be shown is either that many generalizations are true of the bodies of knowledge used by default in higher cognition or that denoting this class fulfills some useful function in cognitive science.

this claim. Furthermore, as noted earlier in my reply to Yermolayeva & Rakison (sect. R9.1), it is not the case that prototypes, exemplars, and theories are necessarily acquired in any particular order. Weiskopf provides a different, intriguing reason for keeping the term concept in the classificatory scheme of cognitive science: There is an important distinction between those organisms that have this type of body of knowledge and those that do not. However, I doubt that the class of organisms that have concepts would be of interest to comparative psychologists. It will probably be more fruitful to examine which organisms have prototypes, and which processes in which species use these, or to compare the causal theories in humans and the theories (or proto-theories) in other species. That is, my concerns about the usefulness of the notion of concept for psychologists working on human higher cognition carry over to comparative psychology. It might even well be that the term concept misleads us in thinking that the class of organisms that have concepts is an interesting class for comparative psychology, exactly as it misleads us in thinking that in the human mind they form an interesting class for cognitive scientists working on higher cognition. Strohminger & Moore note that keeping concept within the classificatory scheme of psychology has numerous virtues. Further research might undermine the evidence suggesting that there are very different kinds of body of knowledge used by default in higher cognition, and not eliminating concept might keep psychologists aware of this possibility. However, I doubt that caution is needed here because the evidence in support of the existence of distinct kinds of concept seems unlikely to be undermined. Strohminger & Moore also note that the description “bodies of knowledge used by default in higher cognition” is ungainly, and, as a result, unlikely to be adopted by cognitive scientists as a replacement for concept (see similar concerns in Mercier 2010). I hope that the benefits that I argue would fall out from eliminating concept will convince cognitive scientists that this is a cost worth paying.

R9.4. Is the notion of concept useful for cognitive scientists?

R9.5. Does the eliminativist argument against “concept” overgeneralize?

Hampton, Lalumera, Strohminger & Moore, and Weiskopf contend that the notion of concept has a useful role to play in cognitive science and that, as a result, eliminating it would be detrimental. Hampton contends that the term concept is useful for bringing attention to the way prototypes, exemplars, and theories are organized, but I do not see exactly why this term would be needed. It seems straightforward to ask whether prototypes, exemplars, and theories are coordinated, whether they acquired in any particular developmental sequence, and so on. Lalumera argues that we need the notion of concept to explain why our representation of, say, dogs changes over time: We start with exemplars of particular dogs, then develop a prototype, and finally build a theory. This won’t do, however, for – as I have argued at length – there is no such thing as our representation of dogs. Rather, we simultaneously have several distinct representations of dogs, and Lalumera provides no reason to doubt

According to Gonnerman & Weinberg, Khemlani & Goodwin, and Margolis & Laurence, my eliminativist argument cannot be valid because, if it were, we would have to eliminate numerous notions that have earned their keep in science in general and in cognitive science in particular, such as the notions of representation, module, algorithm, and nutrient. After all, representations and nutrients are probably no more natural kinds than concepts. In response, first, I do not hold that concept should be eliminated merely because it fails to pick out a natural kind. Rather, in addition to failing to pick out a natural kind, keeping concept has numerous drawbacks and few benefits. Second, the case of concepts is very different from the case of, say, representations. Psychologists do not attempt to discover generalizations about representations in general and to encompass these generalizations within a theory of representations, while they do precisely this for concepts. There is thus no theoretical habit to curb

R9.3. Concepts as a functional kind

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References/Machery: Pre´cis of Doing without Concepts in the case of representations, while, if I am right about how knowledge is organized, cognitive scientists’ tendencies to develop theories of concepts should be curbed (for some recent attempts, see, e.g., Gallese & Lakoff 2005; Martin 2007; Prinz, forthcoming). One might wonder whether the use of concept really impedes the progress of cognitive science. Strohminger & Moore rightly note that I provide little actual evidence in support of this claim. Couchman et al. seem to agree with this criticism. I acknowledge that this claim is partly speculative, but it strikes me as plausible. While the use of concept in cognitive science and the attempts to develop a unified theory of concepts have not prevented cognitive scientists for making numerous findings about concepts, some important questions have not attracted sufficient attention, such as: How are prototypes, exemplars, and theories used concomitantly? And what happens when they yield incompatible judgments? I am less convinced than Couchman et al. and Markman that we have already acquired an extensive knowledge about these questions, although some noticeable work has already been conducted (including by Smith and Markman). I further hypothesize that much more work would be done on these questions if cognitive scientists stopped theorizing about concepts, and started theorizing about prototypes, exemplars, and theories.

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Some appropriate rationales for seeking Open Peer Commentary would be that: (1) the material bears in a significant way on some current controversial issues in behavioral and brain sciences; (2) its findings substantively contradict some well-established aspects of current research and theory; (3) it criticizes the findings, practices, or principles of an accepted or influential line of work; (4) it unifies a substantial amount of disparate research; (5) it has important cross-disciplinary ramifications; (6) it introduces an innovative methodology or formalism for broader consideration; (7) it meaningfully integrates a body of brain and behavioral data; (8) it places a hitherto dissociated area of research into an evolutionary or ecological perspective; etc. In order to assure communication with potential commentators (and readers) from other BBS specialty areas, all technical terminology must be clearly defined or simplified, and specialized concepts must be fully described. In case of doubt of appropriateness for BBS Commentary, authors should submit a detailed target article proposal using the new BBS Editorial Manager site at http://www.editorialmanager.com/bbs/. After evaluating the proposal, the Editors will encourage or discourage formal target article submission. A note on commentaries: The purpose of the Open Peer Commentary service is to provide a concentrated constructive interaction between author and commentators on a topic judged to be of broad significance to the biobehavioral science community. Commentators should provide substantive criticism, interpretation, and elaboration as well as any pertinent complementary or supplementary material, such as illustrations; all original data will be refereed in order to assure the archival validity of BBS commentaries. Commentaries and articles should be free of hyperbole and remarks ad hominem. Please refer to and follow exactly the BBS Instructions for Commentators at http://journals.cambridge.org/BBSJournal/Inst before submitting your invited commentary. Style and format for target articles: Target Articles must not exceed 14,000 words (and should ordinarily be considerably shorter); commentaries should not exceed

1,000 words, excluding references. Spelling, capitalization, and punctuation should be consistent within each article and commentary and should follow the style recommended in the latest edition of A Manual of Style, The University of Chicago Press. It is advisable to examine a recent issue of BBS as a model. Target articles should be submitted in MSWord format to the new Editorial Manager site at http://www.editorialmanager.com/bbs/. Figures should appear in the body of the text, not at the end of the paper, and should also be supplied as separate TIFF, EPS, JPEG, or GIF files. However, if your article is accepted, TIFF or EPS format will be requested for publication since printing requires resolutions of at least 1100dpi. (Please note that costs for color figure reproduction will be passed along to the author. Color printing is expensive, and authors are encouraged to find alternative methods for presentation of their argument.) Once accepted, a Call for Commentators will be sent to thousands of BBS Associates and readers. The Call letter includes a link to the pre-copyedited final draft archived publicly for potential commentators. The copyedited final draft will only be posted for the invited commentators. Please make sure your target article file has ALL of the following in this order: Four Separate Word Counts (for the abstract, main text, references, and entire text – total + addresses etc.), an Indexable Title, Full Name(s), Institutional Address(es), E-mail Address(es) and Homepage URL(s) for all authors (where available), Short Abstract (100 words), Long Abstract (250 words), 5–10 Keywords (in alphabetical order), approx. 12,000 word Main Text (with paragraphs separated by full blank lines, not tab indents), and Alphabetical Reference List. Target article authors must also provide numbered headings and subheadings to facilitate cross-reference by commentators. Tables and figures (i.e., photographs, graphs, charts, or other artwork) should be numbered consecutively, and should appear in its appropriate location. Every table should have a title; every figure, a caption. Endnotes and appendices should be grouped together at the end of the paper and should ideally be locally linked to in the text to facilitate the reader (and of course the referee’s task). Acknowledgements should be placed at the end of the paper. The short abstract will appear by way of an advertisement, one issue in advance of the publication issue. The long abstract will be circulated to referees and then potential commentators should the paper be accepted, and will appear with the printed article. BBS’s rigorous timetable constraints (requiring the coordination of target articles, commentaries and author’s responses within the publishing queue) make it extremely difficult for us to process follow-up drafts of your submission. Please make sure that the paper you submit is the carefully checked final draft to which you wish the referees to address. Please also ensure that your submission has been proof-read by a native English speaker before submission. This, of course, greatly improves its chances at the refereeing stage. References: Bibliographic citations in the text must include the author’s last name and the date of publication and may include page references. Complete bibliographic information for each citation should be included in the list of references. Please also include and link to the WWW URL for any paper for which it exists. Examples of correct styles are: Brown (1973); (Brown 1973); Brown 1973; 1978); (Brown 1973; Jones 1976); (Brown & Jones 1978); (Brown et al. 1978). References should be in alphabetical order in the style of the following examples. Do not abbreviate journal titles: Freeman, W. J. (1958) Distribution in time and space of prepyriform electrical activity. Journal of Neurophysiology 2:644–66. http://cogprints.soton.ac.uk/abs/ neuro/199806009 Dennet, D. C. (1991) Two contrasts: Folk craft versus folk science and belief versus opinion. In: The future of folk psychology: Intentionality and cognitive science, ed. J. D. Greenwood, pp. 26–7. Cambridge University Press. http:// cogprints.soton.ac.uk/abs/phil/199804005 Bateson, P.P.G. & Hinde, R.A., eds. (1978) Growing points in ethology. Cambridge University Press. Editing: The publishers reserve the right to edit and proof all articles and commentaries accepted for publication. Authors of target articles will be given the opportunity o review the copy-edited manuscript and page proofs. Commentators will be asked to review copy-editing only when changes have been substantial; commentators will not see proofs. Both authors and commentators should notify the editorial office of all corrections within 48 hours or approval will be assumed. Author response to commentaries: All invited commentaries received before the deadline are only accessible to the Authors and Editors. Please note that no commentary is officially accepted until the Editor in charge has formally reviewed it and notified both the authors and the Editorial Administrator. Please refer to and follow exactly the BBS Commentary Response Instructions at http://journals.cambridge.org/ BBSJournal/Inst before submitting your response. Authors of target articles receive 50 offprints of the entire treatment, and can purchase additional copies. Commentators will also be given an opportunity to purchase offprints of the entire treatment.

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Offprints of the following forthcoming BBS treatments can be purchased for educational purposes if they are ordered well in advance. For ordering information, please write to Journals Department, Cambridge University Press, 32 Avenue of the Americas, New York, NY 10013-2473.

The weirdest people in the world? Joseph Henrich, Steven J. Heine, and Ara Norenzayan

Comorbidity: A network perspective Angélique O. J. Cramer, Lourens J. Waldorp, Han L. J. van der Maas, and Denny Borsboom

Précis of Doing without Concepts Edouard Machery

Behavioral and Brain Sciences

In this issue

To appear in upcoming issues

Neural re-use: A fundamental organizational principle of the brain An emerging class of theories concerning the functional structure of the brain posits that neural circuits established for one purpose are commonly exapted (exploited, recycled, redeployed) during evolution or normal development and put to different uses, often without losing their original functions. This suggests a functional architecture in which the neural structures that support high-order cognitive tasks are composed from sets of low-level circuits that are frequently and widely shared among many different high-level neurofunctional structures. The idea has implications for evolution, development, localization, cortical parcellation, medical rehabilitation, and machine interface design, among other topics.

The Simulation of Smiles (SIMS) model: Embodied simulation and the meaning of facial expression

Theories of embodied cognition have motivated an explosion of research in psychology and the neurosciences on the processing of facial expression of emotion. Still unclear is how the component processes of emotion and their neural mechanisms support embodied simulation, and what triggers its occurrence. We examine the roles of the brain’s reward systems, amygdala, somatosensory cortices, and motor centers in recognizing and interpreting smiles, and we integrate these with research on facial mimicry and eye gaze. A model of the processing of the smile is presented as a means to advance the application of theories of embodied cognition in the study of facial expression of emotion more generally.

Person as Scientist, Person as Moralist Joshua Knobe, Yale University

Volume 33, Number 2/3

Paula M. Niedenthal, Martial Mermillod, Centre national de la recherche scientifique and the University of Clermont-Ferrand, France, Marcus Maringer, University of Amsterdam, and Ursula Hess, Humboldt-Universität Berlin

April/June 2010

Michael L. Anderson, Franklin & Marshall College and University of Maryland

It has often been suggested that people’s ordinary capacities for folk psychology and causal cognition make use of much the same methods one might find in a formal scientific investigation. A series of recent experimental results offer a challenge to this widely held view, suggesting that people’s moral judgments can influence the intuitions they hold both in folk psychology and in moral cognition. The present target article argues that these effects are best explained on a model according to which moral considerations actually figure in the competencies people use to make sense of human beings and their actions.

Among the articles to appear in forthcoming issues of BBS:

Cambridge Journals Online For further information about this journal please go to the journal website at: journals.cambridge.org/bbs

Pages 61–244

M. L. Anderson, “Neural re-use: A fundamental organizational principle of the brain” P. M. Niedenthal, M. Mermillod, M. Maringer & U. Hess, “The Simulation of Smiles (SIMS) model: Embodied simulation and the meaning of facial expression” D. Jones, “Human kinship, from conceptual structure to grammar” J. Knobe, “Person as Scientist, Person as Moralist” S. Carey, “Précis of The Origin of Concepts W. von Hippel & R. Trivers, “The evolution and psychology of self-deception” H. Mercier & D. Sperber, “Why do humans reason? Arguments for an argumentative theory”

An International journal of current research and theory with open peer commentary Volume 33 | Issue 2/3 | April/June 2010 | ISSN: 0140-525X