The Psychology of Learning and Motivation : Advances in Research and Theory, Volume 31

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s OF LEARNING AND MOTIVATION Advances in Research and Theory

VOLUME 3 I

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THE PSYCHOLOGY OF LEARNING AND MOTIVATION Advances in Research and Theory

EDITEDBY DOUGLAS L. MEDIN DEPARTMENT OF PSYCHOLOGY NORTHWESTERN UNIVERSITY. EVANSTON. ILLINOIS

Volume 31

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ACADEMIC PRESS

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This book is printed on acid-free paper.

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Copyright 0 1994 by ACADEMIC PRESS. INC. All Rights Reserved. N o part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system. without permission in writing from the publisher.

Academic Press, Inc. A Division of Harcourt Brace & Company 525 B Street, Suite 1900, San Diego. California 92101-4495 United Kingdom Edition published bv Academic Press Limited 24-28 Oval Road. London NW 1 7DX

International Standard Serial Number: 0079-742 1 International Standard Book Number: 0- 12-543331 -X PRINTED IN THE UNITED STATES OF AMERICA 94 95 9 6 9 7 98 9 9 B B 9 8 7 6 5

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3 2 1

Contributors ..............................................................................

ix

ASSOCIATIVE REPRESENTATIONS OF INSTRUMENTAL CONTINGENCIES

Ruth M . Colwill I . Introduction ...................................................................... 11. Binary Associations

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...........................

111. Hierarchical Structure of Instrumental Learning ...................... IV. Binary versus Hierarchical Associations ................................. V. Conclusion ........................................................................ References ........................................................................

1 2 45

50 64 65

A BEHAVIORAL ANALYSIS OF CONCEPTS: ITS APPLICATION TO PIGEONS AND CHILDREN

Edward A . Wasserman and Suzette L . Astley I. Introduction ...................................................................... 11. Toward a Behavioral Analysis of Concepts

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111. Empirical Evidence on Basic-Level Conceptualization by Pigeons ........................................................................ IV. Stimulus Generalization and Conceptualization ......... ......... V. A Spencian Model of Basic-Level Categorization ..................... VI. Conceptualization via Primary and Secondary Stimulus Generalization ................................................................... VII. Nonsimilarity-Based Conceptualization ..................................

73 75 77 84 89 105

106

vi

Contents

VIII . Summary of Empirical Evidence ........................................... IX . Concluding Comments ........................................................ Appendix .......................................................................... References ........................................................................

116

THE CHILD'S REPRESENTATION OF HUMAN GROUPS

Lawrence A . Hirschfeld I. I1. I11. IV . V. VI . VII .

Introduction ...................................................................... The Psychological Representation of Human Groups ................ An Alternative Model of Social Development .......................... Race and Perceptual Information .......................................... Do Children Have a Theory of Race? .................................... Racial Thinking and Folk Biology: Areas of Divergence ............ Conclusion ........................................................................ References ........................................................................

133 135 139 141 151 163 174 180

DIAGNOSTIC REASONING AND MEDICAL EXPERTISE

Vimfa L . Patel. Jose' F . Arocha. and David R . Kaufman 1. I1. 111. IV . V. VI .

Introduction ...................................................................... The Task Domain of Medical Diagnosis ................................. Methodological Approach and Conceptual Issues ..................... Expert Diagnostic Reasoning . ............................ Novice Diagnostic Reasoning ............................................... General Discussion .............. ............................. Appendix: Glossary of Medical Terms ................................... References ........................................................................

187 189 194 201 226 233 245 246

OBJECT SHAPE. OBJECT NAME. AND OBJECT KIND: REPRESENTATION AND DEVELOPMENT

Barbara Landau I . Introduction ...................................................................... 11. Object Shape and Object Name: Basic Patterns of Development

I11. Syntactic Effects and the Shape Bias ..................................... IV . Perceptual Effects: Information about Shape and the Shape Bias ........................................................................

253 256 262 267

Contents

V . Effects of Knowledge: The Role of Known Taxonomies and Functions .................................................................... VI . The Role of Object Shape in Early Object Naming ................... VII . Summary and Conclusions ................................................... References ........................................................................

vii

211 291 300 301

THE ONTOGENY OF PART REPRESENTATION IN OBJECT CONCEPTS

Philippe G . Schyns and Gregory L . Murphy 1. I1 . Ill . IV .

The Origins of Parts ............................................................ Empirical Tests of the Functionality Principle ......................... Shape Variation and Part Extraction ...................................... General Discussion ............................................................. References ........................................................................

Index ........................................................................................

305 314 332 341 347

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CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contributions begin

Jose F. Arocha (187), Departments of Medicine and Psychology, McGill University, Montreal, Quebec, Canada H3A lA3

Suzette L. Astley (73), Department of Psychology, Cornell College, Mount Vernon, Iowa 523 14 Ruth M. Colwill ( l ) , Department of Psychology, Brown University, Providence, Rhode Island 029 I2 Lawrence A. Hirschfeld ( 1 33), Department of Anthropology, University of Michigan, Ann Arbor, Michigan 48109 David R. Kaufman (187), Laboratory of Cognitive Studies in Medicine, McGill University, Montreal, Quebec, Canada H3A 1A3 Barbara Landau (253), Department of Cognitive Science, University of California at Irvine, Irvine, California 92717 Gregory L. Murphy (305), The Beckman Institute, University of Illinois, Urbana, Illinois 61801 Vimla L. Patel (187), Departments of Medicine and Psychology, McGill University, Montreal, Quebec, Canada H3A lA3 Philippe G. Schyns (305), Center for Computational and Biological Learning, Whitaker College, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 Edward A. Wasserman (73), Department of Psychology, The University of Iowa, Iowa City, Iowa 52242 ix

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ASSOCIATIVE REPRESENTATIONS OF INSTRUMENTAL CONTINGENCIES Ruth M . Colwill

1.

Introduction

A cardinal feature of much of animal and human behavior is the susceptibility of that behavior to modification by its consequences. In general, it has been found that the arrangement of rewarding outcomes contingent on an action increases the frequency of that action, whereas the arrangement of response-contingent aversive outcomes reduces the probability of that action. This type of behavioral plasticity has been widely documented for many vertebrate species and even for some invertebrates. The question of how instrumental contingencies alter behavior has commanded considerable attention in the discipline of experimental psychology. The focus of this chapter is on the nature of the associative processes involved in instrumental learning. Particular consideration is given to two issues: what structures represent learning that a behavior will produce a positive outcome and what structures represent learning that a behavior will not have a positive outcome. For several decades, the experimental investigation of instrumental learning has been occupied with the task of describing how the consequences of an action bring about a change in the subsequent probability of that action. One attempt to answer that question involves identifying what it is that an animal learns from its experience with an instrumental contingency. A typical situation used to address this issue is one in which THE PSYCHOLOGY OF LEARNING AND MOTIVATION. VOL. 31

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Copyright 0 1994 by Academic Press. Inc. All rights of reproduction in any form reserved.

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Ruth M. Colwill

a rat is trained to make a response (e.g., press a lever) for a rewarding outcome (e.g., a food pellet) either in the presence of a discriminative stimulus (e.g., a light) o r in its absence (S+ and S- training, respectively). Experience with an S + contingency results in an increase in the probability of lever pressing when the light is on, whereas experience with an Scontingency produces a decrease in lever pressing when the light is on. Two rather different ideas have emerged about the nature of the associative structures that might account for these changes in behavior. The conventional approach has been to analyze learning in terms of simple binary associations between the three primary elements of an instrumental situation: the discriminative stimulus (S), the instrumental response (R), and the instrumental outcome (0).Individuals have disagreed, however, about which particular binary connections are important in instrumental learning. Most of the controversy has been over the form that encoding of the response is thought to take. Some have argued for an association between the stimulus and the response (S-R), whereas others have favored an association between the response and the outcome (R-0). In both cases, it has seemed reasonable to complement either the S-R or R - 0 relation with an association between the stimulus and the outcome (S-0). In contrast, contemporary analyses of instrumental learning have favored an approach in which the three primary elements are linked within a single hierarchical structure (S-R-0). Almost uniformly, models of this sort have represented the stimulus as a modulator of the R - 0 relation. In the discussion that follows, I describe some work from my laboratory that is pertinent to identifying the circumstances under which binary and hierarchical associative structures are recruited for representing information about instrumental contingencies. The first part of this chapter reviews evidence that animals learn about each of the potential binary associations. I comment on the conditions that support this learning and discuss evidence regarding the nature of those learned associations. The second part of this chapter reviews studies showing support for a hierarchical account of instrumental learning. Finally, I discuss the relationship between these different representational structures and suggest one factor that might influence the type of associative structure used to support performance in goal-directed tasks. 11. Binary Associations

One of the most popular approaches to understanding instrumental behavior has been to dissect what the animal learns about each of the potential binary connections between S, R, and 0. Colwill and Rescorla (1986)

Instrumental Contingencies

3

reviewed the evidence for the contribution of three different binary associations to instrumental behavior maintained by food rewards. They identified the primacy of an association between the response and the outcome (R-0) and documented the pervasiveness of that learning across a wide range of training conditions. More equivocal conclusions, however, were drawn about the relative importance of associations between the stimulus and the response (S-R) and the stimulus and the outcome (S-0) to instrumental performance. A.

ASSOCIATIONS RESPONSE-OUTCOME

1 . Background

Precursors to the idea that an association is formed between the response and its outcome may be found in a number of early discussions of animal behavior. For example, Spencer (1871, p. 544)argued that animals could “perceive the connection between a muscular act and its immediate effect” and Morgan (1894, p. 216) suggested that one reason an animal repeats a rewarded action is “because it had become associated through experience with pleasurable consequences.” This viewpoint was embraced by Konorski and Miller (1937) and vigorously defended by Tolman (1933). Its popularity has not diminished and it continues to be endorsed by a number of contemporary learning theorists (Bolles, 1972; Colwill & Rescorla, 1986; Mackintosh & Dickinson, 1979). Several lines of support have emerged in favor of the view that an animal’s actions may be part of a deliberate scheme to attain a specific goal (see Colwill & Rescorla, 1986). What is arguably the most powerful procedure for revealing the presence of an R - 0 association is to change the value of the outcome after instrumental learning has taken place. The theoretical implications of the outcome revaluation technique were formally recognized by Rozeboom (1957, 1958). He opined that if subsequent instrumental performance tracks the new value of the goal, it may be inferred that the subject has learned about the consequences of that instrumental act. It has been repeatedly demonstrated in the last few years that instrumental responses change in a manner appropriate to t h e new value of their goal object (e.g., Adams & Dickinson, 1981; Colwill & Rescorla, 1985a, 198Sb, 1986, 1990a; Dickinson & Dawson, 1987; Rescorla, 1990b; Shipley & Colwill, 1993). For example, Colwill and Rescorla (198Sa) trained rats to lever press for one outcome (e.g., pellets) and to chain pull for a different outcome (e.g., sucrose liquid). Then, one outcome was made aversive by pairing it with a nausea-inducing substance. In a subsequent extinction test, the rats preferred the response whose outcome was still valuable. Because the effect of devaluing an outcome was to

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Ruth M. Colwill

change specifically performance of the response trained with that outcome, we have support for R-0 learning. 2 . Permanence of R - 0 Learning Colwill and Rescorla (1986) presented considerable evidence that R-0 learning occurs across a wide variety of instrumental training parameters. In the following sections, I describe some work suggesting that, once established, R - 0 associations are relatively permanent. Using the outcome revaluation technique to assess the presence of an R-0 association, I have explored the impact of three potentially destructive operations on this type of association. In each case, a clear picture has emerged of the immunity of R - 0 associations to disruption caused by the passage of time and to extinction achieved either by simple nonreinforcement of the response or by arranging a negative correlation between the response and its outcome. u. Passage of Time. There is an extensive literature documenting the fact that performance based on long-term memory for individual events deteriorates with the passage of time (Gleitman, 1971; Spear, 1978). Of particular relevance are studies that have examined the effect of increasing the interval between training and testing on memory for instrumental events (e.g., Gleitman & Steinman, 1964; Hunter, 1913, 1934; Perkins & Weyant, 1958; Steinman, 1967). The typical finding in these reports is that performance declines as the retention interval is increased. For example, Gleitman and Steinman (1964) examined contrast effects as a function of time. When rats that had been trained t o run down an alley for a large reward were shifted to a small reward, they ran more slowly for that small reward than control subjects trained throughout with the small reward. This depression effect (Crespi, 1942) was not observed, however, if an interval of 66 days separated the end of training with the large reward and the shift to a small reward. A natural interpretation of these results is that the animals tested after a long retention interval had forgotten details about the magnitude of the original outcome. In contrast, other work has suggested that learned associations may be quite resistant to the passage of time. Using rats, Gleitman & Jung (1963) found no forgetting after a 44-day retention interval for which one of two keys had to be pressed 10 times for a food reward. Other studies have confirmed the relative stability of memory for simple choices (Chiszar & Spear, 1968; Maier & Gleitman, 1967). One possible explanation for these findings is that even if animals forget some of the details of an outcome, they can still use an intact response-outcome association to guide their selection of the reinforced response over the nonreinforced alternative.

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5

In an experiment carried out at the University of Pennsylvania, I explicitly examined the effects of time on retention of response-outcome associations. In that study, I varied the retention interval between training instrumental responses and testing them for evidence of association with their outcomes. Rats were trained initially to make two responses (e.g., lever press and chain pull). One response was followed by food pellets and the other by a liquid sucrose reward. Fifty-four days after this training, those same animals were trained to make two other responses (e.g., nose poke and handle pull). Again, one of these responses was followed by food pellets and the other by sucrose liquid. Training of both pairs of responses was identical. Each response was reinforced first on a continuous reinforcement (CRF) schedule until 50 reinforcers had been obtained, and then on a variable interval (VI) schedule for two 20-min sessions. A VI 30-sec schedule was in effect in the first of these sessions and a VI 60-sec schedule in the second session. The various response-outcome combinations and their assignment to the distant or recent training condition was counterbalanced across animals. Immediately after training of the second pair of responses, one of the outcomes was made distasteful by pairing it with a nausea-inducing toxin, a 0.5% body weight injection of 0.6 M lithium chloride. In this and all subsequent experiments using the outcome devaluation technique, the procedure for devaluing the outcome was identical to that described by Colwill and Rescorla (1985a, Experiment 1). At the end of this treatment, no subjects ate their devalued outcome, but all subjects consumed their valued outcome. Finally, subjects were given two 10-min extinction tests, one with the recently trained responses and the other with the distantly trained responses. The question of interest is whether the passage of time would attenuate the magnitude of the devaluation effect, that is, the degree to which the animals would choose the valued response over the devalued response during testing. The results of these tests are shown in Fig. 1, separated according to length of retention interval (RI) and the current value of the outcome. Analysis of the recently trained response data revealed significantly more valued responses than devalued responses. This finding replicates the basic devaluation effect obtained by Colwill and Rescorla (1985a). The unique contribution of these test results, however, is the finding that interpolating a retention interval of 66 days between the final session of VI training and the test sessions did not attenuate the magnitude of the devaluation effect. In fact, the difference between the valued and devalued responses is numerically, although not statistically, larger for the responses tested after a long retention interval. This pattern of results suggests that the strength of a response-outcome relation does not fade with time.

Ruth M. Colwill

6

SHORT RI

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Fig. I . Sensitivity to outcome devaluation of instrumental responding with either a short (left panel) or long (right panel) retention interval (RI) between training and testing. One outcome had been paired with a nausea-inducing substance (filled circles). but the other had not (open circles).

This design was not intended to diagnose any time-dependent changes in the long-term memories of individual elements. In fact, the design of the experiment presented several opportunities for repairing any decrement that might have occurred to the outcome representations. During training of the second pair of responses or during the outcome devaluation procedure, additional presentations of the outcomes might have served to remind the animals about the specific features of those outcomes. The beneficial effects of outcome presentations as reminder treatments has been well documented in both Pavlovian conditioning and instrumental training procedures (Miller & Springer, 1973, 1974). In summary, these results replicate previous reports that responding is depressed when the outcome used to establish that responding is made undesirable. The specificity of this suppression of instrumental performance indicates that animals learn about the consequences of their actions. Moreover, the present results demonstrate excellent retention of this knowledge over a relatively long period of time. Thus, endurance over time is not a property unique to associations established through Pavlovian conditioning (Gleitman, 1971; Hilgard & Marquis, 1935). Furthermore, this evidence regarding the robustness of long-term associative memory

Instrumental Contingencies

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is in line with other work showing that memories appear fully intact when tests are conducted in the presence of relevant retrieval cues (Miller, Jagielo, & Spear, 1990). b. Simple Nonreinforcernent. A well-established fact about instrumental behavior is that responding declines when rewards are discontinued. Various arguments have been made about the mechanisms underlying extinction of both Pavlovian and instrumental responses (Mackintosh, 1974). One salient distinction between the different accounts is the issue of whether extinction erases the association previously established by training. In the following experiment, I used the outcome devaluation procedure to assess whether simple nonreinforcement of an instrumental response destroys the association between that response and its outcome. Rats were trained to perform two pairs of responses (lever press and chain pull; nose poke and handle pull). One member of each response pair earned pellets and the other earned sucrose. Response-outcome combinations were counterbalanced across subjects. The training procedure for each of the four responses involved a session of CRF training until 30 reinforcers had been earned and then two 20-min sessions of reinforcement on a VI 30-sec schedule. Following this, there were five 20-min sessions of training with each pair of responses. In these sessions, each response was reinforced on an independent VI 60-sec schedule. One pair of responses was then extinguished for twenty 20-min sessions. For one half of the subjects, nose poking and handle pulling were extinguished; for the remaining subjects, lever pressing and chain pulling were extinguished. After extinction, all four responses were retrained in separate sessions with a third outcome, polycose. This training accomplished two goals. First, it raised the level of extinguished responses so that a devaluation effect might be observed. Second, it eliminated the differences in terminal rates across response pairs resulting from the extinction treatment. One of the original outcomes (either pellets or sucrose) was then devalued by pairing it with a toxin. Finally, all animals received two 10-min extinction tests, one with the extinguished responses and one with the nonextinguished responses. Table I outlines the basic design of this experiment. The results of these tests are shown in Fig. 2, separated according to extinction treatment and current value of the outcome. The left-hand side of Fig. 2 shows a standard devaluation effect, at the start of testing, for the nonextinguished responses. The right-hand side of Fig. 2 shows a similar devaluation effect for the extinguished responses. Clearly, simple nonreinforcement of an instrumental response did not remove the association between that response and its original outcome. Thus, not only is a memory of the instrumental outcome retained through extinction (Spear,

Ruth M. Colwill

TABLE I BASICDESIGN OF EXTINCTION EXPERIMENT Training

Extinction

R1-01 R2-02 R3-01 R4-02

R3 R4

Retraining

Devaluation

Test

R1-03 R2-03

01+, 0 2 -

R1 v R2

0 2 + , 01-

R3vR4

R3-03 R4-03

Nore. R I , R2, R3, and R4 are instrumental responses. lever pressing, chain pulling, nose poking, and handle pulling, counterbalanced across animals. 01 and 0 2 denote food pellets and sucrose liquid; 0 3 denotes polycose: + and - indicate the presentation or not of LiCI.

I967), but subjects retain information about which response earned that outcome. However, it is also apparent from Fig. 2 that extinction was not without an effect on responding. Comparison of the extinguished responses found their rate during testing to be significantly lower than that of the nonextinguished responses. Unfortunately, this difference in response rate precludes a meaningful comparison of the magnitude of the devaluation effect for the extinguished and nonextinguished responses. But the NONEXTINGUSHED

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Fig. 2. Sensitivity to outcome devaluation of instrumental responses with a history of extinction (right panel) or not (left panel). One outcome had been paired with a nauseainducing substance (filled circles), and the other had not (open circles).

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implications of these data are clear. They suggest that simple nonreinforcement of a response does not eliminate the sensitivity of that response to a change in the value of its outcome but that it does increase the vulnerability of that response to the suppressive effects of repeated extinction. These findings are similar to those recently reported by Rescorla (1993). However, it is instructive to note two differences between the studies. First, my study used a more extensive period of extinction to take advantage of the fact that considerable spontaneous recovery normally occurs between sessions early in the extinction treatment. Thus, the subjects in the present experiment had frequent opportunities to experience the new consequences associated with responding. Attesting to the effectiveness of this treatment is the finding that spontaneous recovery between sessions was virtually eliminated by the end of extinction training. Yet, despite the greater opportunity t o experience the new consequences of responding, detection of the original R - 0 association was still possible. The second difference between this study and that reported by Rescorla (1993) concerns the conditions under which the responses were trained following initial acquisition. In my study, a response earning pellets was trained concurrently with a response earning sucrose for several sessions. This feature of the present design thus guaranteed equivalent opportunities for each outcome to develop associations with both responses (Colwill & Rescorla, 1985a, Experiment 2). Evidently, the selective effect of outcome devaluation in my study could only have been mediated by the survival through extinction of the unique R - 0 associations, and not the common 0-R associations. c. Negutive Correlntion witti the Outcome. A procedure considered to be superior to simple nonreinforcement in promoting an associative loss is one that arranges for a response to be negatively correlated with its outcome. This assertion is based on three arguments. First, the continued delivery of the outcome during the extinction treatment reduces the contribution of generalization decrement to disruption of responding (Capaldi, 1967; Jenkins, 1965).This is especially relevant if the frequency of outcome presentations remains unchanged between training and extinction. Second, by continuing to deliver the outcome during extinction, any changes in the representation of that outcome (Rescorla, 1973) or in the subject’s motivational state (Spence, 1966) are prevented. Thus, any decrement in performance is more likely to represent a genuine associative loss. Third, by negatively correlating the response and the outcome, the previously positive relation should be replaced by an inhibitory association between the response and the outcome. Thus, whereas simple extinction merely neutralizes the R-0 association, a negative correlation procedure actually

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reverses the polarity of the R-0 connection. This prediction is derived from an influential model of Pavlovian conditioning (Rescorla & Wagner, 1972). Studies examining the reacquisition of extinguished responses provide the strongest support for the belief that a negative correlation procedure is more effective than simple nonreinforcement for extinguishing behavior. In these experiments, reacquisition is routinely found to be considerably slower following noncontingent outcome deliveries or presentations of the outcome on a differential reinforcement of other behaviors (DRO) schedule during the extinction procedure (Pacitti & Smith, 1977; Uhl & Garcia, 1969). To the degree that comparisons across experiments have any validity, it is of interest to note that responding was reacquired more slowly following the negative correlation procedure used in the next experiment than after the simple nonreinforcement procedure used in the preceding experiment. This difference is particularly meaningful: because the reacquisition outcome was different from those used for original training, the difference cannot be attributed to a simple discrimination hypothesis (Jenkins, 1962). In another experiment carried out at the University of Pennsylvania, I examined whether an R - 0 association would remain intact after that response had been negatively correlated with its outcome. The design was similar to that outlined in Table I. Two pairs of responses were trained (lever and chain; nose poke and handle pull). Each response was trained separately, first on a CRF schedule, then on a VI 30-sec schedule for one 20 min session, and finally, on a VI 60-sec schedule for two 20 min sessions; one member of each pair earned food pellets and one member earned sucrose liquid. Assignment of response-outcome pairings was balanced across subjects. Then, one pair of instrumental responses (nose poke and handle pull for one half of the subjects; lever press and chain pull for the remaining subjects) was exposed to negative correlation training. In these sessions, as in training, only one response was available. A variable-time (VT) 60-sec schedule was used to determine outcome availability but delivery of the outcome occurred only after a minimum of 5 sec had elapsed without a response. There were twenty 20-min sessions of this training with each response. To raise the overall response rate of the decremented responses and to minimize the levels differences between the decremented and nondecremented responses introduced by this treatment, all four responses were given six 20-min sessions of training with a third reinforcer, super-strength cherry-flavored pellets, available on a V130-sec schedule. One of the original outcomes was then made aversive through pairings with a toxin. Finally, two 10-min extinction tests were given with each pair of responses.

Instrumental Contingencies

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The results of these tests are summarized in Fig. 3 . The left-hand side of Fig. 3 shows the nondecremented responses separated according to the current value of their outcomes, and documents a robust outcome devaluation effect. The right-hand side of Fig. 3 displays the data from the decremented responses and also reveals a substantial preference for the response trained with the valued outcome. Statistical analyses confirmed the visual impression that the magnitude of the devaluation effect was not reduced by using a negative correlation between a response and its outcome to reduce responding. Similar findings have been reported by Rescorla (1992a), who used a less extensive exposure of a response to either noncontingent or negatively correlated outcomes to produce extinction of that response. Further confirmation of the fact that the R - 0 associations were fully intact following this decremental treatment is indicated by the results of another series of choice tests. In one of these tests, subjects were presented with the two valued responses; in the other test, the two devalued responses were available. In the test with the valued responses, a weakened R-0 relation should be reflected in a preference for the nonextinguished valued response. In the test with the devalued responses, a preferNONDECREMENTED

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Fig. 3. Sensitivity to outcome devaluation of instrumental responses that had been negatively correlated with their training outcomes (right panel) or not (left panel). One outcome had been paired with a nausea-inducing substance (filled circles). but the other had not (open circles).

Ruth M. Colwill

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ence for the extinguished devalued response might appear if a weakened R-0 association lessened the suppressive effect of anticipating an aversive outcome but left intact the contribution of any conditioned reinforcers to performance. But the results shown in Fig. 4 are clear: The animals distributed their responses equally across the two alternatives. This indifference implies an equivalence in the strength of the R - 0 associations. The similarity of these data to those showing matching on concurrent schedules (Baum & Rachlin, 1969; de Villiers, 1977; Herrnstein, 1961; McSweeney , 1975) supports the conclusion that negatively correlating a response with its outcome does not weaken the original R-0 association. d. Conclusions. The results of the three decremental operations reported here suggest that response-outcome associations are relatively permanent. The magnitude of the outcome devaluation effect did not diminish with the passage of time; neither did simple nonreinforcement of a response, or the arrangement of a negative correlation between a response and its outcome, remove the sensitivity of that response to devaluation of its outcome. In fact, in two of these conditions, the outcome devaluation effect was numerically larger for the responses exposed to the destructive treatment. DEVALUED PAIR

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Fig. 4. Preference tests between a response that had been negatively correlated with its outcome (filled circles) and a response that had not (open circles). The training outcome had been devalued for one pair of responses (left panel) but not for the other pair of responses (right panel).

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Two features of the present studies add force to the conclusion that R - 0 associations are fully preserved following these manipulations. First, in each experiment, a within-subjects design was used to probe the status of the R-0 associations. This design guarantees that the potential impact of a decremental treatment on an R - 0 association is not confounded with any effect that treatment might have on the efficacy of the outcome devaluation procedure per se. Second, these studies were concerned with the issue of whether various manipulations altered the strength of R-0 associations. Of the various probes that are available for detecting the presence of R - 0 learning, the outcome devaluation technique has an important advantage in that it has been shown to be sensitive to differences in the strength of R - 0 associations. Colwill and Rescorla (1988b) showed that a response trained with multiple outcomes was more sensitive to the current value of the outcome with which it had a stronger association. In that study, two responses were trained with two outcomes ( 0 1 and 0 2 ) . 01 occurred more often than 0 2 for one response, but less often than 0 2 for the other response. Then, one outcome was devalued. In a subsequent extinction test, performance of a response was found to be more depressed following devaluation of its frequent outcome than of its infrequent outcome. Quite a separate issue raised by the two extinction studies is the question of why responding declines when its outcome is discontinued o r negatively correlated with the response. Obviously, the observed declines in responding are not attributable to concomitant declines in the R - 0 connections. There are at least three separate accounts of extinction that leave original learning intact and appeal to some additional process to explain the decrease in performance. One common belief has been that responding declines because of the increase in some other behavior. This response competition account provides a natural explanation for the more rapid decrease in responding that is sometimes obtained with noncontingent and negative correlation procedures compared to simple extinction ( Johnson, McGlynn, & Topping, 1973; Nevin, 1968; Zeiler, 1971). However, the observation made here and elsewhere (Rescorla, 1992a) that the putative competing response routinely interferes more with performance of the decremented responses than with the nondecremented responses during their retraining in a common context weakens the validity of this account of extinction. The present results are less helpful in distinguishing the merits of two other explanations for why instrumental responding declines in extinction. Several authors have argued that the response becomes associated with the aversive consequences, such as frustration, generated by the omission of an expected reward (Amsel, 1958; Wagner, 1966). Equally plausible,

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Ruth M. Colwill

however, is the possibility that an inhibitory association may develop between the extinction context and the instrumental response, just as an inhibitory S-R association develops between an S- and its nonreinforced response (Colwill, 1991). This view is similar in spirit to a suggestion made by Robbins (1990) that a component of extinction in Pavlovian conditioning involves a decline in attention to the conditioned stimulus (CS). Overall, the pattern of results reported here is explained equally well by both the frustration account and the inhibitory S-R account. The idea that associations remain fully intact once they have been established is by no means a novel one. The study of paired-associate learning led some investigators to the conclusion that initial associations are not lost when new ones are acquired (McGeoch & Irion, 1952). Moreover, Pavlov’s (1927)discovery that extinguished responses recover either spontaneously over time or following the administration of some novel agent alerted him to the prospect that the association between a Pavlovian stimulus and its outcome was not abolished by extinction. This position has recently been reaffirmed by Bouton and his colleagues (Bouton, 1991; Peck & Bouton, 1990) who have argued that contextual stimuli associated with extinction modulate the original excitatory association between the CS and its outcome. In summary, current data provide strong support for the view that R - 0 learning is a pervasive and fundamental component of instrumental learning. It will be important for both neural and connectionist models of the instrumental learning process to recognize the pivotal role played by R-0 associations in goal-directed behavior and to acknowledge the permanent quality of R - 0 associations. The major challenges for future studies of R - 0 learning will be to identify the mechanisms underlying extinction and to specify when the original goals of a response will affect subsequent behavior and when they will remain dormant. B. STIMULUS-OUTCOME LEARNING There remain authors opposed to an interpretation of the effect of outcome devaluation on instrumental performance in terms of R - 0 learning. Their opposition is based on allegiance to an ingenious scheme proposed by Spence (1956) to perserve the fundamental principle of classical S-R theory that instrumental learning involves the acquisition of stimulusresponse connections (Guthrie, 1952;Hull, 1943).The validity of this principle was challenged by evidence that the identity of the outcome could influence instrumental behavior (see Colwill & Rescorla, 1986). However, by allowing stimuli correlated with the response to become Pavlovian signals for the outcome earned by the response, Spence (1956) provided

Instrumental Contingencies

15

a way for instrumental performance to be mediated by an S-R association yet still be sensitive to manipulations involving the outcome. The merits of approaches that combine S-0 and S-R associations, so-called twoprocess theories, were reviewed in some detail by Colwill and Rescorla (1986). As they noted, there is enough versatility in the definition of a stimulus to make it extremely difficult to distinguish empirically between a Pavlovian S - 0 and an instrumental R - 0 account of outcome devaluation effects. Nevertheless, it may yet prove profitable to examine the properties of explicit, discrete discriminative stimuli. Evidence that such stimuli differ from Pavlovian conditioned stimuli greatly reduces the plausibility of assigning Pavlovian properties to more local features of the context accompanying operation of the response manipulandum in order to explain the full range of circumstances under which outcome devaluation effects have been obtained. In what follows, I focus on the issue of whether discriminative stimuli develop associations with their instrumental outcomes. The first section deals with the properties of a stimulus (S+) that signals a response will be reinforced. The second section deals with the properties of a stimulus (S-) that signals that a response will not be reinforced. Evidence is presented that an S+ provides information about the instrumental outcome earned in its presence and that an S- provides information about the identity of the omitted outcome. In each case, the deficiency of a Pavlovian conditioning account of the learning is discussed and the opinion is ventured that one function of simple discriminative stimuli is to modulate the activation threshold of the outcome representation. 1.

S + Procedures

a . Transfer. One technique that has been used to identify an S-0 association measures the effect of a discriminative stimulus on the performance of new instrumental responses. The rationale behind this transfer test is that an animal’s choice between two responses might be biased by a stimulus that signals the availability of the outcome for one of those responses. The use of this transfer procedure is illustrated in a study by Colwill and Rescorla (1988a, Experiment I ) . Rats were allowed to earn a sucrose outcome in the presence of one stimulus (Sl) and a food pellet outcome during another stimulus (S2). They earned one of these outcomes by nose poking and the other outcome by handle pulling. After S+ training, two new responses (lever press and chain pull) were trained in preparation for a transfer test. In this and all subsequent experiments using a transfer test, essentially the same procedure was used to train the responses. One response was trained with the sucrose outcome

16

Ruth M. Colwill

and the other response was trained with the food pellet outcome. The assignment of outcomes to responses was balanced across subjects and orthogonal to the S + treatments. After a session of CRF training and a 20-min session of reinforcement on a VI 30-sec schedule with each response, four 20-min sessions were given with both responses simultaneously available. In these sessions, responding was reinforced on independent VI 60-sec schedules. Finally, the two transfer responses were tested in extinction with occasional presentations of S 1 and S2. It was expected that S1 would promote the transfer response trained with sucrose and that S2 would promote the transfer response trained with pellets. These predictions were confirmed by the results of the transfer test. The effect of a stimulus depended on the identity of the outcome used to train the transfer responses: Each stimulus selectively augmented the response with which it shared an outcome but had no discernible effect on the response trained with a different outcome. The selectivity is important for excluding interpretations of transfer in terms of general effects of S+ training or generalization among the responses that may be legitimately applied to earlier reports of transfer in single-outcome studies (Hearst & Peterson, 1973; Walker, 1942). The inference to be drawn from the present results, however, is clear: An S + provides information about the identity of the outcome earned in its presence. b. Extinction. Evidence that S+s transfer to new responses based on their shared associations with the same outcome leaves unanswered the question of whether the same association mediates their ability to control their originally trained responses. One approach to this problem is to examine the impact on discriminative control of removing the S - 0 association. If performance of an instrumental response in an S+ is a product of the combination of an S - 0 association with either an R - 0 or S-R association, it should be possible to reduce that performance by eliminating the S - 0 association. A procedure that promises to negate an S - 0 connection involves retraining the S+ as a signal for the nonreinforcement of a different response (S- training) trained with the outcome of S + . The evidence for this is presented later in the section on inhibitory S - 0 learning. All that needs to be appreciated at this point is that after receiving training as an S - , a stimulus specifically signals the omission of the instrumental outcome. Colwill(1993b, Experiment 1) inspected discriminative control over the original response after an S + had been trained as an S- for a different response. The design of that study is shown in Table 11. Rats were first trained with two S + s (noise and light), two responses (lever and chain), and two outcomes (sucrose and pellets). Each stimulus was trained as a signal for a different response-outcome association: S 1 signaled that R l

17

Instrumental Contingencies

TABLE I1 BASICDESIGNOF S-0 EXTINCTION EXPERIMENT S + training

S - training

Test

R3-01, SI: R 3 - , S2: R 3 S1: RI-01

SI: RI S2: R2

S2: R2-02 R3-02. SI: R 3 - , S2: R3-

Note. SI and S2 are instrumental discriminative stimuli, noise, and light. R I and R2 are instrumental responses, lever pressing, and chain pulling, counterbalanced across animals. R 3 represents nose poking; 01 and 0 2 denote food pellets and sucrose liquid; - indicates nonreinforcement.

would be followed by 01, and S2 signaled that R2 would be followed by 0 2 . Subjects were then trained to nose poke; one half of the subjects earned food pellets and one half earned sucrose liquid. After this, both S + were trained as signals that nose poke responses would not be reinforced in their presence. In this way, one S + was trained as an S- with the same outcome, and one S + was trained as an S- with a different outcome. This design ensured equivalent exposure to both S + s and thus controlled for any general disruptive effects of S- training. However, only S- training with the same outcome should extinguish the original s-0 association. Both stimuli were then tested in extinction with their original responses. Figure 5 shows performance of a response during the stimulus with which it was trained and during the ITI. Performance is shown separately for the stimulus trained as an S- with the same outcome (left panel) and for the stimulus trained as an S- with a different outcome (right panel). The data are unambiguous: Performance of a response was substantially reduced in the presence of its S + following a treatment designed to remove the S - 0 association. In fact, this treatment completely undermined the ability of the S + to produce a significant elevation of responding relative to the intertnal interval (ITI) rate. These results suggest that an intact S - 0 association is crucial to the ability of an S+ to produce performance of its own instrumental response. c . Nature of the S - 0 Association. Adherents of two-process theories would find neither the conclusion that an S+ encodes the identity of the instrumental outcome nor the conclusion that an intact S - 0 association is necessary for an S + to produce its response particularly remarkable. In the first place, there is good evidence that Pavlovian cues develop associations with representations that are rich in detail about their conse-

Ruth M. Colwill

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quences (Colwill & Motzkin, 1993; Rescorla, 1980). In the second place, eliminating the S - 0 association would be expected to deprive the S + of the ability to produce either the motivational (Rescorla & Solomon, 1967) or stimulus support (Trapold & Overmier, 1972)for responding. However, attempts to show empirically that a discriminative stimulus is in essence a Pavlovian stimulus have yielded somewhat conflicting results (Colwill & Rescorla, 1986). The work that I describe next illustrates the difficulties in deciding this issue. One strategy that has frequently been used to analyze S + s is to examine the degree to which their effects can be mimicked by Pavlovian CS+s. To the degree that the two types of stimuli are interchangeable, we have evidence consistent with the fact that they share a common associative structure. There is general agreement that the transfer of CS+s to instrumental responses is outcome specific (Colwill & Motzkin, 1993; Colwill & Rescorla, 1988a; Kruse, Overmier, Konz, & Rokke, 1983). However, other features of the pattern of transfer with C S + s do not resemble those obtained with S + s . Colwill and Motzkin (1993) gave rats training with two Pavlovian cues (a noise and a light) and two outcomes (food pellets and sucrose liquid). Each CS was uniquely paired with one of the outcomes. Then, two different responses were given standard training in

Instrumental Contingencies

19

preparation for a transfer test. One of these responses earned food pellets and the other response earned sucrose liquid. The results of the transfer test showed that the effect of a CS on a response was outcome specific: same responses were significantly more frequent than different responses. In this regard, the transfer of both an S + and a CS+ seems to be mediated by shared association with a common outcome. However, a potentially important difference appears if these stimulus effects are compared with the level of responding during the ITI. Whereas the effect of an S + is to increase performance of the same response and leave unaffected performance of the different response, a CS+ has no impact on the same response but depresses performance of the different response. Colwill and Rescorla (1988a) discussed at length the constraints imposed by this pattern of results for answering the question of whether an instrumental S+ is simply a Pavlovian CS +. One major concern centered on the potential for differential contributions of a competing response during CS+ and S+ presentations that might have served to reduce the overall level of transfer responses during CS+. Whereas the CS+ would continue to evoke its Pavlovian response (e.g., approaching the food magazine) during testing, the absence of the manipulandum used to train the S + prevented occurrences of a behavior that might have interfered with performance of the transfer responses. Thus, a CS+ and an S+ might differ only in the degree to which t h e y elicit competing responses during testing. This argument may turn out to be intractable to empirical evaluation. To control for differences in the likelihood of competing responses, transfer might be measured in the presence of either a compound of an S + for 01 and a CS+ for 0 2 , or with a stimulus simultaneously trained as an S + for 0 1 and a CS+ for 0 2 . According to a competing response argument, the promotion of the same response by S + would be reduced. But if the original patterns of transfer represent genuine differences in the action of CS+ and S + , any combination of an S + for 01 with a CS+ for 0 2 would lead to cancellation of their independent effects on a transfer response trained with 01. Thus, promotion of the same response should be reduced. An alternative strategy that may prove more promising in illuminating the relationship between an S + and a CS+ is to inspect the range of operations that lead to extinction of the instrumental S-0 association. If treatments that are known to extinguish Pavlovian associations are found to be ineffective in eliminating the ability of an S + to control its response, we would have some justification for making the claim that S+ is fundamentally different from C S + . It is well known that nonreinforced presentations of a CS+ lead to a decline in conditioned responding (Annau & Kamin, 1961; Pavlov, 1927)and that a stimulus that is negatively correlated

Ruth M. Colwill

20

with an outcome develops the ability to suppress responding to a Pavlovian excitor (Rescorla, 1969) or to elicit a withdrawal response (Hearst & Franklin, 1977). Yet, we have found neither of these operations to reduce the ability of an S+ to produce its instrumental response. For example, Colwill (1993b, Experiment 2) gave rats training identical to that used t o produce the data in Fig. 5 with the following exception. Instead of training the former S+ as S- for another response, the S + s were simply negatively correlated with one of the outcomes used for S + training. The results of testing those stimuli with their original responses are shown in Fig. 6. Responding is separated according to whether S + was negatively correlated with the same outcome used for its training (left panel) or with a different outcome (right panel). It is clear that there was no impairment in the ability of an S + to produce its response despite a history of being negatively correlated with its original outcome. This finding indicates that the disruption of s+ control shown in Fig. 5 was not mediated by the negative correlation between the stimulus and the outcome intrinsic to S- training. Corroboration that this procedure of negatively correlating a stimulus and its outcome undermines the 0-0

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Instrumental Contingencies

21

transfer of Pavlovian CS+ s to instrumental responses would obviously strengthen the argument for a separation in the associative structures of S + and CS+.

2. S - Procedures u . Transfer. Traditional explanations for why animals do not respond in the presence of an S- were constrained by the belief that the only association learned in instrumental training was a stimulus-response connection. Because this S-R association was thought to be strengthened only when a reinforcer followed the response, accounts of the effects of an S- admitted just two possibilities. An S- failed to evoke the instrumental response either because it lacked an association with that response (Capaldi, 1970; Thorndike, 1911) or because it elicited an incompatible response (Guthrie, 1952; Hull, 1943; Thorndike, 1932). Criticism of classical S-R theory and the subsequent evolution of twoprocess theories permitted realization of a third possibility. As more theorists came to adopt the position that instrumental discriminative stimuli developed Pavlovian associations with the outcome, it seemed reasonable enough to suppose that the action of an S- was to be understood in terms of its Pavlovian analogue, conditioned inhibition (Rescorla & Solomon, 1967; Rilling, 1977). A principal characteristic of this approach was to regard the effects of omitting different outcomes as distinguishable only in terms of their global motivational valence (Dickinson, 1980; Dickinson & Dearing, 1979; Dickinson & Pearce, 1977). Thus, the omission of different events within the same affective category (e.g., food pellets and sucrose liquid) would be expected to have identical consequences for behavior. Support for this view has come from studies showing that Pavlovian inhibitors transfer to stimuli trained with different outcomes belonging to the same motivational class (Nieto, 1984; Pearce, Montgomery, & Dickinson, 1981). However, the evidence that animals encode very detailed representations of those events when they occur offers more than adequate justification for questioning the suitability of accounts that ignore the potential for learning about the detailed identity of the omitted outcome. To explore the issue of whether specific information about the omitted outcome is encoded by an S- , I examined the role of outcome identity in the transfer of S-s to new instrumental responses. If an S- signals that a particular outcome is locally unavailable, performance of any response trained with that outcome ought to be suppressed by that S-. In one study (Colwill, 1991, Experiment I ) , two stimuli were trained as signals for the nonreinforcement of different instrumental responses. S 1 signaled that Rl would

22

Ruth M. Colwill

not be followed by pellets, and S2 signaled that R2 would not be followed by sucrose. Then, these stimuli were presented with two other responses, R3 trained with pellets and R4 trained with sucrose. It was expected that each S- would suppress the response trained with the outcome whose omission was signaled by the S - . In other words, Sl should suppress performance of R3, and S2 should suppress performance of R4. The data from the transfer test are shown in Fig. 7. As predicted, an S- better suppressed the response trained with the outcome whose omission S- had been trained to predict. The outcome-dependent component of this transfer indicates that the subjects had learned which particular outcome would be omitted during that S - . Figure 7 also shows that an S- suppressed the different response relative to the ITI. This nonspecific transfer may be mediated by the shared features of omitted outcomes (e.g., frustration), by a competing response as anticipated by some of the traditional accounts of extinction, or by nonassociative factors (Colwill, 1991). In any event, the outcome-dependent transfer of S-s points to the presence of an inhibitory association between an S- and the instrumental outcome (S 30).At least two predictions that follow from this viewpoint have been confirmed empirically. First, Bonardi (1989) and Colwill(l991)

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Instrumental Contingencies

23

found that if a previously omitted outcome were subsequently made contingent on a response in the presence of the S -, acquisition of S control was slower than if some other outcome were used. Second, Colwill(1991) identified a condition where knowledge that an event would be omitted led to an elevation of responding. Performance of a response that had earned two different outcomes, one of which was subsequently devalued, was increased during presentations of an S- for the devalued outcome.

+

b. Characteristics of Inhibitory S-0 Associations. Previous reports describing the outcome specificity of instrumental inhibitors have been based on investigations of stimuli that signal the omission of the responsecontingent outcome. However, two reasons make it of interest to examine the transfer properties of stimuli that signal reductions in the frequency of the instrumental outcome. First, it offers an opportunity to appraise the details of the subject’s encoding of the outcome. By varying the reinforcement history of the transfer responses used in testing, it becomes possible to separate learning only about the identity of the outcome from learning about the actual frequency of that outcome. Second, the role of Pavlovian conditioned inhibition can be evaluated by comparing the transfer of stimuli that signal fewer response-contingent reinforcers with the transfer of stimuli that signal the same reduction in outcome frequency but whose reinforcers are delivered noncontingently . i. Precision of outcome encoding. If presentations of a stimulus accompany a reduction in the density of the VI reinforcement schedule associated with an instrumental response, there are at least two pieces of information that such a stimulus could come to convey. On the one hand, the stimulus might simply signal a decrease in the availability of the outcome; the magnitude of that decrease would be determined by the difference between the rate of reinforcement during the stimulus and in its absence. On the other hand, the stimulus might signal the absolute frequency of the outcome. In this case, the subject might compare the value of the VI schedule pertaining to that stimulus with the background rate, and reduce its response during the stimulus only when the stimulus signals a rate reduction. These two accounts can be distinguished by examining transfer to responses trained with different densities of reinforcement. If the rate of reinforcement for the transfer response is greater than that signaled by the S - , both accounts predict that responding will be depressed. However, if the transfer response has been trained on the same V I schedule as that associated with the S - , only the first account predicts that responding will be suppressed. In fact, based on transfer studies of S+s, the view that the local reinforcement rate is encoded by a stimulus anticipates an increase in responding during the stimulus.

Ruth M. Colwill

24

The next two experiments investigated the transfer properties of stimuli that signaled a reduction in the relative frequency of reinforcement. The first study verified that such stimuli would differentially suppress transfer responses trained on a rich reinforcement schedule. The second study examined the transfer of such stimuli to responses trained on a lean reinforcement schedule. In both studies, two stimuli (tone and steady light) were trained each with a different response (nose poke or handle pull) and a different outcome (food pellets or sucrose liquid). Each response was reinforced on a VI 240-sec schedule during 30-sec presentations of its stimulus, but on a VI 30-sec schedule during the 30-sec ITI. The basic design of each experiment is shown in Table 111. Both experiments tested the effects of these stimuli on two different responses (lever and chain), one trained with pellets and one trained with sucrose. In one study, the transfer responses were trained on a V160-sec schedule; in the other study, the transfer responses were trained on a VI 240-sec schedule. The standard procedure was used to train the transfer responses on a VI 60-sec schedule. The procedure for training the transfer responses on a VI 240-sec schedule deviated from that standard in the following ways. First, a 10% polycose solution was used to reinforce lever and chain responses during initial CRF training and for two 20-min sessions of V1 60-sec training. Each response was then trained on a VI 240-sec schedule for one 20-min session with its unique outcome (i.e., pellets for one response and sucrose for the other response). Eight 20-min sessions of V I 240-sec training were then given with both responses available within a session and each reinforced with its unique outcome. In both studies, the effect of the S-s on these responses was tested in extinction. In the

TABLE 111 BASICDESIGN OF TRANSFER EXPERIMENTS WITH SIGNALS RATE FOR REDUCTIONIN REINFORCEMENT Training

Transfer

Test

RI-01 (VI 30), SI: R1-01 (VI 240) R2-02 (VI 30), S2: R2-02 (VI 240)

R3-01 (VI 60) R4-02 (V160)

SI: R3 v R4 S2: R3 v R4

RI-01 (VI 30), SI: RI-01 (VI 240) R2-02 (VI 30). S2: K2-02 ( V I 240)

R3-01 (VI 240) R4-02 (VI 240)

S1: R3 v R4 S2: R3 v R4

Note. SI and S2 are instrumental discriminative stimuli, tone. and light. R I . R2, R3. and R4 are instrumental responses. lever pressing, chain pulling. nose poking. and handle pulling. counterbalanced across animals. 01 and 0 2 denote food pellets and sucrose liquid; VI indicates variable interval reinforcement schedule.

25

Instrumental Contingencies

extinction test, both lever and chain were available and eight presentations each of the tone and light stimuli were delivered periodically. The test results from the first study are shown in Fig. 8. In the presence of a stimulus that had signaled a shift from a rich VI to a lean VI reinforcement schedule, performance of a response trained on a rich VI schedule with the same outcome was suppressed. That suppression was evident when same responses were compared both with different responses and with the IT1 rate. The outcome-specific component of this transfer replicates the pattern of transfer observed with an S- conventionally trained to signal the omission of a response-contingent outcome. In this experiment, however, there was no evidence of any general disruption of responding by a signal for a reduction in reinforcement frequency: Different responses did not vary from the IT1 rate. The test data from the second study in which the transfer responses had been trained on a lean VI schedule followed a pattern more like that shown in Fig. 7. The immediate effect of a stimulus that signaled a decrease in the frequency of reinforcement was to suppress another response trained with the same outcome slightly more than a response trained with a different outcome (4.6 and 5.6 responses per min, respectively, on the

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26

Ruth M. Colwill

first two test trials). However, performance of both responses during the stimulus presentations was reduced relative to their IT1 rate (6.5 responses per min on the first two test trials), although that difference was only significant for the same response. These two studies reveal substantial consistency in the pattern of transfer to other responses of a stimulus trained as a signal for a reduction in the rate at which a response will be reinforced. Such stimuli suppressed preferentially other responses trained with the same outcome regardless of the rate at which they had earned that outcome. This result implies that an S- develops a simple inhibitory association with the instrumental outcome. Experiments analogous to the ones reported here have been carried out on Pavlovian conditioned inhibitors (CI) that signal a reduction in the magnitude of an unconditioned stimulus associated with a CS+ . Specifically, those studies have been concerned with the associative connections of a stimulus followed by a mild shock unconditioned stimulus (US) in the presence of a cue that separately signals a strong shock US. There is universal agreement that such a stimulus reduces responding to another signal trained with the strong shock US. However, the results of testing that stimulus with a signal trained with the weak shock US have been more controversial. Whereas some investigators have found no transfer of inhibition (Cotton, Goodall, & Mackintosh, 1982; Mackintosh & Cotton, 19851, others have obtained successful transfer (Wagner, Mazur, Donegan, & Pfautz, 1980). The potential discrepancy between these results and those we have found for instrumental S-s may reflect a fundamental difference in the associative structures of instrumental and Pavlovian inhibitors. On the other hand, it may be the case that, unlike variations in the intensity of an event, manipulations of the frequency of an event are not encoded as sensory features of the outcome representation. ii. Role of Pavlovian conditioned inhibition. Traditionally, support for an explanation of the suppressive effects of an S- in terms of Pavlovian conditioned inhibition has been drawn from studies showing that Pavlovian inhibitors suppress instrumentally trained responses (Rescorla & LoLordo, 1965; Weisman & Litner, 1969).Arguments against this view have made much of two characteristic differences in the suppression produced by a Pavlovian CI and that produced by an instrumental S - . First, as previously noted, Pavlovian CIS appear to transfer across signals trained with different outcomes belonging to the same motivational class (Nieto, 1984; Pearce et al., 1981). Thus, Pavlovian CIS fail to demonstrate the kind of outcome specificity in their transfer that has been obtained with instrumental S-s. Second, direct comparisons of the magnitude of CI and S- have typically revealed that a conditioned inhibitor is less effective

Instrumental Contingencies

21

and may sometimes be completely ineffective in reducing instrumental responding (Bonardi, 1988; Gutman & Maier, 1978). Neither of these arguments is especially convincing. In the first place, Kruse et al., (1983) detected an outcome-specific element in the effect of Pavlovian CIS on instrumental performance. In the second place, Colwill(l991) has argued that comparisons of the magnitude of transfer are biased in favor of an S - . Because an S- develops an inhibitory connection with its original response, generalization from that association can contribute to its ability to suppress another response. That source of generalization is not available to a Pavlovian CI. The following experiment used an alternative strategy to evaluate the role of Pavlovian conditioned inhibition to the suppressive effects of an S- . The intention was to dissociate the Pavlovian from the instrumental contingencies embedded in training a stimulus to signal a reduction in rate of reinforcement. To this end, a stimulus was correlated with a reduction in the frequency of an outcome by arranging for responding to be reinforced during the IT1 but not during presentations of the stimulus. When the stimulus was presented, the outcome was delivered noncontingently but at a substantially lower rate than its frequency during the ITI. If Pavlovian conditioned inhibition underlies the transfer obtained with an instrumental S - , a stimulus trained in this procedure should demonstrate outcome-dependent suppression of another instrumental response. The design of this study was modeled after that used to produce the data shown in Fig. 8. Two stimuli (tone and steady light) were trained each with a different response (nose poke or handle pull) and a different outcome (food pellets or sucrose liquid). Each response earned its outcome on a VI 30-sec schedule during the ITI. Responses went unrewarded during stimulus presentations but outcomes were delivered on a VT 240-sec schedule. Thus, the stimulus was correlated with the same reduction in density of outcome deliveries as the S- trained for the test shown in Fig. 8. All subjects were then trained to lever press and chain pull; one response earned pellets and the other earned sucrose. Preparation of these responses for a transfer test followed the standard training procedure. The results of that test are shown in Fig. 9. The pattern of responding in the presence of the stimuli bears no resemblance to the data obtained with explicitly trained S-s. Rather than depressing the response trained with the same outcome, a stimulus left that response unaffected and instead suppressed performance of the different response, at least at the outset of testing. This reversal offers persuasive evidence that the present training procedure did not simply generate a weaker form of the learning produced by explicit S- training. In fact, the results plotted in Fig. 9 most resemble those obtained with the transfer of Pavlovian CS+s. That

Ruth M. Colwill

28

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similarity suggests that the stimuli in the present experiment developed simple Pavlovian associations with their noncontingent outcomes. These results imply that an instrumental S- is not merely a more powerful duplicate of a Pavlovian CI but rather possesses a unique associative structure that cannot be reduced to that produced by Pavlovian contingencies. 3 . Conclusion

The transfer technique has provided compelling evidence that an S+ and an S- provide information about the identity of the outcome whose occurrence or nonoccurrence is signaled by their respective presentations. This information allows these stimuli to control the performance of other responses trained with those outcomes. In neither case does it appear reasonable to attribute that learning to the operation of Pavlovian conditioning processes. Instead, it seems more appropriate to appeal to the idea that instrumental cues modulate the threshold for activation of the outcome representation. Whereas an S + reduces this threshold, thus making it easier to excite the outcome representation, an S- raises that threshold, thus making it more difficult to excite the outcome representation.

Instrumental Contingencies

29

In the case of an S + , this mechanism appears wholly satisfactory in accounting for the occurrence of the original response.

C. S-R ASSOCIATIONS One of the most pervasive themes in discussions of instrumental behavior has been that discriminative stimuli become associated with their instrumental responses. The function usually attributed to this learned association is an evocative one; in other words, the discriminative stimulus is thought to trigger execution of its instrumental response. Although this view has been favorably represented in theoretical accounts of instrurnental learning, there has been little justification for it at an empirical level (Colwill & Rescorla, 1986). An alternative idea that has received rather less attention is that the function of an S-R association is to narrow the pool of response options to those that are immediately available. Execution of a specific response whose representation was retrieved by its discriminative stimulus would then depend on the results of evaluating the consequences associated with making that response. In the following sections, 1 comment first on the evidence that animals Learn about the relation between a stimulus and its reinforced response. Then, I discuss the implications of studies showing that an S- demonstrates both a degree of response specificity and, under some circumstances, insensitivity to the identity of the outcome associated with its nonreinforced response. 1 . Association between

S+ and R

a . Persistence of Behavior. Attention has often been drawn to circumstances under which behavior persists despite a reduction in the quality of its consequent outcome. Even when rejection of a devalued outcome in a consumption test is complete, the rates of devalued responses remain at a level substantially above zero. An example of this residual responding is shown in Fig. 1. Observations of residual responding have provided the strongest support for an evocative function of an S-R association. Consistent with this interpretation is the fact that more residual responding is obtained in single-response tests than in choice tests (Colwill & Rescorla, 1986). However, Colwill and Rescorla (1986) advised caution in attributing residual behavior to an evocative function of an S-R association, warning that other explanations of residual performance were equally feasible. For instance, it may be that the standard devaluation procedure leaves the functional reinlorcer with some residual value. In this way, an R-0 association could then support the continued performance of the devalued re-

30

Ruth M. Colwill

sponse. Evidence consistent with this alternative was recently reported by Colwill and Rescorla (1990a, Experiment 2). They employed a reward that was administered directly into the animal’s mouth during both instrumental training and outcome devaluation. Under those conditions, residual responding after outcome devaluation was negligible. Direct administration of a food outcome into the subject’s mouth has two advantages over the more commonly used procedure of delivering the outcome to a food magazine. First, the immediacy of delivery inherent in direct administration of the outcome reduces the potential for events to intervene between execution of the response and consumption of the outcome. In the conventional outcome administration procedure, such intervening events might retain their value after devaluation of the food outcome and thus contribute to residual performance of the devalued response. Second, direct administration of the outcome into an animal’s mouth may improve the efficacy of the devaluation treatment by guaranteeing exposure to the outcome on each conditioning trial. In the conventional procedure, a subject may refuse to consume the outcome long before its value has been fully eliminated, and thus the functional outcome may be left with the capacity to sustain residual performance. For whatever reason that direct administration of the outcome yields a superior outcome devaluation effect, it raises a serious concern about the use of residual responding as a basis for identifying an evocative S-R connection.

6. Mediation qf Transfer. Colwill and Rescorla (1986) were more sympathetic toward the possibility that S-R associations are used to select the various response options whose appropriateness can then be determined by evaluation of their consequences, activated through R - 0 associations. This hybrid model has the advantage of accounting for the sensitivity of responses to outcome revaluation. Moreover, it can accommodate Colwill’s ( 1993b) finding that discriminative control is eliminated by training S + as an S- with the same outcome; the consequence of S- training would be to interfere with activation of the outcome representation by the response. That some form of S-R learning occurs has been inferred from the observation that animals can solve certain kinds of instrumental discriminations (Colwill & Rescorla, 1986; Mackintosh, 1983). What has been lacking, however, is any direct evidence for an S-R association in instrumental learning. The following study was undertaken in an attempt to remedy that situation. It asked the question of whether transfer of a discriminative stimulus to a new response could be mediated by the activation of an outcome through a chain of S-R and R-0 associations. Rats were trained with two discriminative stimuli, two responses, and one

Instrumental Contingencies

31

outcome (i.e., S1: RI-01 and S2: R2-01). For half the animals, handle pulling earned pellets during a light and nose poking earned pellets during a tone; these stimulus-response pairs were switched for the remaining animals. Then, two new responses (lever press and chain pull) were trained, one with sucrose (R3-02) and one with polycose (R4-03). During the training of these responses, neither the light nor the tone were presented. Similar training was then conducted with the nose poke and handle pull responses; each response was separately trained, one with sucrose (Rl-02) and one with polycose (R2-03). Finally, the lever and chain were made available and tested with occasional presentations of the light and tone. What was of interest was whether a stimulus would differentially affect performance of the transfer response trained with the same outcome subsequently associated with the stimulus’ original response. In other words, would S1 selectively affect R3 and S2 selectively affect R4. The results of this study are shown in Fig. 10. Over the course of testing, a differential effect of the stimuli on responding did emerge; the same response was significantly depressed relative to the different response. That is to say, a stimulus whose response had been separately paired with polycose suppressed performance of another response also trained with polycose. Suppression was also evident when a comparison was made

0 c 3

.-C

E L

W

a ln ln

K

0

a

F!

1

2

3

4

Blocks of two trials

Fig. 10. Effect of a discriminative stimulus ( S + ) on a response trained with either the same outcome (filled circles) or a different outcome (open circles) that had been paired with the response of the S + after the discrimination training.

32

Ruth M. Colwill

with the IT1 response rate (3.4 responses per min): Over the entire session, a stimulus suppressed the same response but had no effect on the different response. The selectivity of this effect provides direct evidence of an association between a discriminative stimulus and its original response. The finding that a stimulus suppressed rather than promoted the same response in the transfer test has two important implications for analyses of instrumental learning. First, it is relevant to a decision regarding the relationship between Pavlovian CS+s and instrumental S+s. It is clear that activation of the outcome representation by a response is not adequate to support transfer. That finding suggests a qualitative difference in the way in which discriminative stimuli and instrumental responses operate on the outcome representation. What appears to be important for transfer is not simple excitation or activation of the outcome representation; rather, to promote a response, a stimulus must have the power to lower the threshold for activation of the outcome representation associated with that response. In view of the overwhelming evidence that Pavlovian CS+s and instrumental responses develop identical associations with their outcomes (see Colwill& Rescorla, 1986; Mackintosh, 1983), it seems doubtful that there can be any disagreement about the nonequivalence of Pavlovian CSs and instrumental discriminative stimuli. Second, the present results suggest that, at least under some circumstances, different responses trained with the same outcome may interfere more with each other than with responses trained with different outcomes. This observation has obvious relevance to studies of performance on concurrent schedules (Herrnstein, 1961). It may also have some relevance for the analysis of instrumental contingency effects in which additional noncontingent presentations of an outcome reduce performance of a response trained with that outcome but not that of a response trained with a different outcome (see Colwill & Rescorla, 1986). To the degree that the noncontingent presentations of an outcome adventitiously reinforce another behavior, the selective depression of the same response may, in part, be a product of an outcome-dependent response competition mechanism, and not entirely due to a change in the R-0 association. Finally, it is worth noting one feature of the present study that may have especially encouraged the development of an S-R association. With the use of a single outcome to establish initial discrimination learning, the conditions may not have been optimal for producing learning about that outcome. It will be important to vary the circumstances under which initial training is carried out to verify the generality of the conclusions that may be drawn from this study with respect to the development of S-R associations in the normal course of instrumental learning.

Instrumental Contingencies

33

2 . Inhibitory S-R Associations The quest for evidence of S-R learning has proven far more profitable in situations where stimuli signal that responses will not be followed by their rewarding outcomes. Application of three different techniques has generated evidence showing that an S- exhibits some response specificity and that in suppressing its response an S- appears insensitive to the identity of the outcome used to train that response. Such findings suggest that an S- reduces performance of its original response by specifically suppressing activation of the representation of that response. a . Conflicting Outcomes in Acquisition. One strategy that has been particularly useful in disclosing the nature of the associations formed in Pavlovian conditioning has been to manipulate the conditions under which that learning is asked to take place. If the consistency between a signal and its outcome is varied systematically, it is possible to draw inferences about the identity of the outcome elements that participate in the association with the signal by examining the rate at which conditioning to the stimulus occurs. If learning is affected by changing a feature from one trial to the next, it suggests that learning about that feature takes place. An elegant illustration of the use of this technique was reported by Rescorla (1980). Using an autoshaping procedure with pigeons, he found superior second-order conditioning to a stimulus that was always followed by the same outcome compared with a stimulus that was followed by two different outcomes. In applying this logic to an analysis of an S - , we predicted that if an S- acts directly on its instrumental response, acquisition of that S i R connection should be unaffected by the identity of the outcome that is omitted. To test this idea, rats were given training with four different discriminative stimuli (noise, tone, a steady light, and an overhead flashing light) designed to establish them concurrently as S + s for one response and S-s for another response. Two stimuli signaled that nose poke responses (Rl) would be followed by food pellets and the other two stimuli signaled that nose pokes would be followed by sucrose liquid. One of the pellet stimuli and one of the sucrose stimuli was also trained as a signal that another response, R2 (either lever or chain) would not be followed by pellets; the remaining two stimuli were trained as S-s for a third response, R3 (either chain or lever) trained with the sucrose outcome. The stimulus presentations were 30 sec long separated by a mean IT1 of 30 sec duration. Responses were reinforced during the IT1 on VI 30-sec schedules. Table IV illustrates the basic design of this experiment. The acquisition rates of S + and S- control are shown in Fig. 1 I . There was no evidence of differential suppression of the nonreinforced responses

Ruth M. Colwill

34

TABLE IV

BASICDESIGNOF OUTCOME INFLUENCE ON CONCURRENT S+/STRAINING EXPERIMENT S + /S - training R2-01, R2-01. R3-02, R3-02,

S1: R1-01, S2: R1-02, 53: RI-02, S4: RI-01.

R2R2R3R3-

N o t e . KI denotes the nose poke response; R2 and R3 are instrumental responses. lever press, and chain pull. counterbalanced across animals. 01 and 0 2 denote food pellets and sucrose liquid: - indicates nonreinforcement; S I , S2. S3. and S4 are discriminative stimuli, steady light. flashing light, tone, and noise.

even though the omitted outcome for one response was earned concurrently by another response. But manipulation of the identity relation between the earned and the omitted outcome was not without effect on behavior. Acquisition of the reinforced response was at a disadvantage when the stimulus concurrently signaled the omission of its outcome for 1.o

.$

e

0.8

0-0

SAME

0-0

DlFF

C 0

o.2_

1

2

3

4

5

6

Blocks of three sessions

Fig. 11. Acquisition of discriminative control by a stimulus trained concurrently as a signal for the reinforcement of one response (S + ) and for the reinforcement of a different response (S - ). The outcome following the reinforced response was either the same as (filled circles) or different from (open circles) the outcome withheld for the nonreinforced response.

35

Instrumental Contingencies

a different response. This finding extends the results depicted in Fig. 5, showing that prior S + control was undermined by subsequent S- training with the same outcome. This experiment provides evidence that the operation of nonreinforcing a response during a stimulus has two consequences. First, it strengthens an inhibitory association between that stimulus and the response. Second, it produces an inhibitory connection between that stimulus and the representation of the outcome that was forfeited. This S l O association is antagonistic to that generated between an S + and the earned outcome. b. Summation and Retardation. Another technique for determining the identity of the elements participating in an association with a stimulus is to combine different stimuli that vary in the degree to which they have overlapping associates (Miller & Price, 1974). The general idea is that summation of two stimuli signaling the same information will be different from that of two stimuli signaling different information. In applying this technique to the detection of associations between a stimulus and its nonreinforced response, Jennifer Richeson and I expected that a response would be depressed more in the presence of a compound containing its original S- and another stimulus also trained as an S- for that response than in a compound containing its original S - and an S- for a different response. Table V illustrates the basic design of our experiment to assess the presence of inhibitory connections between an S- and its nonreinforced response. A light was established as a signal for the nonreinforcement of two responses (lever and chain) that were otherwise reinforced with the same outcome (e.g., pellets). Each of these responses was also nonreinforced during another stimulus; one response (R1) was nonreinforced

TABLE V

BASICDESIGNOF ~

s-

SUMMATION

EXPERIMENT

~

S - training

R I - 0 1 , N: RI R 2 - 0 1 . T: R2 L: R 1 - , R 2 R 3 - 0 2 , N : R3 R 4 - 0 2 . T: R4L: R3 - R4-

.

Test

Predictions

LN: R 1 - 0 1 , R2-01 LT: R I - 0 1 , R2-01

( L N : R1 < R2) (LT: R1 1 R2)

LN: R3-02, R4-02

(LN: R3 < R4) (LT: R3 > R4)

LT: R3-02, R4-02

Nore. R I . R2. R3. and R 4 are instrumental responses, lever pressing, chain pulling. nose poking. and handle pulling. counterbalanced across animals. 01 and 0 2 denote food pellets and sucrose liquid; - indicates nonreinforcement: L. T. and N are discriminative \timuli. light. tone. and noise, respectively.

36

Ruth M. Colwill

during a noise and the other response (R2) was nonreinforced during a tone. Parallel training of these stimuli with two other responses (nose poke and handle pull) and a different outcome (e.g., sucrose) was also conducted. For all four responses, outcomes were earned on a VI 30-sec schedule in the absence of stimulus presentations. Finally, summation tests were given with each pair of responses (lever and chain in the first test series; nose poke and handle pull in the second test series) and occasional presentations of a light-noise (LN) compound and a light-tone compound (LT). In this way, the elements of one compound stimulus (LN) had separately signaled the nonreinforcement of one response (RI or R3), and the elements of the other compound stimulus had signaled the nonreinforcement of the other response (R2 or R4). We were concerned that several factors might obscure our ability to detect differential summation. For instance, both nonspecific suppression and suppression produced by the inhibitory S-0 association observed in transfer tests with S-s might contribute to a floor effect. Consequently, responses were rewarded on a VI 30-sec schedule with their original training outcomes in the presence of the compound stimuli during summation testing. Any difference in the rate at which the compounds developed control over R1 and R2 (or R3 and R4) could not be attributed to outcome associations because the relevant comparison was between responses trained with the same outcome. Consequently, if acquisition of RI were slower than R2 during the LN compound, and acquisition of R2 were slower than R1 during the LT compound, we would have evidence for the presence of specific S {R associations. These predictions were confirmed during testing of R1 and R2. Although performance of both responses was negligible during the initial compound stimulus presentations, a significant difference emerged in that the probability of a response was reduced when both stimulus elements signaled its nonreinforcement (3.6 and 4 . 4 responses per min for the same and different responses, respectively). A similar pattern emerged during testing of R3 and R4, but the difference was marginally nonsignificant. These results provide additional support for the conclusion that an inhibitory association develops between an S- and its nonreinforced response. c . Resistance to Manipulations of the S - 0 Relation. The third result that implicates the presence of S {R associations is the finding that an S- will continue to suppress its original response following retraining as a signal for the reinforcement of another response with the same outcome. Thus, even though retraining the S- as an S+ results in the stimulus signaling the availability of the instrumental outcome, that S- nevertheless continues to inhibit performance of the response whose nonreinforcement

Instrumental Contingencies

31

it has predicted in the past (Colwill, 1991, 1993a). This finding is illustrated by the following experiment reported by Colwill (1993a). Two stimuli (a noise and a light) were initially trained as S-s for two different responses (nose poke and handle pull). Each S- signaled that one response would not be followed by pellets and that the other response would not be followed by sucrose. Then, both S-s were trained as signals for the reinforcement of a third response (displacement of a joystick, R3). One S- signaled that R3 would be followed by pellets and the other Ssignaled that R3 would be followed by sucrose. In this way, each stimulus now signaled the availability of one of the two outcomes used for its Straining. However, this treatment had no differential effect on the ability of those S-s to suppress their original responses (5.2 responses per min for the response whose outcome was now signaled by an S-, and 4.6 responses per min for the other response). To verify that S + training with R3 had in fact established a viable S-0 association, a transfer test was conducted with two other responses (lever press and chain pull), one trained with pellets and one with sucrose. In this transfer test, each S- promoted performance of the response associated with the outcome used for training that S- as an S+ (5.1 and 3.7 responses per min for the same and different response, respectively). In other words, the pellet-trained response was selectively promoted by the S- whose S+ training was carried out with pellets, and the sucrosetrained response was enhanced by the S- trained as an S + for sucrose. Thus, despite having the ability to promote performance of another response trained with the same outcome as one of its originally nonreinforced responses, an S- continued to suppress that original response. d. Conclusions. In summary, these three lines of evidence reveal that outcome identity is inconsequential to the development of a stimulus’ ability to suppress its nonreinforced instrumental response. The most straightforward interpretation of these data is that animals learn what response not to make during an S- , an instruction that is readily coded by an inhibitory S-R association. Although that learning obviously does not account for the outcome-dependent component of transfer that we have routinely observed, it may contribute to the nonspecific transfer to other responses sometimes obtained with S - ,

D. ASSOCIATION BETWEEN 0 A N D R In assessing the presence of S-R associations in instrumental learning, the preceding discussion concentrated on an analysis of the acquired properties of the explicit stimulus, and deliberately ignored the potential role that the outcome might play in evoking the response. However,

38

Ruth M. Colwill

there have been significant developments of the idea that the instrumental outcome may serve as a stimulus for the response. This proposition has surfaced in two quite different guises (Linwick, Overmier, Peterson, & Mertens, 1988; Peterson, Linwick, & Overmier, 1987; Wagner, 1981). On the one hand, it has been claimed that the response becomes associated with the memory or trace of a preceding outcome. Evidence for this viewpoint has been drawn primarily from two sources: discrete trial experiments showing that behavior on one trial is guided by the outcome event occurring on the preceding trial (Capaldi, 1967); and free operant studies showing that extinguished responding may be reinstated by the delivery of noncontingent outcomes (Reid, 1957; Rescorla & Skucy, 1974). On the other hand, it has been argued that an instrumental response becomes attached to the expectancy of the instrumental outcome (Asratyan, 1974; Pavlov, 1932; Trapold & Overmier, 1972). Trapold and Overmier (1972) proposed that a Pavlovian association develops between S and 0 during instrumental training. As a result, S develops the ability to elicit an expectancy or anticipation of 0 on subsequent trials. Because the response is reinforced in the presence of this expectancy, the expectancy acquires stimulus control over the response. What is intriguing about this suggestion is that it provides a way for behavior to be sensitive to changes in the value of the outcome even though that behavior is mediated by an evocative S-R association. Because changing the value of the outcome alters the expectancy activated by S, some of the stimulus support for the response is removed, with the result that performance is reduced. 1. Association between Outcome Presentation and Response

A striking phenomenon associated with simple extinction of an instrumental response is the restoration or reinstatement of that extinguished response by the reintroduction of its training outcome on a VT schedule (Baker, Steinwald, & Bouton, 1991; Franks & Lattal, 1976; Reid, 1957; Rescorla & Skucy, 1974; Skinner, 1938). Several authors have suggested that this reinstatement effect is evidence of the stimulus control acquired by the outcome over the response during the original training. Because the response is reinforced in a context of previously earned outcomes, the opportunity exists for an outcome presentation to develop an association with the response. Quite a different explanation, however, has been developed for a related phenomenon observed in Pavlovian conditioning in which presentations of the US lead to recovery of extinguished Pavlovian conditioned responses (Bouton & Bolles, 1979; Bouton & Peck, 1989; Rescorla & Heth, 1975; Schachtman, Brown, & Miller, 1985). In this case, it has been proposed that the function of the US presentations is to restore the integrity of the

39

Instrumental Contingencies

outcome representation whose decay contributed nonassociatively to a decline in conditioned responding. According to this account, outcome presentations might reinstate an extinguished instrumental response because they restore the integrity of the representation of the outcome that used to follow that response. One way to separate these two accounts is to examine the degree to which reinstatement of an extinguished instrumental response depends on presentation of the outcome used to train that response. Concurrent training of two instrumental responses, each with a different outcome, guarantees unique R-0 relations (R 1-01 and R2-02), but nonunique 0 - R connections (01-R1, 01-R2, 02-R1, and 02-R2). If outcome presentations evoke their responses, then deliveries of 01 and 0 2 will be equally effective in reinstating performance of either R1 or R2. However, if restoration of depressed outcome representations underlies the reinstatement effect, presentations of 01 should selectively reinstate R I , whereas presentations of 0 2 should preferentially reinstate R2. Mark Arrigan and I explored these predictions in the following experiment. We trained rats to lever press for one rewarding outcome (01)and to chain pull for a different rewarding outcome ( 0 2 ) during a light S . There were 16 sessions of concurrent training in which lever press and chain pull responses during the light were reinforced on independent V1 60-sec schedules. Both responses were then extinguished during the light for 20 sessions. These extinction sessions were identical to the training sessions except that reinforcers were not available. Finally, a reinstatement test was conducted in which there were eight presentations of one of the outcomes delivered noncontingently at random intervals during four of the eight light presentations. After another session of extinction, the reinstatement test was repeated with the other outcome. The results of these reinstatement tests are summarized on the left side of Fig. 12. It is clear that there was no selective enhancement of a response by presentations of its consequent outcome (S) compared with presentations of a different outcome (D). That reinstatement was obtained is indicated by comparisons of the stimulus rates in these tests to that obtained in the final extinction session (.44 responses per min). A similar comparison of the IT1 rates (.45 responses per min during the final extinction test) revealed that outcome presentations did not promote IT1 responding. These results suggest that reinstatement occurred but that it was not mediated by the R - 0 associations. The finding that the magnitude of reinstatement was unaffected by the identity of the consequent outcome is consistent with an evocative 0 - R association. But it also admits another less interesting possibility in terms of a mechanism of disinhibition. Pavlov (1927) reported that responding to an extinguished CS recovered after a

+

Ruth M. Colwill

40 4

CONCURRENT

S+

9 3 .-E C

L

u

:al

2

u)

c

0

a VI e

l

c 0

al

I

0

SEPARATELY

IT1

TRAINED

1 S

D

S

D

S

D

Fig. 12. Effect of outcome presentations on extinguished instrumental responses, separated according to whether the presented outcome was the same as (dark bars) or different from (blank bar) the outcome used to establish the instrumental response. Responses that had been concurrently trained and extinguished during a light S + are shown during the light S (far left) and during the IT1 (center). Responses that had been separately trained in a free operant procedure are shown on the right.

+

presentation of a novel event. He argued that the novel stimulus interfered with an inhibitory process responsible for depressing performance. An explanation in terms of disinhibition for the present results is ruled out by the following experiment in which two responses were trained in separate sessions. One response was trained with pellets and the other with sucrose liquid. In this way, each response had unique 0 - R and R-0 associations. Both responses were then extinguished for several sessions. Finally, reinstatement tests with each outcome were administered. The results of these tests are shown on the far right of Fig. 12. Performance of an extinguished response was significantly increased by presentations of its training outcome but not by presentations of the outcome used to train another response. This selectivity is important for dismissing a disinhibition account of reinstatement; such an account would have predicted equivalent elevation of both responses. On the basis of these results, it is clear that outcome presentations acquire some sort of discriminative control over instrumental responding. However, the current data do not reveal the nature of the mechanism underlying that control. Rather than serving to evoke the response directly, the function of an outcome presentation may be to reestablish an expectation of that outcome which is then responsible for reinstating

Instrumental Contingencies

41

responding. This possibility predicts that presentation of an outcome should have the same effect on an extinguished response as a presentation of an S + for that outcome. Unfortunately, that prediction is not supported by empirical work. Whereas extinguished responses are not selectively reinstated by the presentations of their consequent outcomes, their performance is selectively promoted by S s trained with their consequent outcomes. In an experiment from my laboratory, two stimuli were given standard training as S + s for different outcomes (food pellets and sucrose liquid). Then, two responses (lever press and chain pull) were trained in preparation for a transfer test. One response earned food pellets and the other earned sucrose liquid. Following this training, both responses were extinguished for 12 20-min sessions. Finally, a transfer test was conducted with the two S + s. The results of this test are displayed on the left-hand side of Fig. 13. A standard transfer effect was obtained: An S + promoted performance of an extinguished response with which it shared an outcome (dark bar) but had no effect on an extinguished response trained with a different outcome (blank bar). Reinstatement tests were also given with these responses. Those data shown on the right-hand side of Fig. 13

+

TEST'iITH

TEST WITH OUTCOME

al

c 3

.-C

E L

al

a rn

rn

C

0

a

E

\ 1 -

c 0

8-

al

2 \

S

D

IT1

S

D Pre

Fig. 13. Concurrently trained and extinguished instrumental responses during a transfer test with an S + (left panel) or during a reinstatement test following noncontingent presentations of an instrumental outcome (right panel). Left panel: Responding is plotted separately as a function of whether the consequent outcome for the response was the same as (dark bar) or different from (blank bar) that associated with the S + . Intertrial interval responding is also shown (hatched bar). Right panel: Responding is separated according to whether the reinstating outcome was the same as (dark bar) or different from (blank bar) that used for its initial training.

42

Ruth M. ColwiU

indicate that there was no selective increase in responding as a function of outcome identity. Presentations of one outcome produced increased performance of both the same (dark bar) and different (blank bar) responses compared with their rate of occurrence during a 4-min extinction period preceding delivery of the first outcome (Pre). The inference to be drawn from these results is that the operation of an S + on the outcome representation is functionally different from a presentation of the outcome per se. David Roe and I have examined this issue further by comparing the transfer of an outcome trained as a discriminative stimulus with an outcome presented as a consequence for a response. In one experiment, rats were rewarded for nose poking with one outcome (01)during periods whose onsets were signaled by the delivery of a different outcome ( 0 2 ) . Each session contained 32 trials separated by a mean IT1 of 90 sec. Trials began with the delivery of 0 2 , which signaled that a VI 30-sec reinforcement schedule was in effect for a 30-sec period. For one half of the rats, food pellets signaled that nose pokes would be followed by sucrose liquid; for the other subjects, sucrose liquid signaled that nose pokes would be followed by pellets. A transfer test was then conducted with two other responses, lever press and chain pull. These responses had been trained before the start of discrimination training. One had earned 01 and the other had earned 0 2 . Figure 14 illustrates the results of the transfer test in which there were eight presentations of each outcome. Responding was measured during the 30-sec period following an outcome delivery and during the 90-sec ITI. The left panel of Fig. 14 shows performance of the transfer response trained with 01 following presentations of 0 2 (dark bar) and following presentations of 01 (blank bar). Performance of that response was increased by presentations of an outcome (02) trained as an S for 01, but not by presentations of its consequent outcome, 01. The data shown on the right-hand side of Fig. 14 come from the transfer response trained with 0 2 . Performance of this response was slightly decreased relative to the IT1 rate by presentations of an outcome trained as an S + for 01 and by presentations of its consequent outcome, 02. Interpretation of these IT1 comparisons is complicated somewhat by the fact that no correction was made for the time taken to consume the outcomes. However, what is important to note is the absence of any increase in performance of a transfer response by presentations of its consequent outcome. These results indicate that it is important to preserve a distinction between a presentation of an outcome and an expectancy of an outcome. The impact of an S + on behavior is not equivalent to the effect of a presentation of the outcome associated with that S + . The analysis of the reinstatement effect suggests outcome presentations develop some sort

+

Instrumental Contingencies R1-01

02 01 IT1

43

R2-02

02 01 IT1

Fig. 14. Transfer test with an outcome ( 0 2 ) established as an S + for a response trained with a different outcome (01). The left side shows the response trained with 01 following presentations of the same S + , ( 0 2 . dark bar). or the same consequent (01,blank bar), and during the IT1 (hatched bar). The right side shows the transfer response trained with 0 2 following presentations of the different S + ( 0 2 . dark bar), or a different consequent (01, blank bar), and during the IT1 (hatched bar).

of discriminative control over responding. It is beyond the scope of this analysis, however, to determine the precise nature of that control. It is possible that a simple evocative association develops between the outcome presentation and the response; but it is equally feasible that the outcome presentation serves as a discriminative stimulus (S + ) and operates as a modulator of the reinforcer representation. 2. Association between Outcome Expectancy and Response

In the initial reports of outcome devaluation and transfer, Colwill and Rescorla (1985a, 1988) took care to minimize the opportunity for the development of differential associations between the outcome expectancy and the instrumental response that might have contaminated their ability to identify R - 0 associations. However, those studies did not address the issues of whether any learning about the outcome expectancy and the response occurred, and if it did, whether that learning was relatively more important than R - 0 learning in the control of instrumental behavior. Rescorla and Colwill (1989) and Rescorla (1992b) addressed these points by directly comparing the relative contributions of the outcome as an antecedent event and as a consequent event to instrumental performance.

44

Ruth M. Colwill

There are three critical stages common to each of the procedures used in these experiments. In the first stage, two stimuli are trained so that each evokes an expectancy of a unique outcome. Thus, S1 is trained to elicit an expectancy of 01 and S2 to evoke an expectancy of 0 2 . In the second stage, instrumental responses are reinforced in the presence of each stimulus. The outcomes used to reinforce these responses are different from those whose expectancies are controlled by the discriminative cues. Thus, R1 is followed by 0 2 during S1, and R2 is followed by 0 1 during S2. In this way, the identities of the antecedent and consequent outcomes available for association with R 1 and R2 are dissociated. Finally, in the third stage, either outcome devaluation or transfer techniques are used to evaluate the relative contributions of 0-R and the R - 0 associations to performance of R l and R2. The consistent finding to emerge from these studies is that the more important determinant of performance is the consequent outcome. Devaluation of an outcome led to greater depression of the response for which that outcome had served as a consequent rather than as an antecedent event in Stage 2. Similarly, transfer was superior when a stimulus signaled the consequent outcome rather than the antecedent outcome associated with the response in Stage 2. These results held when outcome expectancies were generated either by training S1 and S2 as instrumental S + (Rescorla and Colwill, 1989) or by training S1 and S2 as Pavlovian CS + (Rescorla, 1992b). Moreover, when Rescorla and Colwill (1989) directly measured the influence of potential 0-R learning on behavior, its contribution was relatively inconspicuous. 3 . Summary

The preceding review has confirmed that the function of the instrumental outcome is not simply to serve as an associate of the discriminative stimulus or the instrumental response. Instead, it is apparent that previous presentations of an outcome develop the ability to control instrumental behavior. Current evidence suggests that the outcome expectancy is only a minor participant in instrumental learning. However, that conclusion was based on an analysis of behavior during its initial acquisition. It is entirely possible that extended training may increase the contribution of that outcome expectancy to performance. E. CONCLUSION

Instrumental learning situations are a source of many opportunities for the development of binary associations. This survey has outlined evidence for associative links between all three elements of an instrumental task.

Instrumental Contingencies

45

Moreover, it appears that the relative strengths of those links varies depending on the specific relations arranged between the elements. The role of task demands in determining the nature of instrumental learning is pursued in the next section.

111.

Hierarchical Structure of Instrumental Learning

Skinner (1938) is generally acknowledged to be the first to object to an analysis of instrumental learning in terms of binary associations between the elements. He cautioned that a discriminative stimulus should not be viewed as a spur or goad for the response, but rather as “setting the occasion” for reinforcement of the response. Since then, others have agreed with his doctrine that the most suitable scheme for capturing information about an instrumental contingency is one that conjointly represents all three elements (Catania, 1971; Colwill & Rescorla, 1986: Jenkins, 1977; Mackintosh & Dickinson, 1979). However, it is only recently that discussions of this possibility have moved beyond theoretical speculation to some form of empirical evaluation. A.

EVIDENCE FOR ENCODING S-R-0 CONJUNCTIONS

Unambiguous support for the contribution of a hierarchical structure to instrumental learning has come from an analysis of performance on an instrumental analogue of the classic Pavlovian switching procedure. In these experiments, discriminative stimuli disambiguate which responses will lead to which outcomes. Thus, one stimulus (Sl) signals that R l will be followed by 0 1 , and R2 will be followed by 0 2 , whereas a different stimulus (S2) signals the opposite arrangement of responses and outcomes (i.e., R1-02 and R2-01). Because each outcome follows both responses and occurs in the presence of both stimuli, the potential binary associations are rendered uninformative about which outcome would actually follow a response in a given stimulus. Yet several studies have shown that rats learn the particular R - 0 relations that are in effect during each stimulus. These studies have used two techniques, outcome devaluation and summation, to demonstrate that exposure to a switching procedure leads t o the acquisition of some kind of hierarchical structure.

I . Outcome Devaluation A convincing illustration that instrumental learning cannot always be reduced to a collection of binary associations was reported by Colwill and Rescorla (1990b, Experiment 2). Rats were trained on a switching task in

Ruth M. Colwill

46

which various permutations of responses (lever and chain) and outcomes (pellets and sucrose liquid) were arranged in the presence of different discriminative stimuli (noise and light). Specifically, two stimuli (S 1 and S2) signaled that two responses (R1 and R2) would be reinforced, one with pellets and one with sucrose. However, each stimulus signaled a unique combination of those responses and outcomes: thus, S I signaled the relations R1-01 and R2-02, whereas S2 signaled the relations RI-02 and R2-01. After acquisition, one of the outcomes was made distasteful. In a subsequent extinction test with the stimuli and responses, the subjects showed a preference within each stimulus for the response that had previously earned the currently attractive outcome in that stimulus. In other words, after devaluation of 01, subjects showed a preference for R2 over R1 during S1 but a preference for R1 over R2 during 52. The fact that the prevailing response preference was conditional on the identity of the stimulus implies the presence of some kind of hierarchical associative structure . 2.

Summation

Confirmation that instrumental discriminations are solved under some circumstances by learning about the specific conjunctions of particular S, R , and 0 elements was provided by Rescorla (1990a, Experiment 4) using a summation test. In that study, rats were trained on a switching procedure with two visual cues signaling different R - 0 combinations. Thus, LI signaled R l - 0 1 and R2-02, whereas L2 signaled RI-02 and R2-01. In addition, auditory cues were separately established as S + s for various subsets of those R - 0 relations. One auditory cue ( A l ) signaled R1-01 and R2-01; the other auditory cue (A2) signaled R1-02 and R2-02. Summation tests were then conducted with different combinations of the auditory and visual cues. For each combination, the elements of the compound shared one R - 0 relation but not another. Performance of a response was greater if each element of the compound signaled the same outcome for that response than if they signaled different outcomes. The fact that the degree of summation was affected by whether the R-0 relation embraced by the compound elements was identical or not reveals that the animals had acquired that relational information. A similar pattern of results was obtained in an experiment conducted in my laboratory that tested summation across stimuli signaling different R - 0 relations that either did or did not share the outcome element. Rats were trained on a switching procedure with two visual cues (steady light and flashing light), two responses (lever and chain), and two outcomes (pellets and sucrose). In a summation test, each visual cue was combined

Instrumental Contingencies

47

first with a tone S + and then with a noise S + . One of these auditory cues had been trained as a signal that nose poking would be followed by pellets; the other cue had been trained as a signal that nose poking would be followed by sucrose. The immediate effect of each visual-auditory compound was to encourage preferentially performance of the response whose outcome was predicted by both elements of the compound (16.7 and 15.7 responses per min for the shared and mixed 0 conditions, respectively). Thus, lever pressing was preferred to chain pulling when the outcome for lever pressing in the visual element matched that signaled by the auditory component, but this preference was reversed when the outcome for chain pulling was signaled by both elements of the compound. Because both outcomes and both responses occurred during both visual stimuli, any generalization from the individual elements associated with the auditory cues should have been equivalent. However, learning about the specific R-0 relations in effect during the visual cues would permit differential generalization from the relation trained with an auditory cue. This reasoning takes for granted that different R-0 units generalize to each other more when they share an outcome element. These results point to the insufficiency of any analysis of instrumental learning exclusively in terms of simple binary associations. The confidence with which this assertion can be made is increased by the results of several other studies that have explored the conditions under which stimuli trained in a switching task either develop or lose control over their R - 0 relations. Each study used the strategy of manipulating the R-0 relation independently of the individual elements contained in that relation. B.

FACTORS AFFECTING ACQUISITION A N D EXTINCTION

I . Role of Information ahorrt R -0 Relation

Perhaps the most revolutionary finding of modern learning theory is the discovery that learning about the predictive value of stimuli occurs only when those stimuli provide information about their consequences that is not otherwise available. Many studies have shown the importance of this principle in governing the acquisition of stimulus-outcome associations in Pavlovian conditioning (Blanchard & Honig, 1976; Kamin, 1968; Leyland & Mackintosh, 1978; Rescorla, 1968; Wagner, Logan, Haberlandt, & Price, 1968) and response-outcome associations in instrumental learning (Dickinson, Peters, & Schechter, 1984; Hall, Channel, & Pearce, 1981; Hall, Channel, & Schachtman, 1987; Mackintosh & Dickinson, 1979; Pearce & Hall, 1978; Schachtman & Hall, 1990; St. Claire-Smith, 1979a,1979b;Williams, 1978, 1982; Williams & Heyneman, 1982). Its application to analyses of instrumental discriminative stimuli has generated

48

Ruth M. Colwill

additional support for thinking of discriminative stimuli as signals for R - 0 relations. For instance, Colwill & Rescorla (1990b) and Rescorla (1990~) found that a stimulus was impaired in its ability to acquire control over a response when training was conducted in the presence of an S + that already signaled the response-outcome relation. This kind of interference was not observed if training was conducted in the presence of an S + that had previously signaled the individual elements in an instrumental discrimination but not their combination (Rescorla, 1990~). Related to these blocking effects are studies that manipulate the contingency between a stimulus and its R-0 relation. When a stimulus is informative about the R-0 relation, it acquires control over the response. Several studies have reported that discriminative control of a response develops in multiple VI-VT schedules (Bersh & Lambert, 1975; Boakes, 1973; Huff, Sherman, & Cohn, 1975; Lattal & Maxey, 1971; Weisman & Ramsden, 1973). Even if the rate of noncontingent outcomes during the intertrial interval is such that the stimulus is correlated with a decrease in the overall frequency of outcome deliveries, that stimulus nevertheless acquires control over its reinforced response (Colwill & Rescorla, 1990b). A particularly elegant demonstration of the role of contingency was provided by Rescorla (1990~).Rats were trained with two responses and two outcomes. During the IT1 and presentations of one stimulus (redundant), R1 was followed by 01 and R2 was followed by 0 2 ; during presentations of another stimulus (informative), the R-0 relations were switched so that R1 led to 0 2 and R2 led to 01. Following this training, the responses were partially extinguished and then tested with the two stimuli. Rescorla (1990~)found no increase in responding during the redundant stimulus but a significant elevation of response rate during presentations of the informative stimulus.

2. Interactions between R - 0 Relations It is clear that the development of an association between a stimulus and an R-0 relation may be impaired by reducing the relative validity of that stimulus as a predictor of that relation. Recently, we have explored whether there is any interaction between different R-0 relations associated with the same stimulus. In one experiment from my laboratory, one group of rats was trained on a switching design with two stimuli (a noise and a light), two responses (nose poke and handle pull), and two outcomes (food pellets and sucrose liquid). A second group of rats was trained with the same R-0 pairs but the combinations of stimuli and R-0 relations were rearranged so that each discriminative stimulus was uniquely associated with one of the outcomes. Thus, for these animals, SI signaled

Instrumental Contingencies

49

that both responses would be followed by 01, and S2 signaled that both responses would be followed by 0 2 . There was no significant difference between the two groups in acquisition of this problem: The development of control over responding was unaffected by whether the stimulus signaled multiple R-0 relations with the same outcome or with different outcomes. This pattern would seem to suggest that there is no differential interference between R-0 relations that share an outcome element relative to R-0 relations that do not have a common outcome element. However, a transfer test conducted with two new responses (lever and chain), one trained with pellets and one with sucrose, suggested that there had been some interaction within a stimulus between R-0 units sharing an outcome. The transfer responses were increased more by the stimulus compound of cues separately trained with different outcomes than by a compound of cues separately trained with the same outcome (10.7 and 8.5 responses per min) although the IT1 responses did not differ significantly (5.9 and 4.7 responses per min). Taken together, these results suggest that similar R-0 relations generalize to each other during training and thus reduce the asymptotic value of the individual connections S develops separately with each R-0 unit. To account for the difference in transfer, we adopt a recommendation by Pearce (1987) that the component of generalized excitation does not itself generalize to other events. 3. Devaluation of the R - 0 Relation

Manipulations of the R-0 relation after learning has taken place have also yielded results consistent with the thesis that discriminative stimuli may signal specific R - 0 relations. In a series of experiments, Rescorla (1990a) studied the effect that devaluing an R-0 relation in one stimulus had on another stimulus associated with that R-0 relation. In one experiment (Rescorla, 1990a, Experiment 2), rats were trained on a standard switching procedure with two visual stimuli (L1 and L2), two responses (R1 and R2), and two outcomes (01 and 0 2 ) . L l signaled R l - 0 1 and R2-02, whereas L2 signaled Rl-02 and R2-01. Auditory cues were separately trained as signals for subsets of the R - 0 relations occurring during L1 and L2. One auditory cue (Al) signaled R1-01 and R2-01; the other auditory cue (A2) signaled R1-02 and R2-02. Then, both R1 and R2 were extinguished in the presence of one of the visual cues. For each response, this treatment should depress the connection with one of its associated outcomes. Finally, the two responses were tested in extinction with the auditory stimuli. Response preferences exhibited during testing of the auditory stimuli reflected that the animals had learned the particular R-0 relations correlated with each visual cue. Thus, if R1 and

Ruth M. Colwill

50

R2 had been extinguished in L1, the subjects displayed a preference for R2 in A1 and for R1 in A2. If subjects only had the ability to represent the binary relations, they would not have known which outcome followed a particular response in the switching task. Consequently, extinction of those responses would not have generated differential performance in the presence of the auditory cues observed in testing. IV. Binary versus Hierarchical Associations A.

MULTIPLECODES

The prospect of accounting for instrumental learning solely in terms of simple binary associations is seriously undermined by the data described in the preceding section. At the very least, a successful theory must accommodate the fact that animals sometimes represent the R-0 relations operative in the presence of different stimuli. One suggestion as to how this might be accomplished is for S to develop an association with the R - 0 relation (Colwill & Rescorla, 1990b; Rescorla, 1990a, 1990~).The essence of this approach is to treat the R - 0 relation as analogous to a Pavlovian US. In this way, the principles derived for identifying the conditions for learning about the relations between CS and U S may be applied directly to learning about the relation between instrumental stimuli and R - 0 relations. In fact, many of the studies described in the preceding section were guided by this insight. It should also be apparent that an S-(R-0) structure can do more than simply account for the relational data described in the preceding section. Colwill and Rescorla (1990b) argued that outcome-dependent transfer of discriminative stimuli may be attributed to differential generalization between R - 0 units. Thus, outcome-dependent transfer might reflect the greater similarity between R-0 units sharing an element, in this case the outcome, than between those with no common elements. Moreover, by parsing an S-(R-0) structure in different ways, information about the various pairwise associations can be readily extracted. Consequently, it becomes quite reasonable to challenge the necessity for any type of binary model of instrumental learning.

I. Manipulation of S - 0 Association To approach the issue of whether adoption of an S-(R-0) structure eliminates the need for a binary model of instrumental learning, we have examined how manipulations of a binary relation affect performance supported by a hierarchical structure. Because the S-(R-0) structure preserves the

51

Instrumental Contingencies

integrity of the R - 0 relation as a fundamental component of instrumental learning, our manipulations have targeted the discriminative stimulus. A natural starting point is our discovery that discriminative S + control can be seriously undermined by training S + as an S - for a different response reinforced with the same outcome. In the following experiment, we examined whether this treatment would extinguish discriminative control of hierarchical cues. To this end, Heather Seiniger and I trained rats on a switching task using the same procedure as Colwill and Rescorla (1990b, Experiment 2). After acquisition, both stimuli were established as signals for the nonreinforcement of other responses (nose poke and handle pull). One stimulus signaled that one response (R3) would not be followed by pellets, and the other stimulus signaled that the other response (R4) would not be followed by sucrose. If a hierarchical structure is common to all instances of instrumental learning, our prediction was that this treatment should specifically undermine performance of the response that had earned the outcome used for converting its switching stimulus to an S - . The design of this study is shown in Table VI along with these predictions. Because the same response would be the recipient of any decrement induced by this manipulation, this test constitutes a particularly sensitive assessment of the relation between binary and hierarchical models of instrumental learning. The results of the extinction session in which the two switching stimuli were tested with their original responses are summarized in Fig. 15. There was negligible impact of converting the switching stimuli into S - s on performance of their original responses. Both responses continued to be elevated in the presence of the switching stimuli relative to their IT1 rates. Moreover, there was no significant effect of the identity relation between TABLE VI

BASICDESIGNOF EXPERIMENT O N EFFECT OF s-0 EXTINCTION ON SWITCHING PERFORMANCE Switching procedure

S - training

Test

Predictions

R 3 - 0 1 . Sl: R 3 -

S 1 : R I < R2

R 4 - 0 1 . S2: R4-

S2: R2 < R l

SI: R I - 0 1 , R 2 - 0 2 S2: R l - 0 2 , R2-01

N o w . RI and R2 are instrumental responses. lever pressing, and chain pulling: R 3 and R4 are instrumental responses, nose poking, and handle pulling. counterbalanced across animals. 01 and 0 2 denote food pellets and sucrose liquid; S I and S2 are discriminative stimuli. noise. and light

Ruth M. Colwill

52

0-0

''

0-0

DlFF A,----A IT1

-w aJ 3

'EK

12 '

L

m

0

\

0

\

0

a (0

aJ : 0 a

SAME

8

cn

?!

A -...

A.... ,

I

Fig. 15. Effect of converting discriminative stimuli trained in a switching procedure to S - on their ability to produce their original responses. Each stimulus had been trained as an S - with an outcome that was the same as that following one of its original responses (solid circles) and different from that following the other of its original responses (open circles). Responding is also shown during the IT1 (open triangles) when no stimuli were presented.

the outcome that had followed the instrumental response in that stimulus and the outcome used to convert the stimulus into an S - . Thus, despite training as a signal for the omission of a particular outcome, a switching stimulus continued to promote performance of its original response trained with that outcome as effectively as it did performance of its original response trained with a different outcome. This pattern of results is strikingly different from the one displayed in Fig. 5 . These data suggest that animals have access to multiple associative codes for solving instrumental discriminations. However, the data leave unanswered the question of what feature of an instrumental task prejudices the animal toward a solution in terms of either binary or hierarchical connections. Two possible answers to that question are now considered. 2. Biconditional Discrimination Learning One straightforward possibility is that animals employ a hierarchical structure whenever a binary structure fails to capture accurately the information about the prevailing instrumental contingencies. To assess this idea, we

Instrumental Contingencies

53

have analyzed the learning that occurs in an instrumental biconditional discrimination. This task was chosen both because of its structural similarity to the switching task and because it can be solved using either binary o r hierarchical associations. In a switching task, the stimuli signal which one of two rewards will follow a specific response; in a biconditional discrimination, the stimuli signal which one of two concurrently available responses will be reinforced and which will not (Trapold, 1970). More specifically, one response ( R l ) is rewarded only during one stimulus (Sl) and the other response (R2) is rewarded only during the other stimulus (S2). Thus, the identity of the correct response is conditional on the identity of the discriminative stimulus. a . Outcome Devaluation. Our initial studies employed a biconditional discrimination in which the correct responses earned the same outcome (0).The ability of animals to solve the single-outcome version of this problem has traditionally been cited as prima facie evidence for some kind of S-R learning (Mackintosh, 1983). According to classical S-R theory, because rewards only follow performance of the correct response in each stimulus, an association between the discriminative stimulus and the correct response will be selectively strengthened. In this way, the unique S-R associations guarantee performance of the correct response in each stimulus. What is important to note about this evocative S-R account is that the outcome, although responsible for producing the association that permits this problem to be solved, is not itself represented in that associative structure. Consequently, manipulations of the value of the outcome after learning has taken place will have no specific impact on biconditional discriminative performance. To test this prediction, rats were trained concurrently on two independent biconditional discrimination tasks (Colwill& Delamater, 1993, Experiment 2). In one task using auditory discriminative stimuli, one response (R1)was rewarded with one outcome (01)in the presence of one stimulus ( A l ) and the other response (R2) was rewarded with the same outcome (01)in the presence of the other stimulus (A2). In the other task using visual discriminative stimuli, a third response R3 was rewarded with a different outcome ( 0 2 ) in the presence of one stimulus ( V l ) , and a different response R4 was rewarded with that outcome (02) in the presence of the other stimulus (V2). Following acquisition of the two biconditional discriminations, the value of one of the outcomes was reduced by pairing the outcome with a nausea-inducing agent. Subjects were then tested in extinction on each biconditional discrimination. The question of interest is whether performance of the correct response will be depressed when the outcome used to train that response has been devalued.

Ruth M. Colwill

54

The results of testing the biconditional stimuli with their original responses are shown in Fig. 16. Responding trained with the devalued outcome is shown on the left of Fig. 16 and responding trained with the valued outcome is shown on the right of Fig. 16. Ineach case, performance is shown separately for the correct response, the incorrect response, and during the ITI. There is no doubt that outcome devaluation had a major impact on biconditional performance. There was a substantial but selective decrease in performance of the correct response whose outcome was devalued. This sensitivity of the rewarded response t o the value of its outcome argues against a purely evocative S-R account of instrumental performance. Other accounts embracing some form of S-R learning face an equally dismal fate in their efforts to explain the present findings. Trapold and Overmier (1972) make predictions about the effects of outcome devaluation in this study that are indistinguishable from those made by classical S-R theory. Because each biconditional discrimination task employed only one outcome, the two discriminative stimuli within a task would each develop an association with the same outcome, and so elicit the same outcome expectancy. As a result, this expectancy would be less informa-

16

r

12 -

m-0

CORRECT

0-0

INCORRECT

A -- - A ITI

@\

8 -

\m 4 -

m‘ ‘*\e

0

0 .0A. 0 - - -A- --A.- - -

-

O l O

A-..

\

- - -X>-Q

-A-

Fig. 16. Mean rates of responding on the biconditional discriminations in the first extinction test after the training outcome had been devalued (left panel) or not (right panel). In each panel, performance is shown separately for correct responses (filled circles) and incorrect responses (open circles) during the disciminative stimuli and during the IT1 (open triangles) when no stimuli were delivered. From Colwill & Delamater (1993).

Instrumental Contingencies

55

tive about which of the two responses was correct than the physical features of the biconditional cues. Given the evidence that incidental or redundant stimuli do not gain control over behavior (Mackintosh, 1983; Wagner et al., 1968), the contribution of outcome expectancies to performance would be negligible. Therefore, according to Trapold and Overmier ( 19721, outcome devaluation ought not to have differentially affected instrumental responding. The version of two-process theory proposed by Rescorla and Solomon (1967) also encounters difficulty with the present data. According to their account, devaluation of the outcome should have reduced the motivation for responding in the presence of the discriminative cues. Consequently, performance of the incorrect responses should have been depressed following devaluation of the instrumental outcome. Although a floor effect may have contributed to a failure to detect such suppression in the test shown in Fig. 16, increasing the overall level of responding before a second test did not alter the results. Even with a substantial increase in the general level of responding, the likelihood of an incorrect response in the presence of the stimulus associated with the devalued outcome was not significantly different from the rate of incorrect responding in the stimulus that signaled the valued outcome. Such results provide little encouragement for the idea that the observed devaluation effects were mediated by a reduction in the motivation for responding normally provided by the Pavlovian S-0 association. A combination of S-R and R-0 associations fares more successfully with the results displayed in Fig. 16. Assuming that the function of the S-R association is to retrieve a representation of the correct response, subjects can then use the R - 0 association to evaluate the current consequences of that response. Obviously, devaluing the outcome will reduce performance of that response. But this account predicts an outcome devaluation effect only if the response has a single outcome. In a companion study, we used the same pair of responses for each biconditional problem. Thus, in one discrimination, correct responses earned pellets, but in the other discrimination, those correct responses earned sucrose. Under these conditions, an S-R and R-0 account would not predict differential sensitivity to a change in the value of one outcome. Yet, when we depressed the value of one of the outcomes through satiation, correct responses were relatively less likely in the presence of the stimuli in which they had earned that outcome than during the stimuli in which they had earned the other outcome. b. Manipulation of S - 0 Relation. The effects of outcome devaluation on biconditional discriminative performance may yet be explained by a

56

Ruth M. Colwill

binary model that employs R-0, S-0, and inhibitory S-R associations. This account attributes performance of the correct response to a combination of S-0 and R-0 associations. Thus, original responses are controlled through the same kind of mechanism as transfer responses. To generate differential responding in the presence of the biconditional discriminative stimuli trained with a single outcome, promotion of the incorrect response by the S-0 association must be counteracted. One way to accomplish this is through an inhibitory association between the stimulus and the incorrect response. To test this binary hypothesis, we exploited the fact that S + control can be reduced by training the stimulus as an S - with the same outcome. If correct responses are in fact the product of S - 0 and R-0 interactions, it should be possible to reduce their occurrence by training the biconditional cue as an S - . We have explored the impact of this extinction treatment on biconditional cues established in a variety of ways. The results are always the same: Regardless of variations in the procedures used to train biconditional cues, S - training does not have an outcomespecific negative effect on their ability to control performance of their correct response. We illustrate this point with an experiment designed to preserve the biconditional structure inherent in the preceding studies but to increase the similarity of the task t o the discrimination in which we first observed an impact of the S - treatment. To this end, rats were trained on two biconditional tasks that used different outcomes within a task. The basic design of that study is outlined in Table VII. Rats were trained concurrently on two independent biconditional discriminations. Each discrimination employed the same pair of responses (nose poke and handle pull) and two different outcomes (food pellets and sucrose liquid). In one task using auditory cues (noise and tone), one response (RI) earned one outcome (01) during presentations of one stimulus (Al), and the other response (R2) earned a different outcome (02) during the other stimulus (A2); in the other task with visual cues (steady and flashing lights), R1 earned 0 2 during V1 and R2 earned 01 during V2. After acquisition of both discriminations, two other responses (lever and chain pull) were trained, one (R3) with 01 and the other (R4) with 0 2 . The auditory biconditional cues were established as S - s for one of these responses; the visual cues were trained as S - s for the other transfer response; and R3 and R4 earned their training outcomes in the absence of the stimuli on a VI 30sec schedule. In this way, one of the visual cues and one of the auditory cues were trained as S - for a response with the same outcome; and the other visual and auditory cues were trained as S - with a different outcome. Thus, the biconditional cues for one task signaled the omission of an outcome that was the same as that used in original biconditional

57

Instrumental Contingencies

TABLE VII DESIGNOF

s-0 EXTINCTION EXPERIMENT WITH BICONDITIONAL STIMULI

Biconditional training

S

~

training

Test

AI: RI-01, R2A2: R2-02, R1-

R3-01, At: R 3 - , A2: R3/R4-02, VI: R4-, V2: R4-1

A t : RI v R2 A2: R1 v R2

VI: R1-02, R2V2: R2-01, RI -

R3-02, A t : R 3 - , A2: R3R4-01. VI: R 4 - . V2: R4-

V1: R1 v R2 V2: RI v R2

Nore. RI and R2 are instrumental responses, nose poking, and handle pulling; R3 and R4 are instrumental responses. lever pressing, and chain pulling, counterbalanced across animals. 01 and 0 2 denote food pellets and sucrose liquid: - indicates nonreinforcement; VI and V2 are discriminative stimuli, steady light, and flashing light: AI and A2 are discriminative stimuli, tone. and noise.

discrimination training, whereas the cue for the other task signaled the omission of a different outcome. Each pair of biconditional cues was then tested with its original pair of responses. It was anticipated that if performance of the correct response in this biconditional discrimination depended on the combination of S-0 and R-0 associations, then S training would produce an outcome-dependent disruption of correct responses in those discriminations. The results of testing the biconditional stimuli with their original responses are displayed in Fig. 17. Responding in the presence of cues that had been trained as S - s with the same outcome is shown on the left of Fig. 17. and with a different outcome is shown on the right. In each case, correct, incorrect, and IT1 responses are shown separately. There is no evidence that biconditional control was differentially affected by S - training as a function of whether the same outcome or a different outcome was used. Comparisons of correct response rates across the different and same conditions revealed no significant difference. The preceding discussion has established that subjects adopt a hierarchical solution to a problem even when information about the instrumental contingency may be accurately represented by binary connections. In summary, the results of our analyses of biconditional discrimination learning suggest that conventional binary accounts do not capture the full spectrum of associative structures that animals can use to solve instrumental discriminations. It is safe to conclude that hierarchical structures are not limited t o only those tasks where binary models fail. 3 . Response-Outcome Ambiguity a . Determinant of Hierarchical Structure. Another answer to the question of what feature of instrumental training encourages hierarchical

Ruth M. Colwill

58

SAME OUTCOME

28

-

.-C

24

-

L

20

-

16

-

12

-

DIFFERENT OUTCOME 0-0

CORRECT

Y al

3

E al

a v)

al v)

C

0

a

v)

2

8 -

C 0

al

z

4 -

1

2

3

4

1

2

3

4

Trials Fig. 17. Mean rates of responding on the biconditional discriminations following S training. The left-hand side displays performance on the discrimination whose cues were trained with the same outcome used for S - training; the right-hand side displays the comparable data when S - training employed a different outcome. In each case, correct (filled circles) and incorrect responses (open circles) are plotted separately. Responding is also shown during the IT1 (open triangles) when no stimuli were presented.

learning focuses on the role of response-outcome ambiguity. Those studies that have identified hierarchical structures share the property that individual stimuli disambiguate which of two outcomes will follow a particular response. In some studies, a response is followed by one food outcome, for example, food pellets, in one stimulus and a different food outcome, for example, sucrose liquid, in the presence of another stimulus. In other studies, the outcomes used are rewards and nonrewards. To evaluate whether hierarchical control develops over a response whose multiple outcomes are uniquely correlated with different stimuli, I replicated the original study demonstrating loss of S + control shown in Fig. 5 with one slight variation in the design. Instead of training S + s with different responses, the same response was used for both. Thus, rats were rewarded with one outcome (e.g., pellets) for nose poking during a light and with a different outcome (e.g., sucrose) for nose poking during a noise. Then both stimuli were trained as S - for another response, chain pulling. For one half of the animals, chain pulling was reinforced with pellets in the absence of the noise and light; for the other animals, chain pulling was reinforced with sucrose. In this way, all animals had one S + converted to an S - using the same outcome (same S + / S - ) , and the

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other S + trained with a different outcome (diff S + /S -). The impact of this treatment on original discriminative control was assessed during an extinction test in which each stimulus was tested with the nose poke response. The results of that test showed that no matter what outcome was used for S - training, both stimuli continued to promote performance of their original responses relative to the IT1 (6.8 responses per min) and the degree to which they did so was not significantly different (23.0 and 21.5 responses per min for the same S + /S - and the diff S + / S - , respectively). The disparity between these data and those shown in Fig. 5 strongly implies that the critical condition for producing hierarchical learning is one in which the multiple outcomes of an instrumental response are uniquely correlated with different stimulus conditions. The present data are also important for eliminating two other accounts of the failure of S-0 manipulations to influence performance on biconditional and switching discriminations. First, it was usually more difficult to establish the cue as an S in those studies. Often the terminal level of suppression was less than that obtained in the original experiment obtaining loss (shown in Fig. 5). Yet, in the present experiment. the level of inhibitory control at the end of S - training was indistinguishable from that obtained in the original study. Second, the designs of the biconditional and switching studies were extremely complex, involving multiple cues, responses, and outcomes in various combinations. But given the present results, it is unlikely that this factor obscured detection of the effect of extinction of the S-0 relation in those other studies. The present analysis of biconditional and switching discriminations suggests that it is profitable to view a discriminative stimulus trained in these procedures as associated with the particular R - 0 relations arranged in its presence, S+(R-0) and S -/(R-0). This approach provides a natural explanation for the effects of outcome devaluation on instrumental performance. It also anticipates selective transfer of a biconditional cue to other responses trained with the same outcome (Colwill & Delamater, 1993). Such transfer is attributed to differential generalization across R - 0 relations. Novel combinations of a discriminative stimulus and an R - 0 relation are treated as more similar to the original training condition when both responses share an association with the same outcome. An explanation for the preservation of hierarchical control following elimination of the S-0 association is rather more speculative. Colwill and Delamater (1993) proposed that the resistance of biconditional performance to the detrimental effects of extinguishing the S - 0 associations might be understood in terms of changes in the generalization gradients produced by original training. In our biconditional discrimination experi-

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ments, each biconditional cue was simultaneously established as a signal for the reinforcement of one response and the nonreinforcement of a different response. This discrimination training may have sharpened the generalization gradients such that additional training of the cue as a signal for the nonreinforcement of yet another response would have little impact on the ability of that cue to elicit its original R - 0 relation (see Mackintosh, 1974). This account can also be extended to accommodate the absence of an effect of removing the S - 0 association on stimuli signaling which of two rewards will follow a response. To the degree that occurrences of the response with 01 are treated as experiences of the omission of 0 2 for that response, a similar sharpening of the generalization gradients should occur. In summary, the conclusion to be drawn from this discussion of hierarchical learning is that animals resolve the ambiguity of a response with multiple outcomes by associating each R-0 relation with the appropriate stimulus. In the next two sections, I discuss the significance of this work for our understanding of other aspects of instrumental learning. b. Implications for Differential Outcomes Design. The preceding analysis of biconditional discrimination learning raises a serious question about the customary interpretation of the differential outcomes effect (DOE)in terms of binary associations. Traditionally, some of the strongest evidence for an association between a discriminative stimulus and the instrumental outcome has been derived from studies of the DOE (Goeters, Blakely, & Poling, 1992). Trapold (1970) drew attention to the fact that animals learn a biconditional discrimination more readily when the correct response for one stimulus is rewarded with a different outcome from that earned by the correct response for a different stimulus. Almost uniformly however, studies replicating this effect base their evidence that acquisition is more rapid in the differential outcomes condition on a comparison with a condition in which the outcomes following the correct responses are mixed or nondifferential. Consequently, as noted by Colwill and Rescorla (1988a), it may not be the inconsistency between the stimulus and the outcome that retards acquisition in the nondifferential condition, but rather the inconsistency between the response and the rewarding outcomes. More promising evidence that the relationship between the stimulus and the outcome influences instrumental learning comes from experiments implementing the design outlined in Table VIII (Peterson & Trapold, 1982; Rescorla, 1992b).In this case, the relation between instrumental responses and their consequences is held constant across the consistent and inconsistent conditions. Each response is followed by only one reward during its correct stimulus and by nonreward during its incorrect stimulus. However, only in the consistent condition are the outcomes uniquely correlated

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TABLE VIII BASICDESIGNOF DIFFERENTIAL OUTCOMES EXPERIMENT Consistent group

SI: RI-01, R2S2: R2-02, R1SI: R3-01, R4S2: R4-02, R3Inconsistent group SI: RI-01, R2S2: R2-02, R1SI: R3-02, R4S2: R4-01, R3 N o f e . R1 and R2 are instrumental responses. lever pressing. and chain pulling; R3 and R4 are instrumental responses, nose poking, and handle pulling, counterbalances across animals. 01 and 0 2 denote food pellets and sucrose liquid: SI and S2 are discriminative stimuli, noise, and light.

with different discriminative stimuli. Studies using this design have found superior acquisition in the consistent condition. An example of this effect is shown in Fig. 18. Those data come from an experiment with rats by Denise Richardson and I using the design shown in Table VIII. They demonstrate that the difficulty experienced by subjects in acquiring the discrimination in the inconsistent condition is not that they are less likely to make the correct response, but rather that they are more likely to make the incorrect response. The data shown in Fig. 18 are readily accommodated by a hierarchical model of instrumental learning. Each discriminative stimulus develops independent excitatory associations with each of its correct R-0 units. However, the generalization between the correct R - 0 units associated with a particular stimulus and the identity of the R-0 relation that is incorrect is not equal across the consistent and inconsistent conditions. In the consistent condition, the incorrect R - 0 units for a specific stimulus do not share a common outcome element with the correct R - 0 units for that stimulus. But in the inconsistent condition, each stimulus is associated with an R-0 unit containing the same outcome used to train one of its incorrect responses. Evidence that differential generalization underlies the DOE shown in Fig. 18 is supported by the results of extinguishing the connection between one of the R-0 units and its correct stimulus. That treatment eliminated the difference in preformance of the incorrect re-

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Fig. 18. Demonstration of a differential outcomes effect on acquisition of an instrumental biconditional discrimination. For the consistent group (filled circles), the same outcome was used to reward both of the correct responses for a particular stimulus; for the inconsistent group (open circles). the two correct responses for a stimulus were rewarded with different outcomes. Performance of correct responses and incorrect responses is plotted separately.

sponse whose outcome was the same as that of the extinguished correct response, but not that of the incorrect response whose outcome was different. c . Implication for Structure of an S - . The evidence that a hierarchical association develops when a stimulus disambiguates the relations between a response and its outcomes may offer some insight into the conditions that might promote analogous associations for negative discriminative stimuli. Specifically, if a stimulus signals which one of two outcomes will not follow a response, that stimulus may inhibit a particular R - 0 relation rather than develop separate inhibitory connections with the response and the omitted outcome. The following experiment tested this prediction. Two visual cues (a localized steady light and an overhead flashing light) were trained as signals that two responses would not be reinforced. In the sessions with one visual cue, lever presses earned pellets and chain pull responses earned sucrose during the ITI; in sessions with the other visual cue, lever presses led to sucrose and chain pulling led to pellets during the ITI. Thus, each stimulus served as a potential signal for the nonreinforcement of both responses and the omission of both outcomes.

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To detect whether the cues signaled which outcome would be omitted for a particular response, lever presses were reinforced in the presence of both cues with one of the outcomes (pellets for half the animals and sucrose for the other animals). The issue of interest is whether the stimuli would vary in the rate of acquisition of discriminative control over lever pressing. For example, if lever pressing for pellets developed more slowly in the presence of the cue that had signaled lever presses would not be followed by pellets, it would suggest that the signal acted on a specific R-0 relation. In the first acquisition session, performance of both responses was negligible. However, the results of the second session revealed a difference indicative of a hierarchical association. Thus, lever pressing for pellets (or sucrose) occurred significantly less often during the stimulus that had signaled lever presses would not be followed by pellets (or sucrose) than during the stimulus that had signaled lever presses would not be followed by sucrose (or pellets), 3.5 and 4.5 responses per min respectively. This result suggests that under some circumstances a discriminative stimulus may act by inhibiting an R - 0 association. Because each stimulus had been trained with both responses and both outcomes, the pattern of results cannot be interpreted in terms of simple summation of its inhibitory action on the individual elements.

B. IMPLICATIONS FOR ANALYSES OF PAVLOVIAN DISCRIMINATION LEARNING We have proposed that instrumental learning may be served by both binary and hierarchical associative structures. It is of interest to note that a similar distinction emerged in efforts to account for Pavlovian occasionsetting. In this paradigm, one stimulus, so-called occasion setter, provides information about the reinforcement or nonreinforcement of a target stimulus. As a result of experience with this paradigm, the occasion setter comes to control behavior to the target stimulus. Some authors argued that the occasion setter operates on the association between the target stimulus and its outcome (Holland, 1983, 1989, 1992; Lamarre & Holland, 1987). Others have suggested that the occasion setter acts on the outcome representation by modulating its activation threshold (Rescorla, 1985). The present analysis of instrumental learning agrees with the essential features of both views but suggests that their appropriateness may vary depending on details of the training procedure used to produce an occasion setter. Finally, the analysis of instrumental learning provides additional support for exercising caution in adopting a Pavlovian configural cue account of

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all instances of discrimination learning. According to that account, animals associate configurations of the occasion setter and its target stimulus, or the discriminative stimulus and its response, with the consequent outcome (e.g., Preston, Dickinson & Mackintosh, 1986; Wilson & Pearce, 1989). Several investigators (Holland, 1985, 1991, 1992; Rescorla, 1991) have attempted, in rather imaginative ways, to undermine a configural cue interpretation of Pavlovian occasion-setting. Rescorla (1990a) has also presented data that combat efforts to account for instrumental discriminative performance in terms of configural conditioning. This chapter describes two additional results that may present a further challenge to proponents of configural cue theory. First, the evidence supporting a partitioning of instrumental learning into binary and hierarchical structures does not, on the surface, appear easily reconcilable with a single-process model. Second, the evidence suggesting that binary instrumental S-0 associations differ qualitatively from Pavlovian stimulus-outcome associations seems to conflict with a model embracing a purely Pavlovian connection between a configuration and its outcome. It remains for future research to determine if subsequent elaborations of configural conditioning models will succeed in accommodating the present results.

V. Conclusion The work surveyed in this chapter illustrates some of the progress that has been made in documenting the rich array of associative connections that animals have at their disposal to solve instrumental problems. These advances owe much to the development of an impressive array of psychological tools for probing the structure of stored associative knowledge. Moreover, we are beginning to appreciate the relevant dimensions of an instrumental task that encourage use of one or other of the associative structures that define an animal’s cognitive repertoire. Indubitably, contemporary analyses of instrumental learning have revealed a sophistication and flexibility in the capacity of animals to represent information about instrumental contingencies that far exceeds the trial and error legacy of traditional S-R learning theory. ACKNOWLEDGMENTS This research has been generously supported by National Science Foundation Grants IBN 89-15342 to R. M. Colwill and BNS 83-08176 to R. A. Rescorla, and by a grant from Brown University BRSG 5-27469 to R. M. Colwill. Thanks are due to R. A. Rescorla for support of the work conducted at the University of Pennsylvania. I am particularly grateful

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to Eric Wolfinger and Beth Kraemer, whose assistance with data collection at Brown and at Purdue, respectively, was invaluable. I am also appreciative of the efforts of many undergraduates who collaborated on this research during their summer vacations: Deborah Archer, Gregg Belle, John Brown, Ellen Capon, Kevin Goodrum, Jennifer Lee, Barbara Shipley, Virginia Smith, Robert Tetreault, and Emily Whitcomb. I am indebted t o my colleague Andrea Megela Simmons for some insightful and critical comments on a previous version of this chapter, and to Jennifer Richeson and Robert Tetreault for their help in proofing the chapter. Correspondence concerning this article should be addressed to Ruth M.Colwill, Department of Psychology, Brown University, Box 1853, Providence, RI 02912.

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A BEHAVIORAL ANALYSIS OF CONCEPTS: ITS APPLICATION TO PIGEONS AND CHILDREN Edward A . Wusserman Suzette L. Astley

1.

Introduction

Some conceptions are of things, some of events, some of qualities. Any fact. be it thing, event, or quality, may be conceived sufficiently for purposes of identification. if only it be singled out and marked so as to separate it from other things. Simply calling it “this” or “that” will suffice. . . . In this sense, creatures extremely low in the intellectual scale may have conception. All that is required is that they should recognize the same experience again. A polyp would be a conceptual thinker if a feeling of “Hollo! thing-umbob again!” ever flitted through its mind. (pp. 462-463).

These lines. written by William James in 1890, are ripe with possible meanings. To us, they suggest several points that will help place into a special perspective our chapter on concepts. First, James was willing to consider the possibility, albeit remote, that even the simplest animals are capable of conceptual thought. Second, he deemed either identification or recognition to be sufficient indicators of conception. Third, he suggested that identification or recognition implies discrimination, so that some stimuli will elicit a particular reaction, whereas others will not. Finally, he spoke of overt and covert signs of conception; in the first paragraph, different public words in response to different stimuli indicate discriminaT H E PSYCHOLOGY OF LEARNING A N D MOTIVATION. V O L . 31

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tion, whereas in the second, common private experiences in response to the same stimulus indicate recognition. As was James, we too are willing to consider the possibility that nonhuman animals exhibit behaviors that, if they were to be exhibited by human beings, would lead most theorists to describe those actions as evidencing conceptualization. We also think that there are clear behavioral signposts of conceptualization and that recognition and discrimination are among them. We part company with James-and with a good many past and present theorists of cognition-by proposing that a rigorous and comparative analysis of concepts should primarily concern public stimuli and responses; speculations about private experiences, mental structures, and prototypical representations may be unnecessary for a sound understanding of the problem. But just what is the problem? What behaviors do human and nonhuman animals exhibit that lead us to label those actions “conceptual”? Two examples from the exploits of the first author’s children illustrate well the nature of conceptual behavior. When she was 2 years old, Lara was treated to her first film at a local theater. After her parents carefully tutored her in cinema etiquette, Lara attended and thoroughly enjoyed watching The Little Mermaid, her first movie. Soon after that experience, Lara saw her second movie on television, 20,000 Leagues Under the Sea. Days later, while her dad was remotely sampling the television’s viewing fare, he paused on one channel to see if Lara wanted to view an ongoing Jacques Cousteau adventure show. She said, “Yes, watch movie.” When he was 2 years old, David displayed strong dislike for a piece of cake that, unlike any that he had tasted before, was garnished with nuts. Some weeks later, David was given a pasta salad for his main course at dinner. After chewing for a while, he spit out a piece of celery and said, “I don’t like nuts.” The same nonverbal and verbal behaviors occurred on a later occasion when David was eating a helping of frozen yogurt that was filled with chocolate chips. Evidently, Lara’s limited cinematic experience had led her to say that all films containing underwater scenes were “movies.” Similarly, David’s limited dining experience had led him to say that all firm ingredients that are mixed with softer foods were “nuts.” Parents, of course, find such vignettes charming and amusing; youngsters in the early stages of learning language often say cute things like these. Cognitive psychologists describe these behaviors differently; children in these cases are said to have made “conceptual” errors or to have induced “overly inclusive” categories. As we will see, behaviorists take a different tack, they analyze these and many other instances of human and animal behavior in terms of the familiar psychological processes of discrimination and generalization. Lara had previously discriminated dif-

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ferent forms of visual entertainment from one another (e.g., cartoon vs. commercial), but in the preceding example, she generalized the term “movie” to all forms involving underwater action. David had previously discriminated different foods from one another (e.g., cereal vs. berry), but in the example, he generalized the term “nut” to all hard matter in soft food. These two examples help us to define better the kinds of responses that James might have called conceptual had he more extensively analyzed the problem from a behavioral perspective. Instead of talking about things, events, and qualities as single, isolated stimuli, as he did in the opening passages, we should treat them as generic classes or categories, with the members of one category being more similar to each other than they are to the members of different categories (for more on this issue, see Quine, 1969). Generalization must then be considered along with discrimination as key elements in a behavioral analysis of concepts. We turn next to the origins of that behavioral analysis.

11. Toward a Behavioral Analysis of Concepts

The seed of a behavioral analysis of concepts can be traced to B. F. Skinner’s 1935 paper, “The Generic Nature of the Concepts of Stimulus and Response” (Skinner, 1935). This famous article does not explicitly deal with concepts, but it does discuss the basic problem for behavior analysis that no two instances of behavior are the same, nor do they occur in the same stimulating situation. The stimuli that control behaviorindeed, the very responses they control-can best be considered to belong to classes of related entities: “The procedure recommended by the present analysis is to discover the defining properties of a stimulus and a response and to express the correlation in terms of classes” (p. 56). The discovery of defining stimulus and response properties was not going to be a simple process. Skinner recognized that “it is difficult . . . to say precisely what defining properties are” (p. 49). Nevertheless, he offered that “the defining property of the stimulus is inferred from the part common to the different stimuli that are found to be effective [in producing the response]” (pp. 48-49). The notion of generic class and other elements of Skinner’s analysis of operant behavior were joined in an elegant account of conceptualization by two other pioneers in the experimental analysis of behavior: Fred S. Keller and William N . Schoenfeld. In their well-known textbook, Principles of Psychology (1950), Keller and Schoenfeld argued that the very question What is a concept‘? is the wrong one to ask. Instead, we should

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begin with the question, What type of behavior is it that we call conceptual? They proposed that “when a group of objects gets the same response, when they form a class the members of which are reacted to similarly, we speak of a concept” (p. 154). So, for example, when a young child is taught to respond shoe to discriminably different members of one class of objects and book to discriminably different members of another class of objects, we have observed conceptual behavior. Keller and Schoenfeld did not explicitly consider the transfer of discriminative responding from familiar to novel stimuli. Nevertheless, we are quite certain that Keller and Schoenfeld would have expected such transfer, so that new shoes and new books will occasion appropriate behaviors, if they do not differ too much in appearance from the training stimuli. Their additional comments make it abundantly clear that their behavioral analysis of concepts did not hypothesize the existence of mental structures or prototypes, nor did it conjecture that conceptual behavior is under the control of abstract features or representations of objects or events. Indeed, Keller and Schoenfeld thought that there was absolutely no need whatsoever to invent any new behavior-analytic tools to explain conceptual behavior beyond the well-established principles of discrimination and generalization. “Generalization within classes and discrimination between classes-this is the essence of concepts” (p. 155). Finally, Keller and Schoenfeld broke from traditional opinion by proposing that conceptual behavior might be exhibited by nonverbal humans, like infants, and even by nonhuman animals. The proposal that conceptual behavior is neither uniquely verbal nor peculiarly human remains controversial to this day, as it most certainly must have been when James broached the notion over a century ago. Given our own special interest in the matter, that proposal will be of prime concern in this chapter. Thus, it is particularly appropriate here to repeat Keller and Schoenfeld’s (1950) insightful critique of the concept of “concept”: It is curious to note the resistance that may be shown to the notion that the term or found only in the behavior of human adults. We seem to have here a problem in our own behavior. We have formed a concept of conceptual behavior which is based upon such factors as the age of the subject, his [or her] ability to verbalize. and the fact that he [or she] is human. (p. 159)

concept need not be limited to matters capable of being verbalized

Despite the striking simplicity and objectivity of behavioral analyses, like Keller and Schoenfeld’s, these accounts have generally had rather little impact on mainstream research and theory in conceptualization. Until recently, cognitive psychologists with largely mentalistic and anthropocentric leanings have generally appropriated the study of concepts as

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their own, with what some critics would say are unfortunate consequences (see Oden, 1987, for a review and analysis of such cognitive research and theory in the area of conceptual behavior). With only a few notable exceptions (such as work on human behavior using artificially constructed stimuli and similarity or connectionist models of classification; e.g., Gluck & Bower, 1988;Nosofsky, 1984),behavioral analyses of conceptualization have slowly simmered on the proverbial back burner of experimental psychology. One likely reason for the historical lack of prominence of behavioral analysis was the absence of a strong empirical base for this general approach. Keller and Schoenfeld had little more data available to them to support their behavioristic account than James had to support his more mentalistic speculations. Indeed, writing not long after James penned the introductory quotations to this chapter, C. Lloyd Morgan (1894) considered, but then rejected, the possibility that nonhuman animals are capable of conceptual thought. Morgan came to this conclusion, “in no dogmatic spirit, and not in support of any preconceived theory or opinion, but because the evidence now before us is not, in my opinion, sufficient to justify the hypothesis” (p. 377). In this light, the present chapter reviews most of the empirical evidence that our laboratory has collected over the past several years on conceptualization by pigeons. That evidence supports the speculations of James, Keller, and Schoenfeld that nonhuman animals do display conceptual behavior. The concepts that they acquire are not only of basic-level categories of objects, but of superordinate categories as well. We will also see that the notions of primary and secondary stimulus generalization (Hull, 1943) can explain these cases of conceptual behavior. And, despite our concentrating on the behavior of nonhuman animals, we will see that this analysis of concepts is also applicable to the behavior of young children.

111. Empirical Evidence on Basic-Level Conceptualization

by Pigeons Our decision to research conceptualization was not intended to shift the empirical focus of our pigeon laboratory (although that is precisely what happened); rather, it was the outgrowth of a continuing interest that the first author had in exploring the utility of behavioral analyses of cognitive processes (Wasserman, 1981,1982,1983). Many years of discussing Keller and Schoenfeld’s analysis of concepts in the classroom finally inspired an initial inquiry that was guided by two additional developments: Herrnstein’s celebrated studies of snapshot discrimination by pigeons

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and our own experimental analogue for pigeons of concept learning by children. When we began our research into conceptual behavior in animals, we wished to build on the groundwork laid by Herrnstein and his colleagues (for a review of this research, see Herrnstein, 1985). That work-begun in 1964 by Herrnstein and Loveland-clearly established that pigeons could discriminate 35-mm color slides that depicted a particular class of stimuli (like people, fish, or trees) from otherwise comparable slides that did not. These discriminations not only held for large sets of previously seen slides, but they also generalized to novel slides from the featurepresent and feature-absent categories. Successful stimulus generalization in these projects supported Herrnstein’s suggestion that basic-level concepts or categories are effectively open-ended; they comprise limitless instances of related stimuli. So, we too taught pigeons to discriminate colorful photographic images, but we wanted them to engage in a much closer approximation to human conceptual behavior than the feature-present versus feature-absent discriminations studied by Herrnstein’s group. Complementary concepts like tree and nontree do conform with James’ earlier quoted discriminations of “this” versus “that.” However, these concepts are not only highly atypical and asymmetrical, but the stimulus control over behavior acquired in these tasks may differ notably from that acquired in other kinds of discriminations (for more on this matter, see Edwards & Honig, 1987; Herrnstein, 1979; Wasserman, 1974). Thus, in our work, we required pigeons to discriminate concurrently slide images depicting stimuli from four different classes of objects, each class representing basic-level categories from different superordinate categories.’ In addition, Herrnstein (1985) had reported difficulty in training pigeons to discriminate slides with humanmade objects from slides without those objects. Thus, two of the four classes of stimuli that we used were photographs of natural objects and two were photographs of humanmade objects. Of course, if we were to devise an effective and suitable analogue of human concept learning, then we would have to capture the gist of the

’

Two of the categories that we used (chair and car) are identical to some of the nonbiological basic-level categories studied by Rosch. Mervis, Gray, Johnson, and Boyes-Braem (1976). The other two categories that we used were biological in nature and they varied across studies; these categories were not identical to any used by Rosch et al. However, it could be argued that these categories are at an equivalent level to the basic-level categories used there. Rosch et al. found the categories tree, fish, and bird to be at the basic level for human adults. The cat and flower categories that we used in the earliest experiments seem similar in level. In later studies, we substituted “human” for “cat.” It is possible that “human” is a special category unlike any other biological category for human subjects, but not for pigeon subjects.

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process and translate it into a setting that would be suitable for the pigeon-a nonhuman and nonverbal creature. Our behavioral analysis of conceptualization led us to consider the many ways that we commonly learn concepts. We picked the very familiar case of a parent teaching a child to name the pictures in a book. In this so-called “name” game, the parent opens the picture book, points to one of its many colorful illustrations, and asks the child, “What is it?” If the child makes the correct verbal response, then positive social reinforcement is provided. If the child makes the incorrect verbal response, then no positive reinforcement is provided; instead, the parent may ask the child to try again, and if this request also fails to occasion the correct verbal response, then the parent may have to supply it. Many hours are spent by both parent and child in this highly enjoyable activity. Yet, the name game is much more than a mere diversion: It involves the parent’s very careful and selective application of social reinforcement, so that different classes of discriminative stimuli gain control over the child’s ever-expanding repertoire of verbal behavior. A N D GENERALIZATION A. EXPERIMENT 1 : DISCRIMINATION

In our work, we tried to extract and apply the essence of the name game to pigeons to see if these animals can concurrently report stimuli from four different categories of objects. Instead of requesting verbal behavior from our subjects (an obvious impossibility), we asked the birds to report members of four different categories-cats, flowers, cars, and chairs-by pecking four circular keys surrounding a square central viewing screen. On each of 40 daily trials, the four pigeons in one of our first experiments (Bhatt, Wasserman, Reynolds, & Knauss, 1988, Experiment 1B) were shown color slides depicting 10 different examples from each of the four categories. Within each category, the slides differed greatly from each other in the number, size, color, brightness, orientation, location, and context of the stimulus object. Our aim in selecting objects to photograph was to capture a broad range of category instances in those places where humans would ordinarily find them. In some of the slides, the stimulus object was partially obscured, whereas in others, the whole object was visible. After 30 pecks to the viewing screen to ensure that the slide had been observed, the four report keys were illuminated (each with a different color to help the pigeon to distinguish its response options) and a single choice response was permitted. If it was to the correct key for reporting members of the category of stimulus shown on the viewing screen on that trial, then the pigeon was given food reinforcement; if it was to any of the three incorrect keys, then no reinforcement was given and a correction

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trial immediately followed. (Correction trials were never scored.) The pigeon continued on to the next trial only after a correct choice-whether it was the first or the nth of a trial. A particular pigeon might have to peck the top left key in response to pictures of cats, the top right key in response to pictures of flowers, the bottom left key in response to pictures of cars, and the bottom right key in response to pictures of chairs. Different pigeons received different category-key assignments, so that across a quartet of birds each key was equally often associated with each of the four stimulus categories. Acquisition of discriminative responding under these conditions was quite regular and rose from the chance accuracy score of 25% to a mean of about 80% correct after 30 days of training. At no point in the conduct of this experiment or in any of numerous others conducted in our laboratory was there any sign that the pigeons categorized the photographs of humanmade stimuli more slowly or less accurately than they categorized the photographs of natural stimuli. Immediately after reaching the 80% level of acquisition, the pigeons were given generalization testing with 10 brand-new snapshots of objects in each of the four categories. The testing phase lasted 2 days, on each of which the pigeons saw 20 old slides and 20 new ones. Accuracy to the old slides averaged 81% and to the new ones 64%. Thus, the pigeons had acquired very discriminative behavior, which enabled them to categorize a set of highly complex and lifelike stimuli that they had seen 30 times before, and still other stimuli that they had never seen before. If, as Keller and Schoenfeld had argued, generalization within categories and discrimination between categories is the essence of concepts, then surely our pigeons can be said to have evidenced conceptual behavior. We believe that it is noteworthy that categorization accuracy was reliably lower to the novel test stimuli than it was to the familiar training stimuli. This generalization decrement may be explained by a host of different theories of conceptual behavior-from exemplar models to prototype models (Smith & Medin, 1981; see also Astley & Wasserman, 1992). This fact suggests that the pigeons had memorized some or all of the photographic stimuli that they had seen during training, although absolutely nothing in the training regimen required them to do so. B. EXPERIMENT 2: CATEGORY SIZE

Further data on the issue of individual stimulus learning and memory come from a later project conducted in our laboratory by Bhatt (1988; see Wasserman & Bhatt, 1992). There, three groups of four pigeons each were given 48 daily training trials comprising: 12 copies of 1 example from the

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categories cat, flower, car, and chair (Group 1); 3 copies of 4 examples from the categories (Group 4); o r I copy of 12 examples from the categories (Group 12). The obtained speed of discrimination learning was an inverse function of the number of examples shown per category. The mean numbers of days to reach a criterion of 70% accuracy on 2 successive days of training were 6 for Group 1, 1 I for Group 4, and 22 for Group 12. Either the smaller number of stimulus repetitions or the greater number of stimuli to be remembered with increasing numbers of examples per category can account for this learning function. Of additional importance were the results of ageneralization test with 32 novel stimuli-8 from each category. Here, accuracy was a direct function of the number of examples given during training. The mean percentages of correct choices on generalization test trials were 27% for Group I , 45% for Group 4, and 62% for Group 12. Thus, although increasing the difficulty of original learning, greater numbers of training examples per category enhanced the accuracy of generalization performance, perhaps because of the increased likelihood that any given test stimulus will resemble one or more of the remembered training stimuli (Smith & Medin, 1981). Not only are these learning and testing data orderly, but they neatly correspond with a large body of research on categorization in humans (reviewed by Homa, Burruel, & Field, 1987) and with a recent report on two-category discrimination performance in pigeons involving 5 versus 35 examples of bird and mammal sketches per category (Cook, Wright, & Kendrick, 1990).* A final point to note is that all of the pigeons in this experiment exhibited greater accuracy to old than to new stimuli, even the birds shown 12 different pictures per category during original discrimination training (and the subjects that had evidenced the highest level of transfer to novel test pictures).

C . EXPERIMENT 3: TRIAL-UNIQUE STIMULI All of the research described so far has entailed stimuli that were repeated-either between daily sessions or both between and within daily sessions of training. Is such stimulus repetition necessary to support successful discrimination learning and generalization?

’

Using stripes of various colors and lengths, Pearce (1988) did not observe differences in go-no go discrimination learning between a group of pigeons given 18 S + s and 18 S-s, and another group given 1 S+ and 1 S-. Novel test stimuli were not given to test for generalization.

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To investigate this issue, we created a large library of snapshots from four categories: people, flowers, cars, and chairs (Bhatt et al., 1988, Experiment 3). We replaced pictures of cats with pictures of people because the latter individuals were generally (but not always, as we were sometimes pointedly reminded) much more agreeable to having their photographs taken. With 2000 snapshots (500 from each of the four categories) and 40 trials per session, we could train the pigeons for 50 sessions with no stimulus ever being repeated; each trial was thus both a training trial and a testing trial. The results of the experiment were clear-cut: pigeons came to respond discriminatively to stimuli from the four different categories of pictures even if those individual examples were never repeated. After beginning at the chance level of 25%, discrimination accuracy of a group of four pigeons rose to a mean level of 70% over Days 41 to 50 of training. Clearly, the birds had to have been remembering something about the photographic stimuli that they had seen earlier. But what? Given that a large, nonrepeating set of stimuli was used in this experiment, if the pigeons remembered individual stimuli, then it is likely that on a given day they remembered some or all of the slides presented to them on the previous day. We tested this possibility in a follow-up investigation that began immediately after the conclusion of the main experiment. On Day 5 1, the pigeons were tested with a slide tray that contained a mixture of 20 of the slides shown on Day 50 plus 20 new slides. Accuracy to the old slides averaged 79% and to the new ones 75%. Although in the correct direction for indicating individual stimulus memory, this disparity in discrimination accuracy fell short of attaining statistical significance. REPETITION D. EXPERIMENT 4: STIMULUS The prior experiment convincingly showed that stimulus repetition was not at all necessary for categorical discrimination learning. It also failed to show any reliable positive effect of stimulus repetition on discrimination accuracy, although the conditions that were arranged for demonstrating such an effect could hardly have been poorer. Experiment 4 (Bhatt et al., 1988, Experiment 4) more systematically investigated the matter. Here, a set of 40 slides-I0 each from the categories person, flower, car, and chair-was chosen randomly from the library of 2000 used in Experiment 3. Four different pigeons were trained with this set of slides on “repeating” sessions that alternated with “nonrepeating” sessions in which the birds were trained with new sets of slides that were never used again in another session. The pigeons were trained with the repeating 40-slide set on Days 1 , 3, 5, . . . , 95 while being trained with novel nonrepeating slide sets on Days 2 , 4, 6, . . . , 96.

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The results clearly showed that discriminative responding rose faster and attained higher final levels of accuracy to the repeating slide set than to the nonrepeating slide sets. Performance on the repeating set rose from a mean of 29% in the first 4-day block to a mean of 85% in the last 4-day block; performance on the nonrepeating sets rose from a mean of 26% in the first 4-day block to a mean of 66% in the last 4-day block. Although quite unnecessary for categorical discrimination learning, stimulus repetition materially facilitates the process. Given that the set of 40 repeating slides was chosen at random from a total pool of 2000, it is highly unlikely that the superior performance on the repeating slides was a result of their being inherently easier to categorize by the pigeons than the nonrepeating ones. Nevertheless, to be more confident that the results of the experiment were not because of this unlikely confounding, we conducted a follow-up project. Here, we retrained the pigeons on the same task, this time using a different repeating slide set. Specifically, the set of novel stimuli presented to the pigeons on Day 96 was used as the repeating set in the follow-up investigation. The nonrepeating slide sets now entailed the photographs that the pigeons had seen as the nonrepeating slides in the main experiment. Thus, the nonrepeating sets consisted of slides that the pigeons had seen only once before, 95 days earlier, thereby making it even more difficult for us to obtain a reliable difference between the new repeating slide set and the nonrepeating slide sets. This follow-up investigation lasted 16 days and adhered to the same alternating-day plan as the main experiment. The results of the follow-up project were similar to those of the main experiment. Categorization performance on the repeating set of slides was more discriminative (M = 76% correct in the last 4-day block) than was performance on the nonrepeating sets of slides (M = 66% correct in the last 4-day block). Examination of individual birds’ scores disclosed that each pigeon showed more discriminative performance on the repeating slide set than on the nonrepeating slide sets and that this difference was present even in the first 8 days of the project. To obtain a more exact estimate of the number of repetitions necessary to facilitate categorization during the follow-up investigation, we looked for the first session with repeating slides on which two performance criteria were met: (a) the bird’s discrimination performance on that session was more accurate than its performance on the first exposure to the repeating slide set, and (b) the bird’s performance on that session was more accurate than its performance on the preceding and succeeding sessions with nonrepeating sets of slides. It took a mean of only two repetitions for the four pigeons to meet these criteria. That number was less than the six repetitions that, by chance alone, would have resulted in a session meeting

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these criteria. That number was also greater than the single repetition that the birds in the follow-up portion of Experiment 3 had received, where no statistically reliable performance advantage over novel stimuli was evidenced by stimuli seen only one time before on the previous day. Finally, it should be noted that accuracy to the repeating slide set in the follow-up project (76% correct) was lower than was accuracy to the repeating slide set in the main experiment (88% correct). This finding is consistent with the different number of repetitions involved in each case: 7 in the follow-up investigation and 47 in the main experiment. IV.

Stimulus Generalization and Conceptualization

Given all of the preceding data on conceptual discrimination and generalization in pigeons, one might wonder whether the differential reinforcement that we so assiduously administered in those experiments really created the conceptual behavior that our pigeons exhibited. This point may appear to be an odd one to raise, but Herrnstein and de Villiers (1980) speculated on the basis of their own research that differential reinforcement may not produce, but merely disclose already-existing concepts: “Something in the pigeon’s perceptual dynamics ties [stimuli] together as a class, prior to differential reinforcement” (p. 87). This argument is tantamount to saying that primary stimulus generalization is at the root of conceptual behavior, an assertion that is entirely consistent with Keller and Schoenfeld’s behavioral analysis and one for which there is growing empirical support in both human and nonhuman animals (Harnad, 1987). It seems quite reasonable to hypothesize that many, if not most, basiclevel human conceptual categories comprise highly similar stimuli. To our eyes, cats resemble one another much more than they resemble flowers, cars, or chairs. This perceptual similarity may be an important and inborn factor responsible for the emergence of the very concepts that we are considering, a possibility stated most clearly and emphatically by Quine ( I 969): If then I say that there is an innate standard of similarity, I am making a condensed statement that can be interpreted, and truly interpreted, in behavioral terms. Moreover. in this behavioral sense it can be said equally of other animals that they have an innate standard of similarity too. It is part of our birthright. And, interestingly enough, it is characteristically animal in its lack of intellectual status. (p. 1 I )

Quine (1969) went on to suggest that the origin of perceptual similarity as well as the concordance of similarity relations from person to person result from the operation of evolutionary mechanisms (for a divergent

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view, see Keil, 1989). “If people’s innate spacing of [perceptual] qualities is agene-linked trait, then the spacing that has made for the most successful inductions will have tended to predominate through natural selection” (p. 13). Anderson (1991) later expanded on Quine’s thesis and proposed that the main force behind perceived similarity is physical similarity. In his words, “the mind has the structure it has because the world has the structure it has” (p. 428). Quite apart from the origins of perceived similarity (see Spinozzi, 1993, for a discussion of both phylogenetic and ontogenetic trends in spontaneous classificatory behavior in human and nonhuman animals), we can legitimately ask whether such categorical similarity is perceived by nonhuman animals like pigeons? And, if it is, then how can we tell? To answer these questions, our laboratory has pursued three different lines of inquiry suggesting that pigeons and humans do similarly group stimuli into different categories without the birds ever being required to do so by the prevailing contingencies of reinforcement. A.

EXPERIMENT 5: CATEGORIES A N D PSEUDOCATEGORIES

One way to study the coherence of categories and their concordance in pigeons and people involves comparing the relative speeds of pigeons’ learning to sort the same pictorial stimuli into human conceptual categories (“true categorization”) or into absolutely arbitrary collections (“pseudocategorization”). Suppose that all of the slides in the total pool of cat, flower, car, and chair stimuli were equally discriminable from one another. If this supposition were true, then pigeons trained on the true categorization task should learn at the same rate as pigeons trained on the pseudocategorization task, in which equal numbers of cats, flowers, cars, and chairs are associated with the four different key-peck responses. However, if to pigeons members of the human conceptual categories more closely resemble one another than they resemble members of the other conceptual categories, then learning of the true categorization task should proceed faster than learning of the pseudocategorization task. This prediction follows from the fact that correct responding in the true categorization task should be bolstered by direct strengthening of responding to a particular key in the presence of a particular stimulus and by indirect strengthening due to similar stimuli in the same category occasioning the same response. However, in the pseudocategorization task, correct responding will be bolstered primarily by direct strengthening of responding to a particular key in the presence of a particular stimulus; greater generalization within the conceptual categories here should produce an equal likelihood of pecking all four keys, thus decreasing the accuracy of discriminative performance.

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Our study comparing these two conditions (Wasserman, Kiedinger, & Bhatt, 1988, Experiment 2) clearly supported the latter possibility. Over Days 37 to 40 of discrimination training, pigeons on the true categorization task averaged 79% correct, whereas pigeons on the pseudocategorization task averaged only 44% correct. These results (and those of Edwards & Honig, 1987, Herrnstein & de Villiers, 1980, and Pearce, 1988, but not those of Cook et al., 1990, who used special pen-and-ink sketches of animals) implicate differential within- versus between-class generalization as a key feature of visual categorization in animals. Follow-up data were collected by Bhatt (1988) on the role of the number of different examples constituting true categories and pseudocategories. Experiment 2, described earlier, documented the decrease in the speed of learning true categories containing, respectively, 1, 4, or 12 different examples. Bhatt also found that, with pseudocategories, the mean numbers of days to reach a criterion of 70% accuracy on 2 successive days of training were 6 for Group 1,3 18 for Group 4, and SO for Group 12. Thus, the disparity between the speed of true category learning and the speed of pseudocategory learning rose as the number of examples per category or pseudocategory was increased: the disparity was 7 days with 4 examples per category or pseudocategory (11-18 days), and it was 28 days with 12 examples per category or pseudocategory (22-SO days). B. EXPERIMENT 6: SPLITCATEGORIES

A second line of inquiry also concerned the nature of the stimuli that to pigeons constitute a class or category of objects. Although others investigating animal behavior with different techniques had shown that members of one stimulus class resemble one another more than they resemble members of other stimulus classes (Cerella, 1979; Sands, Lincoln, & Wright, 1982), we devised a new method by which this notion could be tested with pigeons (Wasserman et al., 1988, Experiment 1). In any particular 40-trial session, pigeons were given a split-category discrimination in which they viewed 20 cat slides and 20 flower slides (or 20 cat slides and 20 chair slides, or 20 car slides and 20 flower slides, or 20 car slides and 20 chair slides). For each pigeon, one half of the cat slides would require a peck to one key (Key 1) and the other half of the cat slides would require a peck to a second key (Key 21, whereas one half of the flower slides would require a peck to a third key (Key 3) and the other half of the flower slides would require a peck to a fourth key (Key 4). (Cat-chair, car-flower, and car-chair sessions were similarly con-

’

True categories and pseudocategories with just one example per category are, of course, equivalent.

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structed, with different pigeons having different key assignments.) If the cat slides in the first set were equivalently discriminable from the 30 other slides shown in the illustrative session, then errors should be randomly distributed to Keys 2, 3, and 4. However, if the 10 slides in the first set of cats are more similar to the 10 slides in the second set of cats than they are to the 20 flower slides, then more errors should be made to Key 2 than to Keys 3 or 4. The pigeons’ pattern of errors of commission was clearly consistent with the latter possibility. Over Days 105-1 12 of training, a mean of 56% of all errors were within-category in nature (33% was the chance level of errors because on only three keys could mistakes be made). This split-category experiment was relevant to another salient issue: The discriminability of the training and testing stimuli in our initial generalization experiments (Bhatt et al., 1988; the present Experiment 1). Did our birds respond discriminatively to the novel test stimuli because they were indiscriminably different from the training stimuli? Or had generalization occurred to discriminably different stimuli? Because the different split categories used by Wasserman et al. corresponded with the training and testing sets used by Bhatt et al., we could decisively answer the question. Over Days 105-1 12, the pigeons’ split-categorization accuracy averaged 72% (here, the chance of a correct response was again 25%). Because the training and testing stimuli in the Bhatt et al. project were demonstrably discriminable to pigeons, we conclude that the reliable generalization obtained in that study did not result from the birds’ inability to discriminate the new from the old sets of stimuli. This case of stimulus generalization thus passes a rather stringent test of conceptual behavior, one that excludes the rather uninteresting possibility of transfer resulting from the subject’s inability to discriminate the training stimuli from the testing stimuli (for more on this issue, see Quinn & Eimas, 1986; Younger & Cohen, 1985). A final point to note about the split-categorization experiment is that the within-category generalization results cannot be explained simply by the contingency of reinforcement to which the pigeons were exposed. If the pigeons were to have discriminated between but not within the given categories, then all errors of commission would have been of the withincategory variety and correct responding could never have risen above 50%. The obtained rise in correct responding to a mean level of 72% indicates the acquisition of stimulus control by some or all of the individual pictures that the pigeons were shown. Thus, the prevailing contingency of reinforcement might have encouraged within-category generalization (yielding an increase of from 25% to 50% food reinforcement), but not nearly as much as it should have encouraged within-category discrimina-

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tion (yielding an increase of from 25% to 100% reinforcement). Any effort on the part of the birds t o maximize the obtained rate of food reinforcement should have reduced rather than increased their propensity to commit within-category errors.

C . EXPERIMENT 7: Go-No G o DISCRIMINATION Direct evidence on the perceived similarity of category members comes from a more recent investigation (Astley & Wasserman, 1992, Experiment 2), in which pigeons learned a successive go-no go discrimination with 60 slides: 12 S + s and 48 S-s. All eight birds were given the same S-s: 12 people, 12 flowers, 12 cars, and 12 chairs. Different birds had different S+s: A given bird’s S + s might be 12 different people, 12 different flowers, 12 different cars, or 12 different chairs. Assuming that the 12S+s are equally similar t o all 48 S-s, errors should be randomly distributed among the four S- categories-including the one from which the S+s were picked. But, if t o pigeons, members of a given human conceptual category more closely resemble one another than they resemble members of different conceptual categories, then errors should be nonrandomly distributed and should be disproportionately committed to the S- s from the same category as the S+s. Precisely the latter result was obtained regardless of whether the S + s were slides of people, flowers, cars, or chairs. Over all 16 days of multiple schedule training and all eight pigeons, a mean of 43% (rather than the chance mean of 25%) of all S- errors were committed to stimuli from the S+ category. This work thus reveals that pigeons group similar stimuli together, even when that grouping is completely unrelated to the prevailing contingency of reinforcement. Unlike our initial studies that first explicitly reinforced correct categorization responses and then found reliable generalization to untrained instances, categorical generalization here was evidenced by the birds’ untrained propensity to commit the most errors to negative discriminative stimuli from the same conceptual category as the positive discriminative stimuli. A strong perceptual basis for conceptualization is clearly implicated by the results of this study. Beyond the fact that within-category errors exceeded between-category errors, we also found that the pigeons responded much more to the S + s than they did to the S- s from the same category as the S + s. This result again testifies to the discriminability of individual stimuli from the several human conceptual categories that we have studied (cf. Experiment 6). Still other data that we collected in experiments affiliated with this project concern previously discussed empirical relations and yet-to-be discussed theoretical interpretations.

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In addition to investigating a photographic discrimination involving 12 different S+ s and 48 different S - s, we also studied a photographic discrimination involving only 1 S + and the same set of 48 different S-s (Astley & Wasserman, 1992, Experiment 1). The latter discrimination was learned faster and entailed a smaller percentage of within-category errors than did the former. It took only 6 days for birds given the 1 S+/48 S- discrimination to respond to the S + picture at a mean rate that was at least an order of magnitude greater than that to the S- pictures, whereas it took 11 days for birds given the 12 S+/48 S- discrimination to do so. Also, the highest mean daily percentage of within-category errors for pigeons given the 1 S+/48 S- discrimination was 43%, whereas it was 57% for pigeons given the 12 S+/48 S- discrimination. Finally, we replaced some of the original S- slides from the 12 S + / 48 S- discrimination with brand-new slides (Astley & Wasserman, 1992, Experiment 3). Those replacements were chosen from the three S- categories from which the S + s were not drawn, to ensure that any changes in response rate to the new stimuli from the three S- categories were not caused by changes in the similarity of the replaced stimuli to the S + pictures. In special test sessions, the mean response rates to the original S + s and to the original S-s from the three conceptual categories combined were 3.33 and 0.11 key pecks per sec, respectively. The mean response rate t o new stimuli from the three S- categories combined was 0.69 key pecks per sec, a rate that was reliably below that to the S + slides and reliably above that to the original S- slides. The latter difference appears to represent an inhibitory analogue of the generalization decrement that we reported in our first study (Bhatt et al., 1988; the present Experiment 1). Evidently, the new negative stimuli resembled the old ones enough that there was partial, but not complete, generalization of inhibition from the old to the new stimuli. Because of the nature of the discrimination task, generalization decrement in this experiment was translated into an increased rate of response rather than into a decreased percentage of correct choice responses.

V.

A Spencian Model of Basic-Level Categorization

The work that we have reviewed represents most of the empirical evidence that we have so far collected on the pigeon’s acquisition and transfer of basic-level visual concepts. That evidence is, as we have suggested, quite consistent with a behavioral analysis relying primarily on the familiar principles of discrimination and generalization. (If Keller and Schoenfeld

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were to have had similar data at their disposal, then their behavioral analysis of concepts might have had more impact on the psychological community than it has so far exerted.) In fact, after they learned of our data, two close colleagues at The University of Iowa, Cantor and Spiker (personal communication, 1987), found the case for a behavioral analysis of concepts so compelling that they posed to us the disarming question, “Why speak of concepts at all if the simple notions of discrimination and generalization will do?” Perhaps the best answer to this query is that, historically, the ideas of discrimination and generalization are strongly associated with stimuli that are readily measurable and manipulable. Indeed, the notion of a generalization gradient presupposes one or more dimensions along which the experimental stimuli clearly vary. When we consider lifelike stimuli, such as the color slides used in our own research, we see no easy way to think of the complex representations of cats, flowers, cars, and chairs as lying along simple physical dimensions, like hue, brightness, area, orientation, and so on. Hence, the straightforward application of the most prominent theories of discrimination learning, like that of Spence, appears to be impossible. Despite the seeming impossibility of applying Spence’s very influential and successful 1937 model of discrimination learning to lifelike visual stimuli, we (Astley & Wasserman, 1992) have recently made just such an effort. We are not at all sure that Spence would have approved of our extension of his theory. (To date, there have been no obvious signs of displeasure from Spence’s ghost that is rumored to roam the halls of the Spence Laboratories of Psychology at the University of Iowa in the wee hours of the night.) Nor are we at all confident that our extension of Spence’s theory will prove to be as successful as connectionist models or multidimensional scaling accounts; proponents of these other interpretations might view our Spencian model as merely a first step toward their more sophisticated accounts. Nevertheless, we present that model here and show how it can explain the empirical results that we have obtained. We leave it to the reader to evaluate the success of the model. A. CONVENTIONS ADOPTEDI N DEVELOPING T H E MODEL

Some of the implications of our Spencian model of conceptualization were examined with computer simulations of the experiments previously described. Several conventions were adopted in generating these simulations. One of our first decisions concerned how to represent similarities within and between categories. Although many other options were available, we chose t o represent stimuli within a conceptual category as uni-

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formly spaced along a single, common dimension. We refer to such dimensions as “categorical dimensions.” The distance between stimuli along a conceptual dimension is an inverse function of their similarity to one another. The left-right ordering of stimuli along the dimension is arbitrary. To simplify the model, we represented stimuli from different basic-level categories as if they fell along separate and orthogonal dimensions. We do not now have data that would allow us to quantify the similarity relations among the stimuli that we used in the preceding studies. Multidimensional scaling of stimulus similarity has proven to be highly productive in accounting for the behavior of humans categorizing complex visual stimuli (e.g., Nosofsky, 1992); this technique might productively be applied to the sorts of stimuli that we have used in our projects. Our model adopts the Spencian notion that reinforced responding to a stimulus results in excitation accruing to that stimulus. In the model, excitation generated by reinforced responding to one stimulus generalizes to others along the same categorical dimension to the extent that the others are similar to the reinforced stimulus. Likewise, we assume that nonreinforced responding to a stimulus results in inhibition accruing to that stimulus and to similar stimuli on the same categorical dimension. Our simulations represent generalization from one stimulus to another along a categorical dimension as a decaying exponential function of the similarity between them (Shepard, 1987). The model also assumes faster acquisition of excitation than inhibition (see Herrnstein, 1966, p. 37; Rescorla & Wagner, 1972, pp. 85-86); thus, preasymptotically, the inhibitory gradient will be shallower than the excitatory gradient. We use the term “response tendency” to refer to the net value resulting from the combination of excitatory and inhibitory tendencies to an individual stimulus on the categorical dimension. Trial-by-trial changes in excitation and inhibition in our simulations were modeled according to the general principles of the Rescorla-Wagner (1972) model. Specifically, the trial-by-trial changes in excitation and inhibition that accrue to individual stimuli reflect the product of a rate parameter and the distance between the current associative level and the associative asymptote. Two factors affect the level of response tendency in this simulation: (a) directly reinforced or nonreinforced presentations of stimuli, and (b) generalization from neighboring stimuli along the categorical dimension. Approach to asymptote is a function of the summed (direct + generalized) response tendency to a stimulus. Our simulations modeled only the effects of categorical generalization on the tendency to respond to a particular stimulus. In an actual experimental situation, responding will be affected by the specific category to which a stimulus belongs and by any noncategorical attributes (such as

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shape, brightness, location, etc.) that the stimulus shares with others given in the experimental situation. For example, our simulations of the go-no go procedures of Experiment 7 can generally be expected to underestimate the amount of actual responding. This underestimation may be clearest in cases where the simulation results in net inhibition to an S- or to novel stimuli from the S- category, but the actual data show some tendency to respond to them. The last section in this chapter describes the full set of parameters and operations of our computer simulations. Our simulations are not unlike the recent efforts of Gluck (1991) and Shanks (1991) in emphasizing stimulus generalization and in incorporating the Rescorla-Wagner theory of learning. These other simulations are different from our own, however, in that the association of elementaryfearures to categories is their central explanatory principle (also see Medin & Schaeffer, 1978, and Pearce, 1988, for more on related approaches). In our simulations, the stimuli to be categorized are treated as indivisible wh01es.~We are not prepared at this point to make quantitative comparisons between other approaches and our own, nor are we ready to say that our general model incorporates all of the many elements that are necessary to account for complex categorization behavior. We wish only to show how a very simple set of principles with a long history in learning theory can account for a wide range of categorization phenomena.

B. THESIMULATIONS

In this section, we present computer simulations of the experiments previously described. Because it most directly fits the traditional Spencian model of excitation and inhibition, we will first discuss the go-no go procedures used in Experiment 7 and its affiliated investigations. Experiment 7: Go-No Go Discrimination Recall that in this experiment (Astley & Wasserman, 1992, Experiment 2), 12 slides from one category served as the S + s and 12 other slides from the same category served as the S-s, as did 12 slides from each of three different categories. In that experiment, errors were disproportionately committed to S-s from the same category as the S+s. A second experiment (Astley & Wasserman, 1992, Experiment 1) used the same set of 48 S-s, but only a single slide as the S + . Here, the discrimination was learned more quickly than when 12 different slides served as the S+s; in One possible disadvantage of our approach concerns changes in the influence of particular dimensions of stimuli as training progresses; more will be said about this issue later.

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addition, pigeons trained with 1 S + were less likely to peck the samecategory S-s than were pigeons trained with 12 S + s . In these experiments, discrimination training was always preceded by a base-line phase in which responses to all stimuli were nondifferentially reinforced. In our simulations, trial-by-trial increments in response tendency during nondifferential base-line training resulted in the development of excitatory gradients around each training stimulus. Because the baseline phases of these studies were conducted until performance stabilized, the simulation was run until asymptote was reached on each of the individual stimuli. During simulated discrimination training in these two investigations, response tendencies to the S - s declined as inhibitory tendencies accrued to them resulting from nonreinforcement. The S - s that were on the same dimension as the S+(s), however, continued to receive generalized excitation as reinforcement of the S+(s) continued. When a single slide served as the S + , only afew S-s (those most similar to the S + ) received generalized excitation; when 12 different slides served as S + s , however, most of the different S- s from that dimension received some generalized excitation. Figure 1 illustrates this result. The top panel depicts the net response tendencies to the single S + and to the 12 S-s from the same categorical dimension after 16 days of simulated discrimination training. The bottom panel depicts the same net response tendencies when 12 S + s were given. Note the greater response tendency to the same-category S-s when there were 12 S + s than when there was only 1 S+. Because we assume that stimuli from different categories lie along orthogonal dimensions, the decline in response tendency to between-category S- s is unaffected by any generalized excitation (these data are not shown in Fig. 1). Figure 2 depicts the mean response tendencies to the S + (s) and to the within- and the between-category S-s over simulated sessions in the experiments with 1 S + (top panel) and with 12 S + s (bottom panel). As did the actual data, the simulated data show faster acquisition of the overall discrimination with 1 S + than with 12 S + s (the degree of discrimination in these plots is indicated by the d ~ e r e n c ein response tendency between the S+(s) and the S-s; see Figs. 1 and 3 in Astley & Wasserman, 1992). The simulated data also show a greater response tendency to withincategory S-s with 12 S + s than with 1 S+. This result too reflects differences observed in the actual data from these experiments (see Astley & Wasserman, 1992, Tables 1 and 2). In the third experiment of this study, novel stimuli were selected from each of the three S- categories different from that of the S+s; in test sessions, these novel stimuli were intermixed with the training stimuli for birds given 12 S+s. The birds showed a greater tendency to respond to

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the novel S-s than they did to the S-s with which they were trained (see Astley & Wasserman, 1992, Table 5). Our simulation yielded the same result: a greater response tendency was shown to the novel stimuli from the S- category (-66) than to the original S-s (-74). This finding is called inhibitory generalization decrement; it represents the decline in inhibition that results from presentation of test stimuli some distance from trained S-s along the categorical dimension.

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Despite the generally good fit between our simulations and the actual data, we did find two noteworthy discrepancies. First, the Spencian model showed a decline in responding to the S + (s) early in discrimination training because of the generalization of inhibition from the S-s (as depicted in Fig. 2). The actual data showed exactly the opposite result: positive behavioral contrast, in the form of increased responding to the S+ (s) early in discrimination training (see Astley & Wasserman, 1992, Tables 1 and 2). Many authors have noted that Spence’s model of discrimination learning cannot explain behavioral contrast (e.g., Reynolds, 1961, p. 293). If behavioral contrast is to be accounted for, then other mechanisms must be added to the model. Also contrary to our model was the fact that the number of S+s that subjects were given affected their responding to the between-category S- s; specifically, they exhibited more key-pecking to the betweencategory S-s when there were 12 S+s than when there was only 1 S + (see Astley & Wasserman, 1992, Tables 1 and 2). The model expects this variable to have no effect on between-category responding. This outcome is understandable, however, when one considers the nature of the stimuli used in these studies. With photographs, it is likely that raising the number of S + s from 1 t o 12 will increase the probability that one or more of them will resemble a between-category S- . The representation of categories as orthogonal dimensions is clearly limiting and, in this case, allows no mechanism for differential generalization berween basic-level categories based on their perceptual similarity or the number of S+ s. Perhaps a more complex model along the lines of the connectionist account proposed by Shepard and Kannappan (1991) would prove to be better able to cope with these results than our Spencian theory.

C. EXTRAPOLATION TO CATEGORIZATION AS DISCLOSED BY CHOICEPROCEDURES Although the Spencian approach pretty well accommodated the results of the go-no go experiments just described, we wanted to see if it could also explain the results of experiments using other methods, notably our four-choice categorization procedures. Spence’s formulation considers only how excitation and inhibition affect the tendency to make a single response. Extension of the model to the four-choice procedure merely requires that we consider the strength of a connection between a given stimulus and a given response relative to the connection between that stimulus and the three other available response alternatives. So, learned tendencies to make any of the three incorrect alternative responses might be thought of as analogous to inhibition, in that they have a subtractive

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effect on the tendency to make the correct response to a particular stimulus. The effective consequence of this elaboration of the model is that the theoretical correct response tendency now ranged from 25% (an equivalent tendency to respond to all four keys) to 100% (an exclusive tendency to respond to the correct key). I.

Experiment 1: Discrimination and Generalization

In this study (Bhatt et al., 1988, Experiment lB), pigeons increased the percentage of correct choice responses over training sessions in our fourchoice categorization procedure. Their discriminative responding later transferred to novel stimuli drawn from the same four categories. Our simulation did likewise. The simulation was conducted with 10 stimuli in each of the four training categories. The top line in Fig. 3 shows simulated acquisition of the correct response tendency to the training stimuli (see Bhatt et al., 1988, Fig. 1). The correct response tendency began at 25% because the initial subtractive tendencies to make each of the three incorrect responses were assumed to be the same as the tendency to initially make the correct response. We assumed that any unlearned tendency to make erroneous responses remained constant, whereas the connection between each individual stim-

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ulus and the correct response increased over sessions. Thus, acquisition is entirely a function of changes in excitation. The lower line of Fig. 3 shows the simulated correct response tendency to the same set of novel test stimuli were they to be given at different times during simulated discrimination training. Reinforced experience with the training stimuli contributes t o an increasing tendency to make the correct response to the novel test stimuli, a proclivity that never catches up with the tendency to respond discriminatively to the training stimuli. Therefore, a measurable generalization decrement is to be expected after reasonable periods of discrimination training. 2 . Experiment 2: Category Size In this study (Bhatt, 1988), pigeons' speed of acquisition was an inverse function of category size. Their degree of transfer to novel stimuli was, on the other hand, a direct function of category size. Our simulation performed similarly. Figure 4 shows the number of days taken to meet successively stringent criteria for the three groups in the simulation of this experiment. These simulated data closely match the results of the actual experiment in showing that the time to meet the three learning

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criteria rose with increasing numbers of training stimuli per category (see Wasserman & Bhatt, 1992, Fig. 10.2). Figure 5 shows the correct response tendency to the novel test stimuli (10 per category) and to the training stimuli in a test session conducted immediately after the 70% criterion had been met. The simulated results correspond closely with the actual results in showing that increasing the number of stimuli per category in the training set increased discriminative transfer to novel test stimuli (see Wasserman & Bhatt, 1992, Fig. 10.2; in both the actual and the simulated data, accuracy t o the training stimuli was an inverse function of their number). 3 . Experiment 3: Trial-Unique Stimuli

Recall that, in Experiment 3 , category training with a nonrepeating pool of 2000 slides was sufficient to generate a high level of correct responding by the end of 50 daily sessions of training. In Session 51, pigeons’ responding to a subset of five stimuli from each category that had been shown in Session 50 was compared with responding to a completely novel set of five stimuli from each category; their accuracy to the previously seen stimuli exceeded that to completely novel stimuli, but only slightly. Our simulation behaved in much the same way. Our limited computing facility prevented us from simulating the full effect of a nonrepeating set of 500 stimuli per category. Our simulation

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went only to a maximum of 90 different stimuli drawn from 100-stimulus dimensions. The results are depicted in Fig. 6. The top line shows the simulated acquisition of discriminative responding to nonrepeating stimuli as training involves a larger and larger set of prior stimuli. The correct response tendency, here, reflects the total impact of both generalized and direct reinforcement of the training stimuli; that is to say that the correct response tendency includes all of the stimuli given and reinforced to that point of training. The lower line shows the results of simulated novel stimulus tests; these one-time-only tests involve the same five stimuli per category, but they are conducted at different points in the acquisition process. Accuracy to the training stimuli increased from near-chance levels, when the first 10 stimuli were shown, to over 80% when the last 10 stimuli were shown (see Bhatt et a]., 1988, Fig. 3). Comparison of the top and bottom lines in Fig. 6 illustrates the d$ference in response tendency to stimuli in whose presence responding had been reinforced once (members of the training set) and to stimuli that had never been shown o r reinforced (members of the testing set). The slightly higher correct response tendency to past training stimuli reflects the very small effect of a single reinforcement (see Bhatt et al., 1988, Table 9). 4 . Experiment 4: Stimulus Repetition

In this study (Bhatt et al., 1988, Experiment 41, sessions with a repeating set of 10 slides in each of four categories alternated with sessions with

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slides selected from a large nonrepeating set. Pigeons’ performances attained higher levels of accuracy for the repeating slide set than for the nonrepeating slide set. Our simulation did too. Figure 7 shows the simulated correct response tendency to repeating and nonrepeating stimulus sets over sessions. As in the actual data, our simulated data showed substantially faster overall acquisition to the repeating stimuli than to the nonrepeating stimuli (see Bhatt et al., 1988, Fig. 4). In our simulation, this result makes sense because the repeating stimuli benefit both from continued direct strengthening of response tendency through reinforcement and from indirect strengthening through stimulus generalization; the nonrepeating stimuli benefit only from indirect response strengthening through stimulus generalization. Because a different set of nonrepeating stimuli is selected for each simulated session, slight variations in response tendency from session to session are to be expected; as the stimuli vary, so too does the degree of generalize4 response tendency from the previously trained stimuli. This fact accounts for the slight drop in response tendency seen from the fifth to the sixth pairs of sessions in Fig. 7. 5 . Experiment 5 : Categories and Pseudocategories

In this experiment (Wasserman et al., 1988, Experiment 2), birds given true category training acquired discriminative responding far faster than YW

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birds given pseudocategory training. So did our simulation. Figure 8 shows mean correct response tendencies for the category and pseudocategory groups during 30 days of simulated training. The simulated data neatly reflect the actual data (see Wasserman et al., 1988, Fig. 3). In short, the results arise from the fact that within-category generalization engenders correct responding in the category group, whereas this generalization primarily promotes incorrect responding in the pseudocategory group. Recall that follow-up data collected by Bhatt (1988) showed that the disparity between category and pseudocategory groups increased as the number of stimuli per category or pseudocategory increased (the dependent measure was the number of days to a criterion of 70% correct on 2 consecutive days). Figure 9 shows this measure for simulated category and pseudocategory training with 4 and 12 stimuli; these simulated data generally mirror the relations shown in the actual data. The only exception was the equally fast learning seen in the simulation when only 4 stimuli were included in each category or pseudocategory; the actual data showed slightly faster learning by the category group. 6 . Experiment 6 : Split Categories

In this experiment (Wasserman et al., 1988, Experiment 11, pigeons were given training in which four conceptual categories were subdivided, with two different responses out of the four available being assigned to one

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half of the stimuli from each subdivided category-a procedure called split-category training. On this procedure, pigeons attained high levels of discrimination accuracy; in addition, their errors were much more likely to be committed to the key that was correct for other members of the same conceptual category than to either of the other two keys that were correct for members of the other conceptual category that was shown in a training session. Our simulation responded similarly. Figure 10 shows simulated acquisition of the split-category discrimination over sessions (see Wasserman et al., 1988, Fig. I ) . Figure I 1 shows the response tendencies to Split Category 1 stimuli over 2-day blocks of simulated training. In this simulation, the 20 stimuli from a particular conceptual category were allocated to two split categories so that 10 were associated with one response (e.g., R I ) and the other 10 were associated with another response (e.g., R2). Response 3 (R3) and Response 4 (R4) were correct for reporting stimuli from the two other split categories shown in a training session. Errors in this sirnulation are predominantly to the response (R2) associated with other stimuli from the same conceptual category as stimuli from Split Category I (see Wasserman et al., 1988, Fig. 1). Also, the tendency to make the incorrect response associated with stimuli from the same conceptual category (R2) declined only after reaching a peak on the seventh 2-day block of training. At this point, within-category generalization was overtaken by within-category discrimi-

Fig. 9. Mean days to criterion for simulated category and pseudocategory training with

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nation (see Wasserman et al., 1988, Fig. 2 for corroborative results from a Markov model of split-category discrimination). D.

APPLICABILITY OF THE MODEL

With relatively minor exceptions, then, our elaboration of Spence’s (1937) model of discrimination learning fits a substantial body of evidence on basic-level conceptualization by pigeons in both go-no go and forcedchoice situations. Further tests of the model await additional empirical investigations.

VI. Conceptualization via Primary and Secondary Stimulus Generalization We have in this chapter pursued the utility of Keller and Schoenfeld’s behavioral analysis of concepts in terms of the joint operation of discrimination and generalization. We have also seen that this behavioral analysis relies heavily on the perceived similarity of stimuli, so that perceptually similar stimuli may be the basis for the creation of categories or concepts. This possibility brings us to an interesting and controversial point in the behavioral analysis of concepts because many current theorists, including Herrnstein (1990), Lea (1984), and Murphy and Medin (1985), have questioned the completeness of an account grounded in perceptual similarity. Murphy and Medin (1983, for example, have argued that people’s rough theories and practical knowledge of the world lead them to impose more structure on concepts than similarity alone would allow. Although potentially applicable to an analysis of human behavior, the relevance of this particular critique of similarity to animal behavior is not clear. (For more on the issue of similarity and concepts, see Medin, Goldstone, & Gentner, I993.) In a different and possibly more pertinent vein, Lea (1984; see also Watanabe, Lea, & Dittrich, 1993) has proposed that a concept comprises stimuli that are bound together by relations that are not based solely on perceptual similarity. He argued-as did our colleagues Cantor and Spiker-that if responses learned to some members of a category transfer to others, and if this transfer is based only on perceptual similarity, then the very idea of a concept is superfluous; primary stimulus generalization alone can completely explain transfer from some category members to others. Herrnstein (1990) has amplified Lea’s proposal by saying that in the acquisition of concepts learning a response to some members of a heterogeneous set of stimuli should ideally “propagate to all members of the set, without regard to similarity” (p. 150).

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If we accept Lea’s proposal that concepts entail collections of stimuli whose coherence represents something more than perceptual similarity, then just what is that “something more” that allows the aggregation of perceptually dissimilar stimuli? One interesting possibility is that stimuli that are simply associated with the same response or context will come to be classed together-despite their perceptual dissimilarity. As an illustration, because of a common response (tables and rugs are both called “furniture”) and a common context (tables and rugs are usually sold in furniture stores), perceptually different stimuli (tables and rugs) come to be grouped into a common class or concept (furniture). Such perceptually heterogeneous collections are quite familiar and are termed superordinate categories (Rosch & Mervis, 1975). Significantly, the limits of a similarity-based approach to concepts were actually noted decades ago by Keller and Schoenfeld (1950), whose behavioral analysis has been the theoretical centerpiece of this chapter. They argued that secondary or mediated generalization may play a large role in human conceptual behavior. Secondary or mediated generalization occurs when perceptually different stimuli like the words urn and uase are associated with a common set of objects, thereby making one verbal stimulus the functional equivalent of the other (see Goldiamond, 1966, pp. 214-215 for the related distinction between topographical vs. functional classes of stimuli). According to Keller and Schoenfeld (1950), “Generalizations are said to be mediated when they are based upon a stimulus equivalence which results from training” (p. 160). Just what kinds of training support learned stimulus equivalence? Here, Keller and Schoenfeld are not very specific. They do say that “Classes of objects or events, differently responded to, develop different concepts” (p. 155). So, different stimuli associated with the same contingency of reinforcement should presumably become functional equivalents of one another. And, although they discuss functional stimulus equivalence exclusively in connection with human verbal behavior, there is no good reason why Keller and Schoenfeld should limit nonsimilarity-based conceptualization to human adults. VII.

Nonsimilarity-Based Conceptualization

If we grant that adult human conceptual behavior may involve nonsimilarity-based transfer via secondary or mediated generalization, then we should ask whether there is any strong empirical support for the possibility that nonhuman animals exhibit nonsimilarity-based conceptualization (for negative evidence, see Bhatt & Wasserman, 1989). In one of the clearest

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cases to date, Vaughan (1988) found that, after many discrimination reversals, pigeons came to treat members of each of two 20-item slide sets as functional equivalents of one another. Importantly, all 40 slides depicted trees; thus, the 20 items in each set were random assortments, with no obvious perceptual “glue” to bind them together into two classes entailing greater intraclass than interclass similarity. Other evidence of learned stimulus equivalence in pigeons given very small stimulus sets of highly abstract stimuli was reported by Urcuioli, Zentall, Jackson-Smith, and Steirn (1989) and by Zentall, Steirn, Sherburne, and Urcuioli (1991; for related data in rats, see Hall, Ray, & Bonardi, 1993). A.

EXPERIMENT 8: JOINT CATEGORY LEARNING BY PIGEONS

We (Wasserman, DeVolder, & Coppage, 1992) were quite encouraged by these prior successful studies and therefore undertook a project using substantial collections of lifelike stimuli that addressed the issue of superordinate conceptualization in pigeons via secondary or mediated generalization. Our experiment entailed a three-step procedure (cf., Urcuioli et al., 1989, Experiment 2). First, pigeons were trained to sort 12 different stimuli from each offour basic-level categories (people, flowers, cars, and chairs) into two arbitrary aggregations; Response I was reinforced in the presence of members of Categories 1 and 2, whereas Response 2 was reinforced in the presence of members of Categories 3 and 4. It is during this step of training that members of Categories 1 and 2 and members of Categories 3 and 4 might come to be merged into two classes of functionally equivalent, but perceptually different stimuli. Second, pigeons were trained to make new responses to members of Categories 1 and 3 only, with Response 3 being correct to members of Category 1 and Response 4 being correct to members of Category 3. Although only stimuli from Categories I and 3 were reassigned to new responses during this second step of training, stimuli from Categories 2 and 4 might also have been affected if the training in the first step were effective in establishing stimuli from Categories 1 and 2 as one aggregate group and stimuli from Categories 3 and 4 as a second aggregate group. The third and final step tested the pigeons for their tendency to make Response 3 versus Response 4 to stimuli from Categories 2 and 4, the two classes of stimuli withheld from the second step of training. Even though never before taught to make Response 3 or Response 4 to stimuli from Categories 2 or 4, the clear expectation from joint category formation in the first step would be that birds should predominately make Response 3 to stimuli from Category 2 and Response 4 to stimuli from Category 4.

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Eight pigeons served in the experiment that was conducted in the same four-key boxes used in all of the research reported in this chapter. As stimuli, we used 48 different color slides: 12 each of people, flowers, cars, and chairs. Each of the slides contained only one example of the category, except for flowers, in which the slides contained no more than five examples. We chose these particular slides because our prior work (Astley & Wasserman, 1992; the present Experiment 7) had disclosed their intraclass similarity to exceed their interclass similarity. In overview, the experiment comprised eight phases: Original Training 1 (28 sessions), Reassignment Training 1 (28 sessions), Testing 1 (4 sessions), Original Training 2 (28 sessions), Reassignment Training 2 (24 sessions), Original Training 3 (12 sessions), Reassignment Training 3 (8 sessions), and Testing 2 (4 sessions). The first three phases represent the minimum number needed to complete the three-step experimental design. The remaining five phases sought to enhance the (already statistically reliable) results that were obtained in the first test phase. Daily sessions of original training comprised 48 two-choice trials. Following intertrial intervals averaging 15 sec, the rotary projector advanced one slot to reveal a slide from one of four different classes: C1, C2, C3, or C4. The first peck to the screen key after 16 sec of slide exposure lit the top right (Rl) and bottom left (R2) report keys. Choice of R1 was correct if the slide depicted members of C1 or C2; choice of R2 was correct if the slide depicted members of C3 or C4; all other choices were incorrect. Correct choices were followed by food reinforcement; incorrect choices were followed by as many correction trials as were necessary for a correct response to be emitted. To control for the effects of interclass similarity, four pigeons received as C1, C2, C3, and C4, respectively, slides of flowers, chairs, cars, and people, and four other pigeons received as C1, C2, C3, and C4, respectively, slides of people, flowers, chairs, and cars. Twice daily sessions of reassignment training each comprised 24 twochoice trials. Here, only the top left and bottom right report keys were lit as choice alternatives. To control for horizontal versus vertical reassignment of report keys from the original training locations, one half of the pigeons in each stimulus assignment subgroup received horizontal reassignment of R3 to C1 and R4 to C3 and the other half received vertical reassignment. For all pigeons, choice of R3 was correct if the slide depicted members of C1; choice of R4 was correct if the slide depicted members of C3; all other choices were incorrect. Again, incorrect choices necessitated correction trials. Daily sessions of testing comprised 48 two-choice trials. As in reassignment training, only the top left and bottom right report keys were lit as

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choice alternatives, and differential reinforcement (with correction trials) was scheduled on trials involving slides from C1 and C3; R3 was correct to C1 and R4 was correct to C3. Nondifferential reinforcement was scheduled on trials involving slides from C2 and C4; food was given on all C2 and C4 trials irrespective of the choice of R3 or R4. Nondifferential reinforcement was given to guarantee that differential responding to C2 and C4 stimuli was not the result of explicit training effects during testing; repeated testing could then be conducted without confounding. Only nominally, then, can R3 to C2 stimuli and R4 to C4 stimuli be considered to be “correct” responses, as they were to determine the formation of joint categories. As a detailed account of the results of the experiment is available (Wasserman et al., 1992), we will focus here on the last three phases when discriminative responding was strongest: 1. In the final four-session block of Original Training 3, the mean percentage of correct choice responses was 89%. 2. In the final four-session block of Reassignment Training 3, the mean percentage of correct choice responses was 89%. 3. Across all four sessions of Testing 2, the mean percentage of correct choice responses to reassigned stimuli (Cl and C3) was 87% and to nonreassigned stimuli (C2 and C4) it was 72%. For both reassigned and nonreassigned stimuli on each test session, including the first, obtained accuracy reliably exceeded the chance accuracy score of 50%. Accuracy to reassigned stimuli (C I and C3) reliably exceeded accuracy to nonreassigned stimuli (C2 and C4); as well, the drop in accuracy from reassigned (C1 and C3) to nonreassigned (C2 and C4) stimuli was reliably greater for the subgroup of pigeons with both natural and artificial stimuli in each joint category (87-64%) than for the subgroup of pigeons with only natural or artificial stimuli in each joint category (87-80%). Additional analyses confirmed the reliability of the reassigned-nonreassigned decrement in each case. Across all four test sessions, obtained accuracy reliably exceeded chance accuracy for both reassigned and nonreassigned stimuli in each subgroup of pigeons. Finally, as to individual subject performance, across all four test sessions, the obtained accuracy of all eight pigeons to reassigned stimuli reliably exceeded chance accuracy; the obtained accuracy of six pigeons (all four from the subgroup with only natural or artificial stimuli in each combined category) to nonreassigned stimuli did so.

This three-step procedure, therefore, proved highly effective in producing and disclosing nonsimilarity-based conceptualization in pigeons. Merely by being associated with a common response in the first step,

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classes of perceptually dissimilar stimuli, like cars and chairs, appear to amalgamate into a new category of functionally equivalent stimuli. Thus, requiring a new response to be performed to only one of these two stimulus classes in the second step now brings about transfer to the other stimulus class in the third step. This transfer can be quite substantial (78% correct on the first day of the second test phase), but it cannot readily be explained in terms of primary stimulus generalization because of the counterbalancing of stimulus classes constituting the joint categories. Instead, appeal to mediated or secondary stimulus generalization appears to be necessary to account for this form of conceptual transfer. Saying that the experiment constitutes a clear case of nonsimilaritybased conceptualization does not mean that perceptual similarity played no part in the obtained results. First, choice accuracy was higher to reassigned stimuli (CI and C3) than to nonreassigned stimuli (C2 and C4). At least part of that difference might plausibly be attributed to primary stimulus generalization decrement; C1 and C3 stimuli and C2 and C4 stimuli are indeed perceptually different from one another (Astley & Wasserman, 1992). Second, the drop in accuracy from reassigned (Cl and C3) to nonreassigned (C2 and C4) stimuli was greater for pigeons with both natural and artificial stimuli in each joint category. This result might be accounted for by smaller discriminal distances between people and flowers or between cars and chairs than between the remaining pairs of stimulus classes. However, this pattern of discriminal distances was not confirmed by accuracy scores during the three phases of original training, where one would expect faster learning with joint categories comprising only natural or artificial stimuli than with joint categories comprising both natural and artificial stimuli. No statistically reliable differences in performance between the subgroups of pigeons were observed in any of the three phases of original training. We recognize that a related form of stimulus generalization might be implicated in the transfer of control from reassigned to nonreassigned stimuli. This transfer may involve stimulus elements that are common to members of C1 and C2. Perhaps, even though stimuli from C1 and C2 involve greater intraclass than interclass similarity, some aspect(s) of those stimuli may be shared with one another, but not shared by stimuli from C3 and C4. In a parallel fashion, some aspect(s) of stimuli from C3 and C4 may be shared with one another, but not shared by stimuli from C1 and C2. Successful transfer may then represent control by some common element(s) present in stimuli from either of the pairs of basic-level categories. Although we accept the possibility of this “common elements” account, we consider it t o be ad hoc and of limited heuristic value. Most prob-

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lematical is the fact that our pigeons showed effective transfer of control from reassigned to nonreassigned stimuli with two different C1 + C2 and C3 + C4 assignments: one being flowers + chairs and cars + people and the other being people + flowers and chairs + cars. Anyone espousing the common elements interpretation will have to provide realistic and independently manipulable candidates for such stimulus control before this account can be taken seriously. These results and arguments notwithstanding, whatever other features of the present data can be explained by primary stimulus generalization, the significantly above-chance overall accuracy scores obtained to nonreassigned stimuli appears to be largely inexplicable by this interpretation. Nor can response generalization account for the differential tendencies to make R3 to C2 stimuli and R4 to C4 stimuli during test sessions. By locating R1 and R2 along the positive diagonal of response keys and by locating R3 and R4 along the negative diagonal, we guaranteed that R1 and R2 were equidistant from R3 and R4, thus eliminating any systematic tendency for one or the other test response (R3 vs. R4) to be exhibited to either class of nonreassigned stimuli (C2 and C4). A final procedural safeguard was for an equal number of birds to have R3 and R4 horizontally or vertically relocated from RI and R2.

B. EXPERIMENT 9: JOINTCATEGORY LEARNING BY PRESCHOOL CHILDREN We were both pleased and surprised by the magnitude and the robustness of our pigeons’ nonsimilarity-based conceptual behavior. But, without a basis of comparison, we were unsure of just how pleased and surprised to be. Therefore, we undertook a parallel project with preschool children given a photograph-sorting task to assess the generality of our results with pigeons. This project by DeVolder, Lohman, and Wasserman (reported in Wasserman & DeVolder, 1993) followed exactly the same logic and plan as the pigeon project just described. The subjects were 20 children recruited from a preschool in a large Midwestern community. The children’s ages ranged from 4 years, 5 months, to 5 years, 10 months, with a mean age of 4 years, 10 months. Half of the children were boys and half were girls. The children were paid for their participation with a $ I gift certificate at an ice cream parlor. A large white poster board laminated in clear acrylic to prevent soiling served as the response board. There were four rectangles outlined in each quadrant of the board and one rectangle located centrally. During each phase of the experiment, two diagonal rectangles were covered by large X s , making them unavailable. The same 48 snapshots used in the pigeon

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experiment were used here. These photographs were laminated in clear acrylic to prevent damage from handling by the experimenter and the children. There were three phases in this experiment: 1. In Original Training, children received a total of 48 trials. On each trial, the experimenter placed a photograph in the center rectangle and asked the child to place it in the correct choice location. During this phase, the top left and bottom right locations were covered, leaving only the top right and bottom left rectangles available as choice responses. Correct responses were verbally reinforced (“that is correct”) and incorrect responses were verbally punished (“that is incorrect”); no correction trials were given here or in any of the two following phases. Stimulus and response assignments followed the same plan used in the prior pigeon project. Original Training involved photographs from C1, C2, C3, and C4, and continued for 48 trials for all children; by this time, each child had reached the 90% correct level during at least one 12-trial training block. 2. Immediately after Original Training, each child underwent Reassignment Training during which the top right and bottom left response locations were covered, leaving only the top left and bottom right locations available as choice responses. Reassignment Training utilized only two of the stimulus classes (Cl and C3) from Original Training and comprised a total of 24 trials; by this time, each child had reached the 90% correct level during at least one 12-trial training block. 3. After completion of Reassignment Training, each child was shown each of the 48 photographs one time each during a single Testing period. As in Reassignment Training, only the top left and bottom right rectangles were available as choice responses. As in the two prior phases, differential reinforcement was given for trials involving photographs from C I and C3. Nondifferential positive reinforcement (“that is correct”) was given for trials involving photographs from C2 and C4.

During Testing, the mean percentage of correct choice responses to reassigned stimuli (C1 and C3) was 99% and to nonreassigned stimuli (C2 and C4) it was 80%. For both reassigned and nonreassigned stimuli, obtained accuracy reliably exceeded chance accuracy. The drop in accuracy from reassigned (C1 and C3) to nonreassigned (C2 and C4) stimuli was also reliable; however, the assignment of stimuli to joint categories was not a reliable determinant of the children’s performance. Finally, as to individual subject behavior, the obtained accuracy of all 20 children to reassigned stimuli reliably exceeded chance accuracy; the obtained accuracy of 13 children (6 from the subgroup with only natural or artificial stimuli in each combined category) to nonreassigned stimuli did so.

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Compared with the performance of our pigeons, our children mastered the tasks they were given far faster and reached even higher levels of discriminative behavior; nevertheless, their behavior to reassigned and nonreassigned stimuli in testing was rather similar. Mean first-session accuracy of pigeons and children to reassigned stimuli was 88% and 99%, respectively; mean first-session accuracy of pigeons and children to nonreassigned stimuli was 78% and 80%, respectively. All of the pigeons and children responded reliably above chance levels to the reassigned stimuli; 75% of the pigeons and 65% of the children responded reliably above chance levels to the nonreassigned stimuli. More importantly, in each case, the evidence was quite clear that association with a common response was sufficient for stimuli from perceptually different classes to join into a single class of functionally equivalent stimuli. Because of the design of the pair of experiments, the transfer of training from reassigned to nonreassigned stimuli is best interpreted as representing nonsimilarity-based conceptualization. C.

EXPERIMENT 10: CHILDREN’S JOINTCATEGORY LEARNING SIMULATED HUMANFACES

WITH

We now describe early data from an ongoing project with slightly older children. Our goal in this project, conducted by the second author and Paul Norwood, is to extend the range of stimuli to which the three-stage joint categorization procedure has been applied and to use naturalistic stimuli over which we can exert better experimental control than the snapshots that we used in prior research. Additionally, we wish to see if the effects of joint categorization training will extend to novel stimuli drawn from the component categories. The stimuli that we used in Pretraining and in the Original Training and Reassignment Training phases were 16 simulated human faces created with Mac-a-Mug software (Shaherazam Software, Kalamazoo, MI). The faces were black line drawings on a white background. There were four faces in each of four “families.” The members of each family had the same hair and chin, but each individual had unique eyebrows, eyes, nose, and mouth. The interior features of each face were unique; no other face shared those specific features. All of the faces had the same ears. All four faces from each of the four families are shown in Fig. 12. For Testing, two novel faces in each of the four families were created (they are not shown in Fig. 12). Like the family members used in Original Training and in Reassignment Training, the novel faces had the same family hair and chin types, but they had unique eyebrows, eyes, noses, and mouths. The subjects were children between the ages of 5 and 7, who were individually tested at several day care centers. The stimuli were presented

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on an 11-in., black-and-white computer monitor; the children were asked to respond to the faces by pointing directly at a location on the screen. The children’s responses were followed by the experimenter’s manipulation of the computer mouse to indicate the location to which the child had pointed. As pilot testing had found this joint-category task to be hard for children of these ages to learn, we preceded Original Training with special Pretraining (described in the next paragraph) and with exposure to the Reassignment Training procedure. We suspected that these measures might aid learning in Original Training. Other measures we took to facilitate learning were to give two exposures to Original Training and to Reassignment Training, and to prompt correct responses on the first four trials of the first exposures to Original training and Reassignment training. In Pretraining, the children were first shown the hair and chin attributes (with no interior features) characteristic of each of the four families; they were told that they would see “boys” from each of these families. Then, they were shown each of the faces, one at a time, at the top of the screen and the four family outlines (hair and chin) at the bottom of the screen. The children were asked to match (by pointing) the face at the top of the screen with the correct family below. The children responded at a very high level of accuracy in this task. Before starting the main portion of the experiment, the children were informed that they would be playing a new game in which their job was to help the same boys whom they had seen earlier to get home for dinner. Each trial in Original Training began with the presentation of one of the 16 faces (approximately 2 in. wide and 3 in. high) in the middle of the computer screen. After 1 sec, line drawings of two houses appeared as well, one in the upper left corner of the screen and the other in the lower right corner. One of the houses was correct for two of the families and the other was correct for the other two families. The joint categories of families were counterbalanced, so that the upper left response (Rl) was correct for Families 1 and 2 for half of the subjects and for Families 1 and 4 for the other half of the subjects. Correct pointing responses were followed by auditory feedback from the computer; a cartoon-like voice said, “I’m home, what’s for dinner?” Incorrect choices were also followed by auditory feedback from the computer; on these occasions, the computer voice said, “Oh-oh, this isn’t my home.” The experimenter also verbally emphasized the correctness or incorrectness of the children’s choices. In Reassignment Training, only faces from Families 1 and 3 were shown, this time in conjunction with two different houses that were not available in Original Training. One house appeared in the upper right corner of the computer screen and the other appeared in the lower left corner. One of the houses was designated as correct for faces from Family 1 and the

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other house was correct for faces from Family 3. Correct choices were followed by the same computer and experimenter feedback as in Original Training. At the beginning of Testing, the children were told that they would be playing a game like the one that they had just played (in Reassignment Training), except that they would now see boys from all four of the families and some new boys too. In the 56-trial Testing period, there were four presentations of each of the four faces from Families 1 and 3, with correct responses and consequences arranged as in Reassignment Training. There were also two presentations of each of the four faces from Families 2 and 4, and subjects received positive reinforcement after choices in the presence of these stimuli no matter which house they selected. Also in the Testing period was a single presentation of each of the two novel faces from each of the four families, again with choices nondifferentially reinforced by computer and experimenter feedback. Testing trials were organized in blocks of seven, with each block comprising four trials with two different faces from each of Families 1 and 3, two trials with one face from each of Families 2 and 4, and one trial with a novel face from one of the four different families. The order of training phases was Reassignment Training I , Original Training I , Original Training 2 , Reassignment Training 2, and Testing. Correct responding was prompted on the first four trials of Reassignment Training 1 and Original Training 1 . There was no prompting during Original Training 2 , Reassignment Training 2 , or Testing. Pretraining and all other phases of the experiment were completed within a single session that lasted from 30 to 90 min. The data provided good evidence of joint category formation. All 10 children evidenced highly discriminative performance in both Original Training 2 and in Reassignment Training 2 . In Testing, the children achieved a mean accuracy score to the reassigned stimuli (Families 1 and 3) of loo%, indicating that they maintained differential responding to these stimuli. To the nonreassigned stimuli (Families 2 and 4),subjects achieved a mean accuracy score of 86%, indicating that they had indeed formed joint categories in Original Training. Accuracy t o the novel faces first shown in Testing closely resembled accuracy to the familiar faces seen in Original Training and in Reassignment Training. Accuracy to novel faces from the reassigned categories (Families I and 3) averaged 100%; accuracy to novel faces from the nonreassigned categories (Families 2 and 4) averaged 82%. Thus, the results of this project not only provide clear evidence of joint category formation with a different kind of complex visual stimulus than photographs of real-world objects, they also demonstrate transfer of discrimina-

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Fig. 12. Faces in the four family groupings of Experiment 10. Different families are shown in the different columns; different individuals in those families are shown in the different rows. Families I , 2, 3. and 4 are depicted in Columns I , 2, 3, and 4,respectively.

tive control to novel stimuli from the reassigned and nonreassigned categories. VIII. Summary of Empirical Evidence Before making our concluding comments, it may prove to be useful for us to summarize the main empirical findings that we have presented in this chapter.

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1 . The pigeon can be taught to classify color slides of natural and humanmade objects into four mutually exclusive and exhaustive stimulus classes. This categorical discrimination learning can proceed quite rapidly and with similar speed for both natural (cats and flowers) and humanmade (cars and chairs) stimuli (Experiment 1). Categorical discrimination learning is faster the fewer examples per category are shown (Experiment 2) and if stimulus repetition is permitted from day to day (Experiment 4). Nevertheless, categorical discrimination learning is possible even when the slides that are shown to the pigeon are never repeated (Experiments 3 and 4). Finally, categorical discrimination is far faster if the training categories are isomorphic with human conceptual categories than if they are not (Experiment 5 ) . 2. After categorical discrimination learning, the pigeon generalizes its classification behavior to novel slides (Experiment 1). Categorical generalization is broader the more examples per category are given in prior discrimination training (Experiment 2 ) . There is a strong perceptual component to the pigeon’s categorical discrimination and generalization. Without any differential reinforcement of categorization behavior whatsoever, the pigeon shows a clear tendency to respond similarly to stimuli from the same human conceptual categories (Experiment 7). Furthermore, the pigeon’s within-category errors in a split-categorization procedure exceed its between-category errors (Experiment 6). 3. All of this (and other subsidiary) evidence can be encompassed by a modification of Spence’s ( 1937) theory of discrimination learning. The key modification is that stimuli from a single conceptual category lie along a common psychological dimension. Still, there is more to the pigeon’s conceptual behavior (and to the human being’s as well). Collections of stimuli from two different perceptual groupings (like cars and chairs) can be forged into a single functional class by associating them with a common operant response (Experiment 8). Such superordinate category formation would appear to require behavioral mechanisms that transcend primary stimulus generalization. Invoking secondary or mediated stimulus generalization to account for nonsimilarity-based conceptualization is well justified from a behavioral vantage point. And, mediated generalization can be effectively applied to the very similar results of analogous experimentation with young children (Experiments 9 and 10). IX. Concluding Comments “[EJquivalent stimuli is what we mean when we speak of a concept” (p. 155). These words of Keller and Schoenfeld (1950) place the study of concepts squarely within the experimental analysis of behavior. Neverthe-

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less, the research discussed in this chapter makes it clear that the origins of stimulus equivalence may be quite different. On the one hand, stimulus equivalence may result from the perceived similarity of stimuli. In this case, primary stimulus generalization may be said to support the transfer of discriminative responding from some stimuli to others. On the other hand, stimulus equivalence may result from prior experience. In this case, secondary or mediated stimulus generalization may be said to support the transfer of discriminative responding from some stimuli to others. Pigeons and children alike are capable of similarity-based and nonsimilarity-based conceptualization; but, in the case of similarity-based conceptual behavior differential reinforcement appears to disclose preexisting concepts, whereas in the case of nonsimilarity-based conceptual behavior it appears to produce new ones. A.

A MEDIATIONAL ACCOUNT OF NONSIMILARITY-BASED CONCEPTUALIZATION

Our demonstration of nonsimilarit y-based conceptualization in pigeons and children brings us to the nature of the behavioral processes involved in this transfer. Here, there is good reason to point to the importance of the common operant behavior that was performed to members of the joint categories. Thus, R1 was the common response to members of both C1 and C2 during Original Training. Even though R1 was unavailable during Reassignment Training, when R3 was required to members of C1, the partial or incipient performance of that initial response (rl) and the propnoceptive and kinesthetic feedback ( s l ) that may be produced by it could have become a portion of the complex of discriminative stimuli associated with R3. The later presentation of members of C2 during Testing, even though never before paired with R3, could have effectively led to the performance of R3 because these stimuli were associated with some of the discriminative stimulus complex hypothesized to be associated with R3. The full theoretics of this mediational account are elaborated in Table I. This table details all of the experimentally available stimuli and responses in uppercase letters and all of the hypothesized stimuli and responses in lowercase letters for all three phases of experimental training. Most critical for the mediational account is its accurate prediction of the emergent stimulus-response associations that are observed in Testing: namely, the performance of R3 to stimuli in C2 (via the mediating chain C2 + r l + sl + R3) and the performance of R4 to stimuli in C4 (via the mediating chain C4 + r2 + s2 + R4). Such accurate prediction necessitates the training given during bofh of the first two phases of training;

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TABLE 1 MEDIATIONAL ANALYSISOF NONSIMILARITY-BASED CONCEPTUALIZATION Original training CI + r l + s l + R I C2 -+ rl + sl + R I C3 + r2 + s2 -+ R2 C4 + r2 -+ s2 + R2

Reassignment CI

+

rl

C3 + r2

+

sl

-+ s2

+

-

Testing R3 C2

--f

rl

+

sI

--+

R3 or R4?

R4

C4 + r2 + s2 -+ R3 or R4?

N o t e . C1, C2. C3. and C4 refer to the four categories of stimuli shown to the subjects; R1. R2, R3. and R4 refer to the responses availahle to the subjects: r l . r2. r3. and r4 refer to the hypothesized mediating responses; $ 1 . s2. s3. and 54 refer to the hypothesized stimulus consequences of each of the four mediating responses. Boldface responses in testing indicate those that would evidence mediated generaliration.

otherwise, there would be no basis for effective mediation of the emergent stimulus-response relations tested in the final phase. It might occur to some readers that mediational mechanisms of the sort discussed by earlier authors like Osgood (1953), Kendler and Kendler (1962), and Underwood (1966) must surely be out of date. Yet, Herrnstein (1990) too has recently addressed the relevance and importance of mediated generalization and mediated association for comprehensive analyses of conceptual behavior: The terms were used to describe transfer of conditioning across stimuli too different to be attributed to common features or attributes. The theoretical notion was that stimuli may have had no stimulus elements in common. but, presumably because of past conditioning. they evoked overlapping responses. The overlapping responses provided the bridge for transfer. (p. 150)

Mediational mechanisms have proven to be interpretively valuable well beyond the present context of conceptual behavior. For example, Sanders ( 197 1 ) interpreted both species differences and developmental differences in discrimination learning as resulting from differences in mediating responses. Those wishing to learn more about the application of mediational mechanisms to other issues in cognition should consult Peterson ( 1984) and Heit (1992).

B.

VERISIMILITUDE VERSUS CONTROLLABILITY OF DISCRIMINATIVE STIMULI

Some comment is in order concerning our choice of lifelike snapshots and drawings to be discriminated by our pigeon and children subjects. The

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use of naturalistic stimuli substantially increases the representativeness of our results to real life situations; however, it also entails a lack of experimental control that would otherwise be highly desirable. We do not know all that we would like to about the perceptual similarity of the present stimuli, but we do know and can easily demonstrate that, in our studies of nonsimilarity-based conceptualization, unequal discriminal distances among our four stimulus classes are as likely to produce belowchance transfer to new stimuli as they are to produce above-chance transfer. That is why, in the absence of detailed psychophysical information, we must rely on counterbalancing of the stimulus classes constituting the joint categories to neutralize any biases in the transfer performance we obtain. In future studies, we plan to establish composite categories of stimuli, like letters of the alphabet, with known discriminal distances between one another (Blough, 1985).

C. KINDSOF CONCEPTUAL CATEGORIES Another matter for discussion concerns the place of our results within the familiar tripartite scheme: subordinate concept, basic-level concept, and superordinate concept (Rosch, Mervis, Gray, Johnson, & BoyesBraem, 1976). This scheme proposes that human conceptual categories can be located at three levels depending on the relative extent of intraclass and interclass stimulus similarity. The basic-level concept, like chair, enjoys both high intraclass similarity and low interclass similarity. The subordinate concept, like dining chair, entails much higher interclass similarity than the basic-level concept. The superordinate concept, like furniture, entails much lower intraclass similarity than the basic-level concept. The joint categories established in two of our final three experiments and in the experiment of Vaughan (1988) fit well within this scheme. Each project began with basic-level categories: Vaughan chose 40 photographs of trees and we chose 12 photographs each of people, flowers, cars, and chairs. Vaughan proceeded to establish two subordinate categories, each comprising assortments of 20 photographs, with each of six pigeons trained with different assortments. We proceeded to establish two superordinate categories, each comprising two basic-level categories, with each of two different groups of pigeons or children trained with different pairs of basic-level categories. Each pigeon study found clear evidence that new categories of functionally equivalent stimuli were formed, thereby supporting the view that much of the richness and complexity of human conceptual behavior is to be found in the behavior of nonhuman and nonverbal animals (see also Roberts & Mazmanian, 1988). Just what verbal behavior adds to conceptualization now becomes a matter of special interest, given that

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our own results conform well to the principle of secondary or mediated generalization-yet our pigeons’ behavior (unlike that of our children) must surely not have involved verbal mediation.

D. CONCEPTS A N D LANGUAGE Our pigeon data suggest that a good many of the details of conceptualization-both similarity-based and nonsimilarity-based-can be exhibited by the pigeon, a nonhuman and nonverbal animal. In addition, the behaviors of our preschool children look very much like those of our pigeons. Although it would be downright silly to insist that there are no differences in the conceptual behaviors of these vastly different organisms, we are quite impressed with the similarities that we have so far observed. At the very least, these resemblances indicate that complex conceptualization requires neither language nor the human brain.’ Even if it were to be shown that language importantly influences conceptualization, there is no need to abandon a behavioral analysis. The very terms of our language may have been learned by the same process that is responsible for the learning of nonverbal concepts. At least that was the premise behind our choice of the name game as the human concept learning context on which we fashioned our pigeon conditioning analogue. And, it was the premise behind Quine’s (1969) suggestion that learning the meaning of such intangible terms as yellow through the rough and ready route of practical experience also requires no new principles: All these delicate comparisons and shrewd inferences about what to call yellow are, in Sherlock Holmes’s terminology. elementary. . . . It is the same process by which an animal learns to respond in distinctive ways to his master’s commands or other discriminated stimulations. (p. 10)

E. A TAXONOMY OF STIMULUS CONTROL Our findings also relate to a recent taxonomy of stimulus control proposed by Herrnstein (1990). Open-ended categorization, in his scheme, is what we have termed similarity-based conceptualization. “Any natural contingency of reinforcement is . . . likely to be open-ended . . . , comprising a virtually limitless set of exemplars from the subject’s vantage point . . . [and] the inevitability of this demand, and its solution by perceptual Our discussion of conceptualization in pigeons and children has centered on categorization behavior. Other work (Heit, 1992; Osherson, Smith, Wilkie. Lopez. & Shafir, 1990) has also concerned behavioral inferences of the form, “if this object is a bird, then it is likely to fly.” Whether such inferences can be shown in the behavior of nonhuman animals is an intriguing, but as yet unexplored issue.

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similarity, are what suggest that similarity is an evolutionary adaptation” (p. 144). A concept, in Herrnstein’s scheme, is what we have termed nonsimilarity-based conceptualization. Here, “the effects of contingencies [of reinforcement] applied to members of the [stimulus] set propagate to other members more than can be accounted for by the similarities among members of the set” (p. 150). Herrnstein notes that his level of concept relates to earlier authors’ notions of secondary or mediated generalization. Whereas most of those authors linked such mediation to human verbal behavior, Herrnstein does not. To him, “the issue is simply whether analogous examples of categorization can be convincingly shown for nonverbal animals” (p. 150). Our pigeon data and those of Vaughan (1988) seem to do just that. Our data also force us to update Premack’s (1983) appraisal that “pigeons have never been shown to have functional classes-furniture, toys, candy, sports equipment-where class members do not look alike; they only recognize physical classes-trees, humans, birds-where class members do look alike” (p. 359).

F. PERCEPTUAL SIMILARITY A N D MODELS OF CATEGORIZATION Similarity has been an important explanatory construct in many theories of human conceptual behavior. Classical, prototype (family resemblance), and exemplar models all rely to some extent on similarity t o explain how categories are formedand how new instances are classified (e.g., Komatsu, 1992). The work in the latter half of this chapter concerned a nonsimilaritybased associative mechanism of categorization. This mechanism may prove to be a powerful addition to current models of categorization (see Nosofsky, 1992). Much about the relative contributions of similarity- and nonsimilaritybased associative mechanisms to categorization must still be determined. However, some initial remarks are in order here. First, although it was not included as a factor in our own computer simulations, we believe that it is quite possible that the perceived similarity of stimuli may change with an organism’s experience with them or with like stimuli. As Smith and Heise (1992) have recently noted, the dynamic nature of perceptual similarity has been a central concern of work ranging from Nosofsky’s (1984) account of categorization in humans to Sutherland and Mackintosh’s (1971) theory of selective attention in animal discrimination learning. Any full account of the role of perceptual similarity in categorization must take this dynamism into account. Second, asserting that perceptual similarity may play a central role in categorization is not equivalent to asserting that perceptual similarity is

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always the primary factor. Gelman and Markman (1986) and others have shown that even young children will frequently make judgments on the basis of category membership rather than on perceptual similarity when these two factors are pitted against one another. This result does not mean, however, that perceptual similarity plays little role in category judgments in the “real world” outside of the laboratory. Further research is necessary to explore the varying role of perceptual similarity in different types of tasks and at different developmental times. Third, and finally, implicit or explicit in several recent discussions of categorization (e.g., Komatsu, 1992) is a view that perceptual similarity and associative factors are too primitive and inflexible to deal with the full complexity of human categorization behavior. It is indeed likely that in many (if not most) cases, changes in selective attention of the sort described by Nosofsky (1984) and others will inevitably take a number of stimulus exposures to develop. This attentional change is also likely to be true of associative relations of the sort studied by Wasserman et al. (1992). Although the formation of associations and long-term changes in selective attention may take substantial time to develop, once established, the stimuli or associative relations that effectively activate behavior may be subject to change in an instant. In what is conventionally called a “conditional discrimination”, for example, the presence of one cue signals that a response to one of several available stimuli will be reinforced and the presence of a different cue signals that a response to a different stimulus will be reinforced. Even pigeons are quite capable of reversing their choices in response to such “conditional” cues (e.g., Carter & Eckerman, 1975). Smith and Heise ( 1992) less conventionally refer to those cues that momentarily change the evident similarity of a set of stimuli or differentially activate learned associations as “contexts.” By way of example, for 3-year-olds, the context of a verbal cue entailing a count noun (e.g., “This is a DAX”) guides the child’s response to an object’s shape; this is not the case in a different verbal context using the same word as an adjective (e.g., “This is a DAX one”; Smith, Jones, & Landau, 1992). As complex and intricate as these cases of conditional or contextual stimulus control are, they would appear to be straightforwardly incorporated into the kind of behavioral analysis that we have advanced here.

G. CONCEPTS A N D CONSCIOUSNESS Still more might be said about the completeness of the behavioral analysis of concepts that we have advanced. It will surely seem to some readers that we must have left something out. Discrimination, generalization, reinforcement-can that be all that there is to this remarkable feat of human and animal cognition?

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We cautiously respond, Perhaps not. There may be even more complex actions of humans and animals that importantly transcend the explanatory power of contemporary behavior theory. We ourselves deemed it necessary to appeal to secondary or mediated stimulus generalization to explain nonsirnilarity-based conceptualization when primary stimulus generalization alone was not fully up to the task. If future evidence emerges that challenges the completeness of behavior-analytic principles (such as the contextual or conditional stimulus control just discussed), then we suggest that those new findings too may yield to more careful and complete behavioral analysis. We are confident that this general approach has a great deal to contribute to our understanding of both human and nonhuman cognition. Should the full richness of behavioral adaptability truly fail to be captured by this approach, then our science will nevertheless have been well served by forcing competing theorists to stick to the behavioral facts at issue rather than drifting off to peripheral and diverting disputes. Here, in fact, we must discuss just such a digression, as the respected ethologist D. R. Griffin (1992) has recently argued that our own analysis of conceptual behavior fails because it does not deal with the organism’s alleged thoughts and feelings. Speaking of this research, Griffin offers the following critique: In keeping with the behavioristic tradition, the papers describing these impressive achievements of pigeons are titled “Conceptual Behavior in Pigeons.” Presumably this wording was chosen to reinforce the behavioristic insistence that any mental terms be scrupulously avoided. Animals may behave as though they utilized simple concepts, but behaviorists are constrained to ignore or deny the possibility that they might consciously think about the categories or concepts that must be postulated in order to explain their behavior. (pp. 135-136)

We did not in our papers refer to our subjects’ conscious experience-be they pigeons or children. To have done so would, in our opinion, have added nothing to our account of conceptualization that would be open to objective scrutiny. We see no need to discuss our subjects’ behaviors romantically or sentimentally. We recognize that others do. But we consider these efforts to be misguided. They undermine rather than advance the scientific study of behavior, particularly behavior that is considered to be complex or cognitive by most theorists. They confuse behavioral facts with mentalistic interpretations. They substitute unverifiable notions of feeling or sentiment for testable theoretical propositions. They tend to squelch rather than to foster experimental inquiry. As well, those espousing these notions often grossly distort the true aims and premises of a behavioral analysis of cognition in the interest of promoting their own mentalistic agenda.

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It really is remarkable that we should be witnessing the reintroduction of consciousness into the analysis of behavior. Some years ago, cognitivists drifted perilously close to this point (see Hintzman, 1993, for a critical essay on the excesses of cognitivism). But now, the field of cognitive science is becoming increasingly computational and materialistic. Connectionist theories are edging ever closer to associative accounts of complex cognitive functions (see Gluck & Bower, 1988, and Kruschke, 1992, for connectionist and adaptive network models of category learning), and there are growing signs that theorists may once again be willing to develop theories of cognition that are sufficiently general to apply to both human and animal behavior (see Shanks, 1993, and Wasserman, 1990, for more about associative accounts of both human and animal learning). The emergence of Griffin’s not so aptly dubbed “cognitive ethology” in the midst of these positive developments is at once unexpected and unfortunate. Is behaviorism (and other objective analyses of cognition) really as bad as Griffin and his followers would have us believe? Are we behaviorists a “cold and clammy” lot, of “limited imaginations,” and suffering from the “self-imposed handicap or blindness” of “paralytic perfectionism.” Are we afflicted with “compulsive parsimony” because we cannot or will not expand our interpretive horizons to embrace the private experiences of our subjects. 1s it “really absurd [for us] to deny the existence and importance of mental experiences just because they are difficult to study” (Griffin, 1992, p. 117)? Shouldn’t we capitulate and at long last accept the central premise of cognitive ethology that “we cannot understand animals [or people for that matter] fully without knowing what their subjective lives are like” (Griffin, 1992, p. 252)? Those familiar with the writings of H. S. Jennings, J. B. Watson, and B. F. Skinner know the answers to these questions. We will not repeat them here, having discussed them at length before (Wasserman, 1981, 1982, 1983, 1993). We will grant Griffin one important point, however. With the exception of Skinner’s ( 1957) pioneering treatment of verbal behavior, few behaviorists have seriously endeavored to analyze complex cognitive functioning from a behavioral perspective. We thus felt the sting of Griffin’s (1992) pen most when he prefaced his discussion of our own work and that of other behaviorists on conceptualization in the pigeon by the following remark, “Some of the best evidence that animals can think in terms of categories or concepts has become available from what at first thought may seem an unlikely source, namely the detailed analyses of animal learning by experimental psychologists” (p. 116). An unlikely source? Who should be in a better position to investigate conceptualization than experimental psychologists with a strong background in the laboratory study of learning?

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We sincerely hope that whatever measure of success we have achieved in our studies of conceptual behavior in animals (and children) will encourage others to enter the fray. We surely do not want future critics of behaviorism to complain that we are too timid a bunch to dare to apply our analysis to complex learning and cognition.

H. CONCEPTS A N D

THE

BRAIN

Finally, we come to the interrelation between conceptual behavior and the neural mechanisms that underlie it. The 1972 Nobel prize-winning neuroscientist G. M. Edelman has recently discussed this issue at some length. However, his specific neurological formulation is not as important to us here as is his general orientation. To begin, Edelman (1992) too does not insist that conceptualization requires the human brain or human language. Nonhuman and nonverbal animals are also capable of conceptual behavior. “An animal capable of having concepts identifies a thing or an action and on the basis of that identification controls its behavior in a more or less general way” (p. 108). Furthermore, Edelman hypothesizes that conceptualization is not of recent evolutionary vintage. “Conceptual capabilities develop in evolution well before speech” (p. 108). Edelman correctly notes that most theorists do not appreciate that animals are capable of conceptualization because “it is difficult to know which animals beside humans have conceptual abilities” (p. 108). Here, however, he pays no apparent attention t o the basic behavioral basis for defining conceptualization and, given his own prior remarks on the role of language in conceptual behavior, he curiously resorts to the question of communication. Edelman asserts that the case for conceptualization in chimpanzees is persuasive, but “decisions about the conceptual capabilities of other animals are harder to make . . . because unlike the case with the chimpanzee, our communication with other animals is severely restricted” (p. 108). We strongly disagree with this assertion and so too should the reader after considering the large collection of empirical evidence that we have presented in this chapter. Even more emphatically, we disagree with Edelman’s pessimistic and reductionistic conclusion that “the best we may be able to do [to discover if animals have concepts] is to compare the structures and functions of their brain regions with those of humans and make guesses to guide further study” (p. 108). Behavioral science is in a far better position to elucidate the cognitive abilities of nonhuman animals than to make neurological “guesses.” We can go out and collect objective and reliable evidence on the question. If Edelman’s recent comments are an accurate guide to how broadly

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experimental psychologists’ work has extended beyond our own field, then we are also going to have to do a much better job publicizing the important positive contributions that behavioral science can make to our understanding of brain function than has heretofore been the case. Neuroscience will be better for it. So too will experimental psychology. Appendix

All computer simulations were performed with the Quattro-Pro spreadsheet program (Borland International, Inc., Scots Valley, CA). Trial-bytrial changes in response tendencies to individual stimuli were modeled via the Rescorla-Wagner model (Rescorla & Wagner, 1972), with an asymptote of 100 assumed for reinforced trials and an asymptote of -100 assumed for nonreinforced trials. The per session learning rate parameter (combined alpha and beta) for the simulations was 0.10 for reinforced occurrences of a stimulus in all procedures and 0.05 for nonreinforced occurrences in the go-no go procedure (Experiment 7). Figure 13 shows the excitatory and inhibitory acquisition curves that were generated in 24 sessions using these specific parameters. Categorical dimensions were represented by 100-cell columns in the spreadsheet and individual stimuli were represented by cells in a column. Columns were arranged so that changes in the response tendency to a particular cell generalized (at 0.75 strength) to adjacent cells. Figure 14 illustrates the excitatory and inhibitory gradients generated by these

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4

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8

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10 12 14 16 18 20 22 2.

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100 80

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a,

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20

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Fig. 14. The generalization gradients of excitation and inhibition after 24 sessions of simulated training.

particular parameters. Each stimulus had its own column in the spreadsheet. The net response tendency to a stimulus was obtained by summing across columns representing stimuli from the same categorical dimension. In all of our simulations, we assume only that there is a monotonic relation between response tendency and various aspects of actual responding. We have insufficient evidence at this point to specify more precisely the function relating response tendency to observed responding. Our simulations are thus intended to achieve qualitative, but not quantitative patterns of prediction, as is common in this domain of inquiry (Rescorla & Wagner, 1972; Shanks, 1993; Wasserman, Elek, Chatlosh, & Baker, 1993). ACKNOWLEDGMENTS We thank our many past co-authors, assistants, and technicians for their invaluable help in conducting and reporting much of the work described in this chapter, particularly Ramesh Bhatt. Carol DeVolder, Lloyd Frei. Rob Kiedinger. Kim Knauss, Keith Miller, and Bill Reynolds. Especially helpful in the writing of this chapter were Joan Cantor, Evan Heit. Jon Ringen, and the late Charles Spiker. Preparation of this chapter was supported in part by Research Grant MH47313 to Edward A. Wasserman from the National Institute of Mental Health and by a Faculty Development Grant from Cornell College to Suzette L. Astley.

REFERENCES Anderson, .I.R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409-429.

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THE CHILD’S REPRESENTATION OF HUMAN GROUPS Lawrence A . Hirschfeld

I. Introduction Developing the skills for competent cultural behavior is a major task of early childhood. Achieving cultural competency, in turn, depends on recognizing what entities form the cultural environment. Given that human groupings (i.e., collectivities of people based on their gender, race, native language, kinship status, etc.) are integral parts of nearly all social environments, acquiring knowledge of such groupings is a necessary part of the young child’s early development. Curiously, although we know a considerable amount about the young child’s understanding of classes of nonhuman living things (i.e., animals; Keil, 1979; Carey, 1985; Gelman, Spelke, & Meck, 1983), little research has explored the way children come to identify and represent the relevant social entities in their environment. Similarly, although many researchers have examined young children’s abilities to individuate persons-either in virtue of a perceptual device dedicated to the representation and recall of human faces (Carey & Diamond, 1980) or in terms of the ability to understand behavior as caused by specific constellations of traits and stable dispositions (Eder, 1989)-we have little insight into the way children understand the aggregates of persons that compose the social environment. Nonetheless, the importance of understanding human groups can be readily seen by considering the range of competencies dependent on such THE PSYCHOLOGY OF LEARNING AND MOTIVATION. VOL. 31

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awareness. Learning to use kinship terms (by learning who is and who is not a member of one’s family), culturally appropriate forms of politesse (in knowing one’s own and others’ status group membership), or even mastery of language itself (in which awareness of human collectivities based on gender, relative age, or degree of familiarity between speakers is necessary for selecting the appropriate syntactic or lexical form), all rest on an appreciation of human group boundaries. Not surprisingly, then, researchers have found that sensitivity to social group difference and the ability to adjust behavior in virtue of membership in a social group emerge early (Dunn, 1988; Corsaro, 1979; Becker, 1982; Anderson, 1986, 1990). Acquiring knowledge of the human collectivities used in one’s culture is a fundamental part of common sense, as precociously learned and crucial to human interaction as the capacity to understand others in terms of mental states and mental causes. This social competence is even more remarkable when we consider the range of social affiliations the young child encounters. Who a particular person is, and how his or her behavior is to be explained, is highly variable, depending on context, perspective, and other changing phenomena. The same individual’s relevant group affiliation may not always be the same. Aunt Mary, for example, may be my physics teacher as well as my father’s sister. The variability of our identity relations has consequences for determining the behaviors I should adopt and the expectations I can entertain toward her. Arriving at the culturally appropriate interpretation of our relationship-by figuring out the nature and scope of the group membership that should govern our interactions-is not a simple matter. Yet, despite the complexity of social experience, students of social cognition have generally imagined that these sorts of social understanding are not difficult to acquire. It is widely assumed that children discover the regularities in the social environment through relatively straightforward learning processes, no different from the way children learn about any sort of phenomenon. Although most researchers tacitly acknowledge that children’s attention is directed only toward those human groupings that their culture deems relevant (no one imagines that American children learn the human groups specific to Javanese society), it is generally supposed that the child initially induces social categories from perceptual experience. Social categories, according to a widely held view, are constructed around the physical correlates of group membership. Figuring out group affiliation, then, is attributed to low-level perceptual processing, typically involving little more than judgments of similarity in appearance (Flavell, 1985; Kosslyn & Kagan, 1981; Ross, 1981). This picture of social development is curious for several reasons. First, research increasingly supports the view that domain-general, similaritybased learning plays a smaller role in the formation of common sense

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knowledge than earlier accounts suggested (Hirschfeld & Gelman, 1994). Second, discovering the social dimensions that a given culture deems relevant often does not involve attention to perceptual or raw behavioral cues, but rests on an awareness of underlying currents of social organization, typically elaborated in language not action (Hirschfeld, 1988, 1993). In fact, anthropologists and social cognitivists have provided compelling evidence that young children are sensitive to nonobvious and hidden dimensions such as status, power, and context-dependent authority. Toddlers, for example, possess a nuanced understanding of social relations of power, investing the same person (such as an older sibling) with a different level of authority depending on whether an adult is in the room (Dunn, 1988). Preschoolers display considerable sophistication about the language of social status hierarchies (Anderson, 1986, 1990;Corsaro, 1979; Becker, 19821, and several ethnographically oriented studies have shown that young children can moderate their own linguistic and other behavior as a function of subtle differences in perceived status (Ochs & Schieffelin, 1984; Watson-Gegeo & Gegeo, 1986; Corsaro & Rizzo, 1988). In this chapter, I discuss several studies probing the young child’s understanding of a range of less hidden but widely studied social statuses, involving both intrinsic aspects of group identity (such as race) and variable ones (such as occupation). As noted earlier, previous researchers have supposed that young children have an early emerging and robust sensitivity to these social statuses because they have conspicuous physical correlates. To the contrary, I will argue that young children’s understanding is considerably richer in structure and more adultlike than standard views suggest. Young children’s understanding of these statuses reflects, I will show, a domain-specific concern with the nature and scope of human collectivities.

11. The Psychological Representation of Human Groups

Given their importance to explaining human behavior, it is disappointing that so little empirical work has examined how human groups, as opposed to individuals, are represented in memory (Hirschfeld, in press a; Hamilton, 1981). Not only has little research investigated the ways in which human groups might be conceptually distinguished, much previous research in social cognition has instead emphasized the close parallel between social and nonsocial concepts (Flavell, 1985; Kosslyn & Kagan, 1981), thus downplaying the differences between social and other object categories. A good example of this is the long tradition of research in social psychology on stereotyping (i.e., the willingness to induce proper-

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ties about an individual based on their group identity alone; Allport, 1954; Hamilton & Trolier, 1986;Hamilton, 1981; Bar-Tal, Grauman, Kruglanski, & Stroebe, 1989). Much of this work has focused on the ways that social stereotypes may be linked to general categorization processes (Fiske & Taylor, 1991; Taylor, 1981; Asmore & Del Boca, 1981; McCauley & Stitt, 1978).Emphasis is placed on points in common among social and nonsocial categories, rather than those qualities that might set them apart. As a result, there has been a tendency to see all social categories as essentially alike (Turiel, 1983). For example, running through much of the literature on social prejudice is the assumption that all stereotypes have the same structural properties no matter whether the social category stereotyped is based on race, gender, occupation, sexual or political orientation, or other social status (Hamilton & Trolier, 1986). Little attention, accordingly, has been paid to possible differences in conceptualization that might result from variations in types of social groups encountered. Still, a few studies have addressed the ways in which human groups and individual humans may be representationally distinct. Wyer and Martin (1986),for example, have proposed that information about groups is stored independently of information about individual group members, particularly traits associated with the individual. Milner (1984) has suggested that different kinds of learning are involved in acquiring knowledge of individuals and groups. Adults appear to distinguish social properties (i.e., the attributes that are true of an actor) from social kinds (i.e., the sorts of actors there are; Hirschfeld, 1988), suggesting that the kind of group represented shapes the inferences one draws about members of different groups. For example, Rothbart (1981)recalls an anecdote of Sartre’s (1948) about a woman who disliked Jews because of her disagreeable encounters with Jewish furriers. Sartre astutely wondered why she chose to hate Jews, not furriers. The explanation presumably is that it is more “natural” to hate Jews than furriers because prejudice adheres to the kinds of people there are (i.e., race) more readily than the kinds of activities in which people engage (i.e., occupation). According to most previous research, young children are incapable of drawing distinctions based on such abstract criteria. As observed earlier, standard accounts of social cognitive development have emphasized the integral role similarity in appearance is thought to play in the derivation and representation of a wide range of social categories. Young children are seen as externalists whose attention is drawn to surface properties and who rarely appeal to internal and hidden properties, causal principles, or psychological motives when explaining the behavior of humans and other living things (Piaget, 1951; Ross, 1981). Accordingly, it is generally accepted that young children do not distinguish social properties from social kinds, and in particular are thought not to model their explanation

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of events on nonobvious social causes (for a review, see Hirschfeld, 1994). Perhaps the most detailed discussion of this process involves the young child’s notion of race, the topical focus of the following studies. Widely researched, the concept of race is among the earliest emerging of the preschool child’s social categories (Horowitz, 1939; Clark & Clark, 1940; Aboud, 1988; Katz, 1982). Cross-cultural and cross-national studies indicate that this sensitivity to race recurs across widely different systems of cultural belief (LeMaine, Ben Brika, and Bonnet 1988; Vaughan, 1987; Hirschfeld, 1988). By most accounts, this stability in racial concept formation is attributable to a direct integration of perceptual experience: In building racial categories, young children are assumed to attend to “easily discernible” cues (Katz, 1982, p. 20) and group “people into categories on the basis of attributes perceived in common” (Vaughan, 1987, p. 91), including such indices of “concrete reality” as skin color (Clark & Clark, 1940, p. 168), and other “overwhelmingly” external attributes such as costume, cuisine, and language (Aboud, 1988, p. 106). Still, it is also widely accepted that the notion of race eventually comes to be embedded into an enriched conceptual system (adult understanding of race is clearly theory-like, involving essentialist expectations that go beyond the range of direct experience; Allport, 1954; Atran, 1990; Rothbart & Taylor, 1990). Thus, even though the exrenrion of adult’s and children’s racial categories largely overlap, the meaning of these categories is quite different (Aboud, 1988). The ability to categorize individuals racially without understanding the adult meaning of racial concepts is supposed to occur because the two tasks-categorization and interpretation-are thought to be the result of different processes. Much as living things at the basic level “cry out to be named” (Berlin, 1992), both common folk belief and scientific tradition assume that the social environment visually imparts racial categories (Mosse, 1978; Banton, 1987). One reason that this account of racial concept formation enjoys such wide appeal is that it converges with other well-grounded arguments. For example, the close parallels between the adult notion of race on the one hand, and folk and scientific systems of biological belief on the other, have been frequently observed. A hallmark of both the racial and biological systems of belief is essentialist reasoning (Atran, 1990; Banton, 1987; Guillaumin, 1980; Allport, 1954).Viewing racial concepts as derived from unmediated observations of nonrandom gaps in human phenotype, accordingly, seems plausible because lay people and ethnobiologists alike argue that the folk-species concept is derived from unmediated observations of discontinuities in flora and fauna (Berlin, 1992; cf. Atran, 1990). Like acquiring knowledge about taxonomic differences among plants and animals (Johnson, Mervis, & Boster, 1992; Carey, 1985), coming to understand racial differences among human populations would initially turn on

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similarity-based learning. Later, a more “theoretical” model would emerge in each domain. The analogy between folk biology and racial thinking, however, may not be apt. In contrast to work on folk biology that has uncovered surprisingly strong cross-cultural agreement in living kind classification (Atran, 1990; Berlin, 1992; Johnson et al., 1992), research from several traditions suggests that racial categorization is strikingly nonconuergenr across cultures and historical epochs. For example, social historians and cultural anthropologists have shown that different systems of racial classification sort individuals of the “same” physical type quite differently (Harris, 1964; Stoler, 1992). Thus, despite the prevalence of the idea that racial types are fixed and derived from the way physical differences in the world are patterned, the notion of race itself varies considerably across time and across cultures. What salient racial differences are, and where their boundaries are drawn, depends on historical and cultural context. For example, during the early twentieth century, discussions of race in America were as (and perhaps more) likely to contrast Northern and Southern Europeans (and Protestants and Catholics) as blacks and whites. As Gordon (1989) observes, social workers “comments and expectations about [Southern European, Catholic] immigrants in this period were similar to views of black clients in the midtwentieth century” (p. 14), and racially mixed relationships referred to Protestant-Catholic, not black-white, unions. In contemporary American society, the same individual’s racial status can differ substantially inasmuch as each state has it own, often conflicting, rules for determining whether someone is black or white (Davis, 1991). Moreover, in the United States, a person is either black or white, but cannot be both. The category of mixed-blood is not a viable cultural or legal option in spite of the frequency of mixed-racial unions (Hirschfeld, in press a; Molnar, 1992). In contrast, in many other cultural traditions (e.g., contemporary South Africa), mulatto populations constitute a third race (even when not legally defined as such; Stoler, 1992). Given this cultural and historical variability, it should not be surprising that most biologists now conclude that human races, as they have been culturally defined, simply do not correspond to interesting biological categories (Bodmer & Cavalli-Sforza, 1976; Gould, 1981). Although problems with fixing the referents of racial concepts do not force us to abandon the view that children learn about race by attending to physical differences, they do suggest that children would have to attend to something more than phenotypic gaps in the human population (and the way language labels these gaps) if the close correspondence in extention between adult and young children’s racial concepts is to be achieved. An obvious candidate would be parental teaching: children learn the culturally appropriate racial categories, even if the physical environment underdeter-

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mines them, because parents guide children’s attention. There is reason to believe, however, that attention to local environmental cues may also be insufficient. Several studies have shown that young children’s racial uttitudes do not closely correspond to those of their parents, nor do children’s racial attitudes seem to respond readily to parental interventions directed at changing them (Branch & Newcombe, 1980, 1986; Hirschfeld, 1988). This does not, of course, rule out the possibility that young children’s racial categories are derived either from observation or through tuition from parental categories, but it does raise the possibility that children bring more to the task of learning about race than a willingness to categorize objects in the world or simply accept what their parents tell them. There are also cognitive questions raised by the view that racial concepts develop directly out of observations of physical difference. By stressing the integral role similarity in appearance is thought to play in the derivation of social categories, existing work on social cognitive development sets itself apart from recent research on common sense concept formation. Many researchers now believe that in a wide range of domains, early knowledge is organized through specialized faculties for understanding that direct t h e child’s attention to certain sorts of data and guide the child’s hypotheses about the nature of relations between category members. Often likened to scientific theories, several of these faculties have been empirically studied, including naive biology (Carey, 1985; Keil, 1989; S. Gelman, 19891, core beliefs about physical matter (Spelke, 1991;Baillargeon, 1992), theory of mind (Astington, Harris, & Olson, 1988), naive mathematics (Gelman & Gallistel, 19781, face recognition (Carey & Diamond, 1980), music (Lerdahl & Jackendoff, 1983), and syntactic knowledge of natural languages (Chomsky. 1986). Of particular importance, these faculties provide the principles (a) for defining entities within a domain of knowledge, (b) for specifying operations on these entities. and (c) for governing causal reasoning about these entities (Carey, 1985; R. Gelman, 1990). With few exceptions (e.g., Turiel, 1983), however, social cognitivists have not seriously considered the possibility that the acquisition and representation of social categories may also be governed by a specialized faculty for understanding sensitive to deeper differences. and not merely appearances.

111.

An Alternative Model of Social Development

What would a domain-specific account of social development in general, and of the notion of race in particular, look like? Such a model might predict that children develop racial categories in response to an impulse

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to discover the sorts of humans there are rather than as an attempt to catalog physical differences among the humans they encounter. In trying to identify the human groups relevant to their cultural environment, and to discover the natures of their members, children would provide evidence of an early ontological curiosity. In earlier work, I have tried to show that young children do appear to believe that different principles of organization underlie the various common sense collectivities that govern social events. These include groups based on notions of common kinship (Hirschfeld, 1989a), language (Hirschfeld, 1989b), race, and occupation (Hirschfeld, 1988, 1993, 1994). Children, accordingly, might discover the kinds of groups there are in the world, not by attending to perceptual regularities in the environment, but by elaborating strategies for reasoning about difference derived from expectations about the nature of society and nonobvious social commonalities. Discovering the nature of society and social commonality, in turn, might involve discerning the principles for sorting the kinds of people there are in the world. Such a view suggests that young children’s social categories would be more richly structured than previous researchers have believed, so that social categories would be determined in significant measure by the folk theories in which they are embedded. In this regard, social and other object categories would be similar in that the development of both would be governed by domain-specific skeletal principles that guide children to parse the environment in terms of hidden and nonobvious commonalities among category members. The particular commonalities perceived, of course, would vary as a function of properties of the domain. In this case, the relevant structural property would be the specification of a social ontology and a discrimination of domain-specific principles of social causality. As already observed, the issue is not whether race is theory-like. All observers agree that adult belief about race is richly structured, and that children’s belief is not. Moreover, despite the considerable variation in racial systems of class$cation, there is marked universality in systems of racial theory. Adults the world over construe human racial variation in biological terms (as reflecting immutable and heritable anatomical differences; see van den Berghe, 1967; Banton, 1987). There is equally broad consensus about the source of this essentialist understanding. The conspicuous parallels (both cognitive and historical) between the species model of living kind difference and the racial model of human difference has frequently been noted (Guillaumin, 1980; Banton, 1987; Atran, 1990).The identification of the two realms is typically attributed to a transfer of biological principles of inference to the human domain (Rothbart & Taylor, 1990; Atran, 1990; Boyer, 1990).

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The transfer of knowledge from better understood (or better grounded) domains to less understood (or less grounded) domains is of interest to a broad range of psychologists. Often discussed under the rubric of analogical transfer (Vosniadou & Ortony. 1989; Gentner & Stevens, 1983), such reasoning is thought to be particularly important in the development of knowledge in novel areas (Inagaki & Hatano, 1987; Carey, 1985). Strikingly, however, no convincing and general developmental account of the mechanisms governing the analogical transfer of knowledge, particularly across domains, has emerged (Brown, 1990). Moreover, several researchers have pointed out that the sorts of “natural” transfers, readily observed in normative contexts, are notoriously difficult to replicate experimentally (Novick, 1988; Resnick, 1994). This difficulty aside, cross-domain transfers do appear to occur. The spontaneous modeling of inferences about racial difference after principles of biological reasoning plausibly represents one of the best researched. normative instances of this sort of crossdomain knowledge transfer. The alternative, domain-specific model of race is not as committed as the standard view to knowledge transfer. On the standard model, racial thinking comes to encompass the causal principles and ontological commitments that lay folk use to reason about biological creatures in virtue of the perception of parallel morphological clustering: race is commonsensically seen as an analogue to species (more accurately, the folk species concept). In contrast, the alternative model predicts that a richly structured understanding of race develops bqfow the visual aspect is fully elaborated. In fact, the naive theory enables the child to select the relevant physical differences to which to attend. Thus, according to the alternative model, a theory-like construal of race should be evident virtually from the outset. On the standard model, perception shapes theory; on the alternative model, theory guides perception. Focusing on these contrasting predictions, the studies that follow explore in depth the derivation of young children’s beliefs about social difference.

IV. Race and Perceptual Information A. EXPERIMENT 1: THECONTRIBUTION OF PERCEPTUAL INFORMATION TO THE CONCEPTUAL SALIENCY OF COMMON SOCIAL CATEGORIES The perceptual (or similarity-based) model of social category acquisition assumes that children attend to different categories as a function of their perceptual prominence. If a category has marked physical correlates, it

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will be recognized more precociously than categories with less conspicuous attributes. If several categories are all similarly conspicuous, they should be equally salient conceptually. Existing studies provide some support for these claims. For example, young children learn racial categories (in which category membership is supposedly defined in terms of physical cues) before ethnic or religious ones (in which category membership is supposedly defined in terms of beliefs) (Aboud, 1987). Children’s sensitivity to racial, occupational (Blaske, 1984). and body build (Lerner, 1973) categories, which are all perceptually prominent social dimensions, emerge during the same early preschool period. These data, however, provide only weak support for the claim that all similarly conspicuous social categories have equal conceptual salience because previous studies have explored children’s understanding for each social dimension separately. Thus, it is not possible to tell whether two social dimensions, whose developmental courses closely parallel each other, are similarly salient. To explore this question. in a first experiment, 1 probed French preschoolers’ memories for social information as a way of assessing the conceptual importance of race relative to other perceptually prominent social descriptors, such as nonracial physical features, occupation, gender, and behavior. Social information was presented to children in a short narrative in which each character’s social affiliation and status were mentioned but were otherwise irrelevant to the story’s plot and structure. The story (adapted from a type that young children efficiently parse and readily recall) centers on a young protagonist whose goal is to buy a birthday present for his or her (depending on subject’s sex) mother. The story’s principal complication is that the child, in trying to find an appropriate store, encounters four different adults. In story-grammar terms, the narrative consists of a setting, a complication, four developing episodes (each including a solicitation, a reaction, an invitation to further action), and a resolution. Each adult character is described twice in terms of his or her race, occupation, gender, and a nonracial physical feature (stature, body build. or age). After hearing the test text, each subject was asked to freely recall the story. Following the recall, the experimenter reread the story to the subject and again solicited a free recall. The number of times subjects recalled the race, occupation, nonracial physical feature, or gender of each character was computed. Recall indices sensitive to each subject’s memory for the story were computed by dividing the frequency that each social dimension was recalled by the number of story characters the child remembered (e.g., if a subject recalled three of th’: four characters, two of whose race was mentioned, the subject would get a race-recall score of .66, as race was mentioned two out of three times).

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Conceptual importance was construed in terms of the relative availability of these social descriptors in children’s memory for a short narrative. Differences in subsequent recall of the social descriptors were accordingly attributable to differences in their conceptual importance. If children reasoned that all conspicuous social dimensions were equally important, then the rates of recall of the various dimensions should be quite similar. The pattern of the children’s responses, however, shows that the various social dimensions were not equally memorable. Subjects recalled the characters’ occupations ( M = .29) significantly more than race ( M = .18). gender ( M = .15), or nonracial physical feature ( M = . I I). Contrary to what the standard view predicts, different social descriptors appear to be differentially salient. Also in contrast to standard predictions, race is not the most salient social dimension for preschoolers: Occupation-a social property usually seen as contingent rather than essential-was found to be more salient than race. Although children recalled more social information following the second reading than following the first, controlling for memory factors by using a measure of recall sensitive to children’s overall memories of the story, also proved to be informative. There were no reliable between-age-group differences in rates of recall when indices that controlled for each child’s overall level of recall were used.’ This finding is significant because previous work suggests that major changes occur in children’s social cognitions between 3 and 4 years of age. Awareness of race and other social properties supposedly emerges and becomes increasingly elaborated during the preschool years. The present findings. in contrast, indicate that it is children’s memory, not social awareness, which may be changing during this period (Table 1). In sum, not all social categories are equally salient conceptually. The prominence of a social category’s physical correlates does not predict its conceptual salience. Neither race nor gender are the most salient social descriptors for preschool children. Preschoolers’ social beliefs are both consistent and stable during this period of supposed change. These results are inconsistent with the standard account of young children’s social categorization, but fall short of allowing us to reject it. This experiment relies on verbal stimuli to probe the role of visual saliency and thus is not the strongest test of the contribution of surface cues to social category formation. It is possible. for example. that visual categories bring to mind the perceptual correlates of the categories more readily than do category labels by themselves. Thus, the perceptual-rich view would distinguish

’

For details of children’s performances as assessed using indices controlling for memory factors. see Hirschfeld. 1993.

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TABLE I EXPERIMENT 1: MEAN PROPORTION OF RECALL OF SOCIAL DESCRIPTORS FOR THE T w o AGEGROUPS Age level 3-Year-olds N = 33 Social descriptor Occupation Gender Race Nonracial physical feature

4-Year-olds N = 31

First recall

Second recall

First recall

.221

,302 ,214 .214 . I 12

,234 ,130 ,148 ,092

,118

.I08 .086

Second recall .392 ,141

.238 .I57

Nore. From Hirschfeld (1993)

between performances involving the category labels alone and category labels directly linked with stimuli whose physical correlates are inescapably evident. To rule out this possibility, I conducted a second narrative recall experiment in which visual stimuli as well as verbal labels were used. If the conceptual saliency of racial categories is influenced by the sheer availability of physical exemplars, then rates that social information is recalled in Experiment 2 should exceed those in Experiment I . 2: VISUAL,CUESA N D THE RELATIVE B. EXPERIMENT CONCEPTUAL SALIENCE OF SOCIAL CATEGORIES Subjects in Experiment 2 were also presented with characters whose social descriptions were irrelevant to the story’s plot and structure. In this instance, however, children were presented with a visual narrative paralleling the verbal narrative used in Experiment 1 and their memories ofthe story were probed. As in the first experiment, conceptual importance was construed in terms of the relative availability of social descriptors in children’s memories for the visual narrative. The effect that labeling had on memory for social information was examined by conducting the narrative recall task together with tasks that matched verbal labels for the social dimensions mentioned in the narrative recall task with their referents. One half of the subjects completed the verbal narrative recall task after participating in these additional tasks, the other half did the verbal narrative recall task before performing the additional tasks. If priming increases the association of label and knowledge of physical features, then children’s recall should improve under this order manipulation.

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In the narrative recall experiment, subjects were shown a picture book containing 14 color-wash drawings. The plot line of the picture story paralleled that of the verbal narrative used in Experiment 1. In the picture story, the protagonist was a lost dog who encounters four adults in succession in trying to rejoin his master. As in Experiment 1, each adult character’s occupation, race, gender, and a nonracial physical feature were marked. Subjects were asked to look through the story book page by page. To ensure that subjects attended to the pictures, children were asked to describe each frame as they went through the book. Following this “reading,” the experimenter asked each subject to recall three things about the first person encountered by the lost dog. The procedure was repeated for all four characters. The number of times children recalled each of the social dimensions was calculated. These scores were transformed into proportions by dividing them by the total number of descriptions each subject offered. Again, if children reasoned that all conspicuous social dimensions were equally important, then the rates of recall of the various dimensions should be quite similar. As Table I1 shows, the rate of recall of gender ( M = .27) was reliably higher than the rates of recall of occupation (M = .13), race (M = .07), and nonracial physical feature (M = .06), which were not significantly different from each other. Like the results of Experiment 1, these findings also lend little support to the claim that racial categories are rich in visual information. In particular, visual descriptions of racially marked stimuli did not elicit descriptions using racial labels. Strikingly, children were actually less likely to describe a social event in terms of a character’s race following a visual narrative than they were when recalling a verbal one. This was not true of all social categories. Gender, for example, was better recalled following a visual narrative than a verbal one (though French provides more opportunities than English to underscore an individual’s gender). The results of Experiment 2, like those of Experiment I , suggest that different social categories have distinct saliencies. Taken together, these findings are consistent with the alternative model 1 have proposed; namely, that a social category’s relevance is not simply a function of its perceptual properties. Specifically, racial categories do not appear to be initially rich in perceptual information. One potential problem with this interpretation is that race might be so obvious that children would not think to describe the pictured individuals in terms of it. Mentioning race would accordingly violate conversational rules about relevance. There are two reasons to reject this second interpretation. First, gender is an obvious way to describe people, yet this did not lead to a dispreference for gender descriptions on the task. Just the contrary; as Table I1 shows, gender is the most common descriptor recalled. Second, if race is taken as unreflectively shared knowledge (and thus there is no

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TABLE I1

EXPERIMENT 2 : MEAN PROPORTION OF RECALLOF SOCIAL DESCRIPTORS FOR THE Two AGEGROUPS,VISUAL NARRATIVE TASK Age level

3-Y ear-olds N = 16 First recall

Social descriptor ~~~~~~

~

Gender Occupation Race Nonracial physical feature ~~~~

Second recall

4-Year-olds N = 16

First recall

Second recall

~

,285 ,103 ,086 ,056

,231

,329

.223

,113

. I34

. I73

,072 ,044

.047 .078

,075 ,068

~

Note. From Hirschfeld (1993).

need to draw attention to it), we would expect race to be an especially memorable aspect of the described individual's identity. Yet, subsequent questioning revealed that children were not particularly accurate at recalling whether the various ethnic groups had been depicted in the picture story. After completing the recall task, children were asked whether they recalled seeing individuals of each ethnic group in the story. Children's memories for this information was generally poor, and their responses were no better than chance. If race is such an obvious feature that children do not find it worth mentioning, it is not clear why their reia!l would be so poor.

I . Additional Measures Two additional measures were given to the children participating in Experiment 2. The first assessed a subject's ability to match physical depictions of occupations and races, figuring in the visual narrative, with verbal labels for these occupations and races. This label-to-picture pairing task served principally as a priming condition for the narrative recall task. Previous research has established that children of this age perform at ceiling in matching common occupational and racial labels to depictions of their referents, and our results replicated this. Accordingly, the results from this task will not be discussed here (see Hirschfeld, 1993, for the results and complete description of this task). A second task, match-tosample, was included to help resolve a question left open from Experiment 1. That study established that children are more likely to recall a person's

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TABLE 111

MATCH-TO-SAMPLE ITEMS USED I N EXPERIMENT 2 Target

Comparison I

Comparison 2

1st Triad: Occupation versus race

Male black physician 2nd Triad: Sex versus race Black female nurse 3rd Triad: Occupation versus sex White female physician

Black female nurse

White female physician

White female physician

Black male physician

Black male physician

Black female nurse

occupation than race. The findings do not tell u s if occupation is conceptually or perceptually more salient than race. The match-to-sample tasks address this issue by pitting race against occupation in perceptually based sortings. Subjects were shown three triads consisting of a target and two comparison pictures in which a person's race, occupation, and gender were marked. Table 111 summarizes the three triads. Each child was asked to put the target drawing with the comparison picture to which it was most similar. In total, the three triads provided each subject with two opportunities to select race, occupation, or gender. The number of times each subject chose each social dimension was calculated. As Table 1V shows, judgments did not differ from the chance expectation of 1 . Both younger and older preschoolers displayed the same lack of preference for one dimension over the others, suggesting that they TABLE IV

EXPERIMENT 2 : M E A NNUMBEROF TIMESEACHDIMENSION WASCHOSEN (OUT OF T W O ) Age level 3-Year-olds N=lh

4-Y ear-olds

Sex

1.06

Occupation

t.68) I.oo

0.94 (.77) 0.94 (.68) 1.12 (.72)

Dimension

(54)

Race

0.94 (.68J

N = 16

N o f e Standard devi.itions are given in parenthese5 From Hirxhfeld (1993)

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believe that individuals of the same race go together as readily as individuals of the same occupation, and as readily as individuals of the same gender. It is possible that this lack of preference is due to subjects’ failure to engage the task. As the results of Experiment 3 will show, however, this interpretation is not compelling: Given slightly different instructions (but essentially the same materials), children of the same age produce a quite different pattern of sorting. Thus, the perceptual salience of occupation is not substantially different from that of race or gender, so that differences in salience of occupation, relative to race and gender, uncovered in Experiments 1 and 2 must be attributed to conceptual, not perceptual factors. 2. Priming Results from the order manipulation lend additional support to the claim that racial categories are not initially rich with perceptual information. The narrative recall part of Experiment 2 was conducted under two conditions: a primed and an unprimed condition. In the primed condition, subjects first participated in the tasks described in the previous section. In the unprimed condition, subjects participated in these tasks after completing the picture book recall task. Recall that these tasks involved asking children to look at pictures portraying individuals of various races and occupations, and to match these pictures to the appropriate racial and occupational labels. If seeing a category exemplar (and hearing the verbal label for it) bring to mind the physical correlates of the category, then priming should increase the availability of this knowledge. Table V summarizes the results. Strikingly, priming has no significant effect on recall: Linking verbal labels with depictions of their referents on the pairing task did not facilitate the label’s retrieval when the child encountered similar representations of the same referent. Having the child pair a picture of a black male with the label black, for example, did not increase the likelihood that subjects would subsequently refer to a picture of a black male with the term black. On the face of it, this is a curious finding. Previous research shows that preschool children can readily match such labels with pictorial (and other kinds) of exemplars (for race categories, see Aboud, 1988; Clark & Clark, 1940; for body build categories, Lerner, 1969, 1973; Lerner & Schroeder, 1971; for occupational categories, see Blaske, 1984; for a nonracial physical feature, see Pope Edwards, 1984). These performances have been interpreted as showing that perceptual properties are closely linked to the relevant verbal concepts. In fact, these previous studies establish only that some perceptual information is associated with the verbal categories,

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TABLE V EXPERIMENT 2: MEANRATESOF RECALLFOR EACHSOCIAL DESCRIPTOR BY CONTROL CONDITION Condition Social descriptor Gender First recall Second recall Occupation First recall Second recall Race First recall Second recall Nonracial physical feature First recall Second recall

Primed

Unprimed

,237 .I91

,377 ,265

,126 . I53

,110

,133

.074 ,072

.076

,092 ,062

.050

,057

,043

Noru. Data from Hirschfeld (1993)

they do not specify the scope of that association. For example, recognizing that a verbal label and a particular perceptual cue go together implies either that (a) the perceptual information is directly represented in the concept such that observing the perceptual cue brings to mind a specific verbal label; (b) the perceptual information is contained in the verbal concept, although in a less direct way, such that the perceptual cues brings to mind the specific verbal label only under certain conditions (say, priming): or (c) only the fact that perceptual information is important (rather than any specific perceptual information per se) is represented in the concept. Hence, observing the perceptual cues brings to mind the verbal label only when attention is drawn to a relevant and perceptual contrast and the verbal label is provided. If the first relation (i.e., the verbal label is directly brought to mind by the perceptual cue) typified young children’s racial concepts, we would expect the results of Experiment 2 to parallel or exceed those of Experiment 1 . At the least, race should be moderately salient in social description on both tasks. Clearly, neither of these predictions holds. If the second relation typified young children’s racial concepts (i.e., the label is brought to mind by the perceptual cues only under certain attention-directing conditions), then we would expect that the results of Experiment 2 would parallel those of Experiment 1 under the priming condition, but not under

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the unprimed condition. This was not the case. Finally, if the third relation holds (i.e., the verbal label and the perceptual cues are associated only if the two are immediately conjoined and attention is drawn to the contrast), priming should have no necessary effect, and accordingly performance on the two tasks should not be related. This is the pattern obtained. These findings not only indicate how racial information is nor represented, they help reveal the format under which racial information is initially represented. Preschoolers readily differentiate people on the basis of racially relevant perceptual cues. These same children also have racial categories: they recognize the existence of named, enduring groups of humans. Nearly all previous work on racial concepts formation has assumed that these two phenomena are aspects of a single system. I argue that they represent two distinct, but overlapping conceptualizations. The systems articulate in two ways. First, perceptual and verbal categories overlap in development such that during early childhood a gradual calibration of the perceptually oriented and domain-oriented concepts is achieved. Previous research has confounded this evolving articulation of perceptual and racial categories by interpreting the first stages of this calibration as developmental changes in racial awareness per se. Second, racial concepts contain some perceptual information, but it is extremely fragmentary, specifying only that certain classes of perceptual differences are pertinent to group membership (young children probably realize that skin tone is a pertinent dimension, finger length is not).* My argument accordingly is nor that perceptual input is irrelevant to emerging racial categories. As with other common sense categories, empirical regularities in the environment are recruited in the service of racial concept formation. I do suggest, however, that these empirical regularities greatly underdetermine racial concepts, so that in the process of building racial categories, obvious surface cues like skin color are not de$ning for young children. The results of Experiment 2 show that although young children are adept at matching racial labels to individuals differing on relevant physical dimensions, they do so only when both label and referent are simultaneously made available. When asked simply to describe a social event, race is not a particularly important dimension-so much so that children are not even sure if they have just encountered a story character of a certain race. Do these results imply that race is generally not relevant to young children? And, particularly, do these results suggest that race is poorly understood by young children? 1 turn to these questions in the next section. Skin tone is the easiest racially relevant perceptual feature to discriminate for preschoolers. although it is not the most salient perceptual cue in racial recognition (Sorce. 1979).

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V.

151

Do Children Have a Theory of Race?

Most previous work assumes that the development of racial and other social categories is characterized by low level computational processes involving attention to similarities in superficial appearances. Experiments 1 and 2 undermine confidence in this standard view by suggesting that children’s attention to social categories is not predicted by differences in the prominence of the category’s physical correlates. First, preschoolers do not find all conspicuous social categories to be equally salient. Second, preschoolers do not invest all social categories the same amount of perceptual information, and thus presumably do not derive all social categories (most importantly race) from observations of physical difference. One interpretation of these findings is that children simply do not see race as a particularly important aspect of the social environment. Yet, observing that children do not find race an especially important way to describe social contexts does not mean that race is unimportant to explanations of social life. Studies showing that young children maintain strident racial prejudices, for example, certainly suggest that the potential of race to promote inference is quite powerful (Horowitz, 1939; Clark & Clark, 1940; Katz, 1982; Aboud, 1988). The studies in this section examine whether young children believe race to be a potent explanatory concept. In particular, they explore the possibility that young children’s expectations about nonobvious and abstract commonalities among members of social groups provide the context for development of children’s beliefs about racial and other social difference. If young children’s beliefs about race are not a function of their perception of regularities in outward appearance, as Experiments 1 and 2 suggest, it is plausible (in fact necessary) that these beliefs are derived from other features of the social environment. One possibility is that race, although poorly understood in perceptual terms, is well understood in an ontological sense. According to this view, children would be better at thinking about race than attending to its physical qualities. Young children, in other words, conceptualize race more than see it. What would this conceptualization be like? First, as just noted, although we may not know what the conceptual model derives from, we do know that it eventually integrates with one feature of the social environment, namely, the adult model of race. As was also noted earlier, one striking aspect of all cultural models of race is the putative biological implications that race is supposed to have. Adults the world over believe humans are partitioned into “natural,” physically relevant, and innately determined groups. Biology provides the context and causal model in which race is understood, comprising a commitment to a particular ontology, a specific

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pattern of explanatory principles, and a specified set of concepts. For race, the relevant ontology is the expectation that humans fall into several distinct physical types. Several causal principles are thought to govern the generation of these types. First, race is believed to be derived from physical (anatomical) differences. Second, physical properties differ in meaning, hence, they differentially contribute to racial identity (skin color is generally considered pertinent, stature is not). Third, racially relevant physical properties are thought to be immutable, not changing during one’s lifetime. Fourth, these properties are derived from family background, and are transmitted and fixed by birth. Young children, however, supposedly do not expect race to have any of the biological implications with which adults impute it (Aboud, 1987; Semaj, 1980). More generally, young children are thought not to understand that any sort of biological identity is immutable. Researchers have suggested that preschoolers fail to appreciate that most aspects of a person’s biological identity cannot change, including one’s race (Aboud, 1988), gender (Carey, 1985; Emmerich, Goldman, Kirsch, & Sharabany, 1977; Slaby & Frey, 1975), biological category (Keil, 1989), and personal identity (Guard0 & Bohan, 1971). More specific to race, young children do not grasp that race is a biological property, derived from family background (Aboud, 1988) and fixed by birth (Solomon, Johnson, Zaitchik, & Carey, 1993). Children fail to understand these things because they supposedly overrely on outward appearances. Aboud & Skerry (1983) suggest that before 8 years of age, children do not distinguish the contribution variable properties such as ethnic clothing, and intrinsic ones such as race, make to identity. Similarly, Carey and Spelke (1994) propose that young children do not understand that family resemblances are due to biological transmission and are fixed at birth rather than attributable to social interaction and association. The findings from Experiments 1 and 2 force us to revisit the claim that young children have an atheoretical understanding of human variation: If young children do not rely on physical appearances in constructing racial categories, it is not obvious how they could overrely on them. A.

EXPERIMENT 3: RACE,MUTABILITY,A N D FAMILYBACKGROUND

What meaning do young children give changes in appearance? Do children believe that an individual changes identity when gaining or losing a physical feature? Is such a change conceivable at all? For adult common sense, change of dress is not a change of identity, change of sex is. Change of race, in contrast, is not conceived of as a genuine possibility. In a series

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of studies, I explored how children use their knowledge of various social categories in making judgments about preserved identity. If children do not see all social categories as equally salient, as Experiments 1 and 2 indicate, do they believe that different social categories make unequal contributions to identity? To test this, I asked American preschoolers and young school-age children living in a university town to make judgments about a person’s identity, using a format much like the one used in standard identity constancy studies. (The remaining studies reported in this chapter were conducted in the United States.) Three-, 4-,and 7-year-olds were shown a series of pictures, each portraying an adult of a specific race, body build, and wearing occupationally relevant apparel (e.g., a stout, black police officer, or heavy, Hispanic nurse). The children were then shown a series of paired pictures portraying two children, each of whom shared two (of the three) social features (body build, race, and occupation) of the target picture. Each pair contrasted with the target picture on one social dimension (e.g., one pair consisted of a thin, black child wearing a police hat, toy gun, and whistle, and a plump, white child wearing a police hat, toy gun, and whistle), so that across the three pairs, all possible contrasts were presented. Subjects were then asked about possible identity relations between the target and companion pictures. Figure 1 illustrates one set of items (contrasting occupation and race). Most identity constancy studies ask children if the individual in the target and (altered) companion pictures are the same person. The task I used had a somewhat different design. Children were asked whether the target and companion pictures depicted pairs of related individuals following a familiur transformation. Children are knowledgeable about two sets of changes that encompass preserved identity. First, as people age they change in appearance, and children appear to have little difficulty reconciling such changes with a constant identity. Second, children appreciate that there are family resemblances, and that in spite of differences between the way individual family members look, they nonetheless maintain a certain identity relation (Hirschfeld, 1989a). I relied on this knowledge in designing the study. Two experimental conditions were used, an inheritance condition and a growth condition. In the inheritance condition, children were asked which of the contrast pair was the child of the target adult; in the growth condition, children were asked which of the contrast pair was a picture of the target adult as a child. If children are innocent of the underlying connection between growth and inheritance (and thus do not grasp the supposed biological basis of some social categories), then they should not infer that the patterns of change and stability in the two cases are much the same.

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I54

Fig. I .

Experiment 3. Sample items, male series.

Furthermore, if children focus only on changes in physical appearance when making identity judgments, modifications in body build should be as likely to signal a change in identity as a change in skin color. Yet, as Fig. 2 shows, although the effect was strongest for the oldest age group, children of all ages reliably expected that race, but not body build, preserved identity in the face of dramatic changes in appearance. One prediction of the standard view is that preschoolers should not distinguish between the contribution corporeal features (like skin color) or noncorporeal

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ones (like clothing) make to identity. Springer & Keil(l989, 1991)propose a more moderate version of this hypothesis according to which some, but not all, conceptually enriched distinctions might be drawn (e.g., social and behavioral properties would be jointly contrasted with physical and biological properties). On their view, finer-grained conceptually enriched contrasts (say, between qualities that are relevant to collectivities vs. those that are not) would not influence young children’s judgments of identity. Yet, here too, results from m y study challenge this characterization of young children’s social beliefs. As Fig. 2 indicates, by 4 years of age, children distinguish the contribution race, as opposed to other salient (but collectivity irrelevant) properties like occupation, make to identity. Strikingly, as Table VI indicates, there were no differences in children’s judgments about growth and judgments about inheritance. It is worth considering the implication of this finding. Most studies of identity constancy ask children to assess the possibility or consequences of changes in characteristic but nonessential cues (dress) versus changes in characteristic but essential features (skin color). Implicit in such contrasts is the notion that inessential features are noncorporeal, whereas essential ones are literally embodied. Although these tasks all use familiar properties, they do not all involve familiar trcinsformations. Children (or adults presumably) typically do not witness abrupt and major changes in a person’s

RACE OVER BODY BUILD

0 OCCUPATION OVER BODY BUILD

fa RACE

-.-

OVER OCCUPATION

3-YEAR OLDS

4-YEAR OLDS

7-YEAR OLDS

N = 25

N = 29

N=W

Fig. 2. Experiment 3. Mean number of choices (out of two) by age group and type of comparison. N o f e : From Hirschfeld (in press b).

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TABLE VI EXPERIMENT 3: MEANNUMBER OF CHOICES (OUT OF T w o ) BY QUESTIONA N D TYPE OF COMPARISON Question

lnheri tance Comparison Race over body build Race over occupation Occupation over body build

N

=

40

I .62 1.23 1.10

Growth N = 38 1.62 1.55 1.20

Note. Data from Hirschfeld (in press b).

intrinsic physical state or presentation of self. Accordingly, asking children to determine whether someone’s identity remains the same under several different meaningful but unfamiliar changes in appearance may confuse young subjects because it is not clear whether the pretransformed and posttransformed individual is supposed to be (as opposed to could be) the same individual (Bem, 1989). As observed earlier, children do have considerable experience with-and knowledge of-natural transformations whose scope is as dramatic as those used in most identity studies: Children are familiar with major physical and behavioral changes occurring across the iife span (i.e., in the context of growth) and across generations (i.e., in the context of inheritance). Young children appear to understand that these natural transformations are both lawful and nonrandom, and this understanding apparently involves domain-specific knowledge (Rosengren, Gelman, Kalish, & McCormick, 1991; Springer and Keil, 1989; Keil, 1989). For adults, these two sorts of resemblance (or canonical change) follow from the same source, biological relatedness. There is no a priori reason, however, to assume that young children make the same identification. The finding from this study that children do identify the preservation of features in growth and the preservation of features across generations, suggests that children view this preserved identity in both circumstances as resulting from the person’s intrinsic nature, in that by definition it is this intrinsic quality that is constant over both growth and inheritance. Young children, thus, appear to have a biological understanding of these social properties. In short, and in contrast to previous studies, I found that children do not consider all physical properties of a person to be equally informative of their identity, and by extension, equally resistant to modification. If

Child’s Representation of Human Groups

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children were focusing only on changes in corporeal appearance when making judgments about identity, they should find modifications in skin color as likely to signal a change in identity as changes in body build. Clearly this is not the case. It is important to keep in mind that body build is informative of individual identity and racial affiliation (Molnar, 1992). Moreover, variation in body build is attention-demanding for young children, and has inferential potential in the sense that stereotyping is predicated on it (Lerner, 1973). Body build is not a superficial property to either the biologist or the child. Yet it is consistently seen as irrelevant to identify by even 3-year olds. In contrast to earlier reports, by 4 years of age children find race to be more critical to identity than either occupation or body build. Finally, the congruence in performance in the growth and inheritance conditions points to a general biological understanding of essential traits . B.

EXPERIMENT 4: BIRTHA N D

THE

REPRODUCTION OF RACE

The results of Experiment 3 indicate that even young preschoolers consider a person’s racial identity to be immutable, related to family background, and derived from some physical properties but not others. These results paint a picture of children’s social inferencing that is quite different from that portrayed in most standard treatments. First, young children go beyond superficial reasoning when thinking about social difference. Second, this pattern of racial inference closely resembles expectations children have about nonhuman living things. How deep is this parallel? A more fully biological understanding of inheritance contains two components, namely, a belief in family resemblance and an understanding that the mechanism underlying this resemblance involves birth (Carey & Spelke, 1994). Several studies suggest that preschoolers indeed have such a grasp of inheritance, at least for nonhuman living things. Springer and Keil(1989)showed that preschool children expect offspring to resemble their parents. Gelman and Wellman (1991) found that preschoolers expect living kinds to possess an intrinsic potential that underlies expectations for continuity over the changes that occur during growth. Results from Experiment 3’s family condition suggest that children believe that children racially resemble their parents, and results from the growth condition suggest that preschoolers expect racial continuity during growth. But these results fall short of confirming that children believe that race is fully heritable, at least in the adult sense of heritable. As Carey and Spelke (1994) caution, Gelman and Wellman’s and Springer and Keil’s results d o not fully support the conclusion that young children have a biological understanding of inheritance, even for nonhuman living things.

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Children, for instance, may attribute family resemblance to social, not biological causes. Based on a switched-at-birth study, Solomon et al. (1993) reason that children’s expectations about inheritance are far less adultlike than Springer and Keil, Gelman and Wellman, or the results of Experiment 3 suggest. Their study indicates that preschool children did not expect a child to share many physical properties (including race) with his or her birth parents. Solomon et al.’s contention has two parts: first, children younger than 7 do not understand that child-parent resemblances are mediated by mechanisms of biological reproduction. Second, young children fail to differentiate between the heritability of psychological and biological properties. Considering these conflicting findings, I conducted a study to reexamine whether preschoolers’ beliefs about race include a more fully biological notion of inheritance. Four- and 5-year-olds heard a set of stories about two couples, one black (Mr. and Mrs. Smith) and the other white (Mr. and Mrs. Jones). As they listened to the story, they were introduced to drawings depicting each couple. The first story was about a white couple who adopt the infant of a black couple: These two people, Mr. and Mrs. Smith, had a baby girl [subject is shown a picture of a black couple]. That means that the baby came out of Mrs. Smith’s tummy. Right after it came out of her tummy, the baby went to live with these people, Mr. and Mrs. Jones [subject is shown a picture of a white couple]. The baby lived with them and Mr. and Mrs. Jones took care of her. They fed her. bought her clothes. and hugged her and kissed her when she was sad.

Children were then shown two pictures depicting a white school-age child and a black school-age child and asked which of these girls was the infant grown into school age. The second story was identical to the first except that the couples were reversed, so that the black couple adopt the white couple’s infant. If children’s responses are not governed by an understanding of the mediating role of birth in fixing family resemblances, they should reason that the school age child will racially resemble the adoptive parents. Conversely, if they grasp t h e role of birth, they should expect the school age child to racially resemble the birth parents. Children’s responses were scored in terms of how often they answered in accord with the “nature” o r “nurture” hypotheses. Scores were summed across the two stories, and children were credited with a nature bias only if they selected an infant of the biological parents’ race on both items. Similarly, they were scored as reasoning in accord with a nurture bias only if they selected an infant of the adoptive parents’ race on both items. As Fig. 3 shows, both older and younger subjects clearly favored the “nature” hypothesis,

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Child’s Representation of Human Groups

K!

Y0

E 8

NATURE NURTURE INCONSISTENT

0.50-

o.ooA

3-YEAR OLDS N = 15

4-YEAR OLDS N

= 18

5-YEAR OLDS N

= 14

Fig. 3. Experiment 4. Mean percentage ”nature,” “nurture.” and inconsistent choices by age group.

overwhelming choosing the children whose race matched the birth parents on both items. Children’s justifications were informative: 34% of the 5year-olds justified their responses in terms of the birth parents’ skin or hair color: 21% of their justifications cited experiential, emotional, or apparel associations (‘$1 like her better,” “she’s wearing the same outfit,” etc.), 9% made reference to the adoptive parents’ skin color, and 34% of the children offered no justification. Younger children’s justifications were very similar: 37% of the 4-year-oldsjustified their responses in terms of the birth parents’ skin or hair color, 30% in terms of experiential, emotional, or other associations, and 33% of the children offered no justification. In contrast to Solomon et al.’s (1993) findings, both 4- and 5-year-olds expect that identity-relevant physical properties such as skin color are fixed at birth. Neither age group believes that family members physically resemble each other because of shared life experiences. Preschoolers are thus essentialists with respect to race, believing that something causes members of a racial category to develop in a certain way. no matter what the environment in which members are raised. We can plausibly attribute children’s reasoning about the origins of racial differences to an expectation of some material, but nonobvious link between biological parents and their offspring. Following Gelman and Wellman (1991), I interpret this nonobvious link as a belief in an intrinsic essence shared by category members. In this case, the relevant category membership involves the

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Lawrence A. Hirschfeld

notion of family under a biological interpretation. The adultlike scope of this belief is underscored if we recall that in Experiment 3 , young children endorsed a single set of principles governing identity relations during growth and within a family.

C. EXPERIMENT 5: RICHSTRUCTURE A N D NONRACIAL SOCIALCATEGORIES Taken together, the results of Experiments 3 and 4 suggest that preschool children have a more richly structured and adultlike grasp of race than earlier researchers imagined. Still, there are interesting differences between children’s and adult models of social difference. Paradoxically, these differences may not turn on children having a less theory-like model of social difference, but one that is more theory-like than adults. For adults, occupation is a functional (or artifact-like) category and not a theory-laden one. Results of Experiment 3 raise the possibility that children in the youngest age group invest occupation with deeper importance than adults would. Recall that in that study, 4-year-olds not only found race more relevant to identity than body build, they also found it more informative than occupational apparel. Three-year-olds also reasoned that race was more informative of identity than body build, but unlike 4-yearolds, the younger preschoolers did not distinguish between the contributions race and occupational apparel make to identity. Two alternative interpretations come to mind. A first possibility is to attribute the difference in performance of youngerpreschoolers (who did not find race more important than occupation in judgments of identity) and older preschoolers (who did) to a developmental shift from a reliance on appearances to a reliance on a deeper understanding of social difference. Both groups appear to understand that the meaning of stable physical features (such as skin color and body build) is not the same, but only the older group expect that meaningful corporeal features are generally more important than sartorial differences. Thus, older but not younger preschoolers recognize afurther level of contrast between the features. Threeyear-olds, by distinguishing between the role race and body build play in identity, show that they discriminate attributes in terms of the relevance they have in human variation. In contrast, 4-year-olds, by distinguishing the role that corporeal features play relative to variable properties like costume, discriminate the relevance different classes of features have for understanding identity. A second possible interpretation is that the youngest subjects in Experiment 3 may give apparel such weight not because they invest clothing with special meaning, but because they invest occupation with a greater

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measure of significance. This is plausible for several reasons. Occupation is sartorially marked but it is not simply a category of clothing. As Experiments 1 and 2 showed, occupation is attention-demanding for children of this age, in part because it is an explanatory frame of reference: Occupation involves expectations about goal-directedness, areas of specific expertise, and habitual patterns of behavior (Hirschfeld, 1993). Young children who rely on occupational cues in judgments of identity may do so because of their sensitivity to function and teleological understanding. This interpretation suggests that the 3-year-olds’ performance may not reflect low level computational processes at all. Rather, the youngest children in Experiment 3 may be using an explanation-based inference strategy in thinking about social difference. In contrast to the strategy they use to reason about race (in which biological commonalities are thought to underlie category membership), here, children’s expectations may be derived from judgments about goals and habitual patterns of utilitarian behavior. Experiment 3, however, does not provide unambiguous evidence for this interpretation. Experiment 5 addresses this question directly. This study was similar to Experiment 3 in most respects except that instead of contrasting the relative contribution race, body build, and occupation make to identity, Experiment 5 explored the contribution that shared clothing color versus common occupation makes to identity. Two triad sets were used; the first consisted of a target picture of a boy dressed in yellow clothes and wearing a firefighter’s outfit and two comparison pictures, one depicting an adult firefighter dressed in red, the other portraying an adult in yellow civilian clothing. The second triad set consisted of a target picture that portrayed a girl wearing a pink dress and a waitress’s apron and cap, and comparison pictures depicted an adult waitress wearing a blue dress and an adult unmarked for occupation wearing a pink dress. Children were asked to choose which of the comparison pictures was a picture of the child as an adult. The dependent measure of interest was the number of times subjects selected shared occupation over shared clothing color. In general, children did not find occupation more salient to identity than shared clothing color, but separate analyses of each triad set, summarized in Fig. 4, revealed that occupation was significantly more relevant to the male items’ identity ( M = 1.20, out of 2 ) than to the female ( M = .81, out of 2). Occupation choices for the female items were significantly below the chance expectation of 1. Consistent with the findings of Experiment 3 (that 3-year-olds expect occupation to be as identity-relevant as race), occupation choices for the male items were reliably above chance for the 3-year-olds ( M = 1.36). Overall, occupation choices on the male items tended toward but were not significantly above chance ( M = 1.18).

Lawrence A. Hirschfeld

I62

crl

’1

X v

1

rA

u,0

8

W MALEITEMS

C2 FEMALE ITEMS

fL

crl eq

5z 0 3-YEAR OLDS

N = 11 Fig. 4. group.

4-YEAR OLDS

7-YEAR OLDS

N = 15

N = 12

Experiment 5. Mean number of occupation choices for each item set by age

Two interpretations of this item effect seem plausible. One possibility is that the male stimulus materials may have better conveyed the relevant cues about occupation than the female stimulus materials. A second possibility is that children consider occupational apparel more meaningful than clothing when drawing inferences about a male’s identity, but less meaningful when inferring a female’s identity. This interpretation would accordingly suggest that young children do discriminate between the contribution different kinds of apparel make to identity, it simply indicates that this expectation is complex. One reason to favor this second interpretation is that it is consistent with other work on children’s occupational stereotypes on the one hand, and their beliefs about gender status and role differences on the other (Blaske, 1984; Gettys 22 Cann, 1981; Cordua, McGraw, & Drabman, 1979). These studies indicate that young children find occupation to be more closely associated with males, whereas the home and child care is more closely associated with females. The results of Experiment 5 suggest that these beliefs not only condition children’s stereotypes, but also shape the inferences they draw about the contributions occupation and gender make to identity. Even if children’s beliefs about social difference are consistent with gender stereotypes widely, and redundantly, available in the environment,

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the question remains: Why would children develop a more theory-like set of expectations about a social category than the adult model warrants? Why, in short, would young children come to believe that occupation captures something deeper about an individual than adults accept? 1 can only speculate, but imagine that this has something to do with the function these social categories play in young children’s thought. The results of Experiments 1 , 2, and 3 suggest that race and occupation have quite different saliencies, and different meanings, for young children. Social descriptions are purposeful. Like all category-based reasoning, they allow the child to pick out relevant aspects of the environment and generalize over them. In the case of social categories, they allow the child to identify types of actors and appropriate explanations for their actions. Occupation categories are particularly salient because occupations represent recurring and purposeful behaviors (the police protect people, doctors cure them when they are ill, letter carriers deliver the mail, etc.). Occupation is a behaviorally relevant collectivity. The concept of race, in contrast, performs a quite different job. The notion of race is part of the child’s expanding social ontology, it is an early step in cataloging and discovering the relevant human groups. The fact that young children appear more concerned with developing a conceptual vocabulary for racial variation than a catalog of physical differences suggests that this impulse to find racial types involves expectations about global kinds of things, rather than the differentiation of specific ones (Hirschfeld, 1988, 1993; Mandler, Bauer, & McDonough, 1991). In the early stages of social category development, children may be unsure whether a category is a behaviorally relevant or kind-relevant one. Or they may be in the throes of determining what the basis of kindhood is, so that behaviorally based categories are construed as the template against which kinds of people are measured. I t is possible that this is why they come to momentarily invest occupation with the rich structure that they do. Further research is needed to decide these issues.

VI. Racial Thinking and Folk Biology: Areas of Divergence Experiments 3 and 4 provide considerable support for the claim that young children have both a theory- and adultlike understanding of race. Like adults, the framework that children use to organize their beliefs about race is an essentialist one. Essentialist reasoning is most clearly elaborated in common sense biology (Atran, 1990; Keil, 1989; Gelman & Coley,

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1990), but is not limited to biological thinking (Medin, 1989). Experiments 6 and 7 illustrate this by exploring one area in which biological and nonbiological essentialist reasoning do not converge. Essences function, among other ways, to model expectations about category identity. For example, Gelman & Wellman (1991) provide evidence that young children expect nonhuman living kinds to have an innate potential that governs subsequent development so that biological category identity is maintained despite variations in the environment or vagaries in the organism’s initial state. Children believe that tigers grow to be large, fierce, and capable of growling even though as cubs they are small, helpless, and purring. Children also expect tiger cubs to become tigers even if they are reared by lions. Adults explain this continuity across changes in behavior and appearance by appealing to the tiger’s underlying essence. Adults expect human races to have innate potential as well. The notion of racial potential, however, is recruited to serve a somewhat different purpose than the one studied by Gelman and Wellman. Unlike folk beliefs about nonhuman biological categories, common sense recognizes that members of different sorts of social categories are interfertile: doctors can mate with lawyers, blacks with whites, friendly people with grumpy ones. Innate racial potential comes into play as a way of determining the social category identity of offspring of mixed racial unions. According to both American folk belief and formal law, a “small” amount of one race’s “blood” renders one a member of that race, whereas a substantially greater proportion of another race’s “blood” does not affect category identity. In short, adults in the United States expect that some races have greater innate potential than others, in that they expect some races to contribute “more” to the physical identity of future generations than others (Davis, 1991; Molnar, 1992). Although racist construals of this are familiar (Nazism, the Klu Klux Klan, etc.), this expectation is restricted neither to fanatics nor marginal belief systems. The taxonomic corollary is codified in many state laws about racial status. [In Louisiana, e.g., a person is black if one of his or her great-grandparents were classified as black; thus, 1/16 black “blood” has more innate potential than 15/16 white “blood” (Ebony, 1983).] It is the method used by the United States Census Bureau for determining the race of children of interracial families (Molnar, 1992).3Given the wide distribution of the notion of innate racial potential (see Harris, 1964), it is of interest to know how and when children come to believe this aspect of the cultural model of race.

’

The one-drop rule derives from racist ideology, but all adherents to it are not by definition racist. See Footnote 5.

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Child’s Representation of Human Groups

A.

EXPERIMENT 6: CHILDREN’S BELIEFSABOUT POTENTIAL OF HUMAN RACE

THE

INNATE

Understanding of innate racial potential was assessed by asking children to judge what the offspring of racially mixed couples would look like.4 Two groups of school age children (7- to 8-year-olds and 11- to 12-yearolds) were shown pictures of four different couples one at a time, each holding an infant whose face and body were obscured. The couples were composed of either a black male and black female, a white male and a white female, a black male and a white female, or a white male and a black female. Each child was then shown pictures of three infants (a black infant, a white infant, and an infant that was intermediate between the black and white infants in terms of skin color, hair color, and hair quality), and asked which of the infants was the child of the target couple. All children judged that the black couple would have a black infant and the white couple a white infant. The contrast of interest involved the interracial couples. If children have no notion of racial potential (i.e., no strategy for determining heritability of racially relevant physical properties in ambiguous cases), they should choose at random. If children believe that offspring racially resemble both parents (i.e., both races have the same amount of potential), they should select the intermediate child. If children believe that whites possess more potential for determining identity-relevant properties than blacks, they should select the white child. If they believe that blacks have more potential than white, they should select the black infant. As Fig. 5 shows, younger subjects showed no clear preference for any outcome. They believed that it was equally likely that the offspring of a racially mixed couple would have black, white, or intermediate colored skin, indicating that they have no beliefs about racial potential. Older children, in contrast, overwhelmingly chose the black baby on both mixed-race pairs. Older children, accordingly, appear to have learned the culturally dominant model of innate social potential, believing that blacks have greater racial potential than whites. These results suggest that by early adolescence, children come to believe that each race has a distinct racial potential. Still, the data fall short of confirming this because it is not clear whether children are making inferences specifically about race or whether their judgments are about the inheritance of physical properties generally. For instance, children might expect darker colored properties to prevail over lighter colored properties. To rule out this possibility, the mixed-race task was combined with a mixed hair color task. Children were shown, one at a time, pictures This line of inquiry came out of discussions with Dan Sperber, whose suggestions 1 gratefully acknowledge.

Lawrence A. Hirschfeld

166

WHITE INTERMEDIATE BLACK

2 0.4

er;

20 g

0.2

0.0 7-8

YEARS

N=26

11-12 YEARS

N = 20

Fig. 5. Experiment 6. Mean proportion black. white. and intermediate infants were chosen by age group.

of couples whose hair color was either the same or different (blond mother and father, brunette mother and father, blond mother and brunette father. brunette mother and blond father). After viewing each adult pair, subjects were shown three infants (a blond infant, a brunette infant, and a redhaired infant) and asked which was the child of the adult pair. If children’s answers in the first task were based on a belief that darker colors dominate in inheritance over lighter colors, their inferences about hair and skin color should be largely the same. If children were reasoning specifically about race in the first task, their inferences about a socially less relevant property, hair color, should differ from those about a socially relevant property, skin color. As Table VII shows, younger children’s expectations about hair and skin color inheritance do not reliably differ; in both cases, they showed no preference for a particular outcome. In contrast to their judgments about skin color, older children also displayed no preference for any one outcome on the hair color task. That is, children in both age groups expected children of mixed hair color unions to be as likely to be blond, brunette, or redheaded. It is important to keep in mind that neither set of judgments is attributable to experience, inasmuch as neither corresponds to the biology of the phenomena. Older children’s belief that a child with one black and one white parent will have black features is not derived from direct observation of interracial children because there is no genetic dominance for skin color

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Child’s Representation of Human Groups

TABLE VII EXPERIMENT 6: M E A NPERCENTAGE EACH COLORWAS CHOSEN B Y AGE GROUP,HUMAN HAIRCOLORTASK 11-12 Years N = 19

7-8 Years N = 19 Hair color Blond Red Brunette

Hair color 34.2 44.7 2 I .o

Blond Red Brunette

26.5 29.4 44. I

(Bodmer & Cavalli-Sforza, 1976).Children whose parents have markedly different skin color tend to have skin color that is intermediate between that of each parent (Byard, 1981). On the other hand, older children’s belief that a child with one blond and one brunette parent will have red (or intermediate) hair color is not an induction from experience because darker hair tends to be dominant over lighter hair (Robins, 1991). If experience does not account for children’s judgments, what causes older children to believe that the children of interracial couples will have dark skin? In the United States, mixed-race children are considered black rrgnrdless of their physical appearance (Molnar, 1992). A considerable literature in psychology has demonstrated that even quite young children are sensitive to the politics of race (Cross, 1991). Thus, older children’s expectation that darker skin predominates over lighter skin appears to be shaped by knowledge of the social relations underlying an identity-relevant physical property, whereas their expectation that hair color is randomly inherited reflects a judgment about the lack of social relationship underlying a non-identity-relevant physical property. Children appear to be reasoning from social relations to biological principle. That is, biology provides the context for modeling these inferences. but the notion of race provides the principles that ultimately shape these judgments.

B.

EXPERIMENT 7: CHILDREN’S BELIEFABOUT THE INNATE SURFACE COLOR POTENTIAL OF NONHUMAN

The results of Experiment 6 show that during the late elementary school years children come to hold the adult model of innate racial potential, expecting that a child with one black parent is physically black. These

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findings cannot be attributed to a general strategy for predicting the inheritance of surface color properties because the same children do not believe that hair color has the same innate potential as skin color. I interpreted this as evidence that children base their inferences on the distinction between social relevant properties (i.e., skin color) and socially less relevant properties (i.e., hair color). There is one potential problem with this interpretation. Eleven-year-olds may simply have different rules governing the inheritance of skin color and the inheritance of hair color, without regard to whether one is socially relevant and the other less so. Although such a belief would have implications for the formation of later stereotypes, it would not be derived from prejudice itself. To explore this possibility, I conducted a second study that parallelled Experiment 6 in most respects, except that it used animals, not humans, as stimulus materials. Four different animal pairs were used; two consisted of animals whose surface color resulted from their skin color (alligators and elephants), and two whose surface color resulted from their hair color (bears and camels). Otherwise, the procedure was unchanged. Children were shown pictures of a mixed-color pair and told that they were the parents of a baby animal. Subjects were then shown pictures of three baby animals (one dark, one light, one intermediate) and asked to identify which one was the couple’s baby. Hair and skin color are both socially irrelevant for animals (at least for the species chosen). Thus, if subjects in Experiment 6 reasoned about skin color rather than race, per se, then their judgments should be the same, whatever the biological species in question. If, in contrast, subjects in Experiment 6 reasoned specifically about race, a uniquely human phenomenon, then their judgments about the heritability of skin color for humans and nonhuman animals should differ. Table VIII summarizes the findings. In contrast to their expectations about human stimuli, children’s judgments about animal skin color and hair color did not differ, indicating that grade schoolers believe that the principles governing the inheritance of an animal’s surface color are the same whether the color is derived from the tint of the animal’s skin or hair. As with Experiment 6, older and younger children’s judgments differed. Younger children showed, as they did in Experiment 6, no preference for one outcome over another. Older children, however, significantly preferred a single outcome-as they did in Experiment 6. But unlike their judgments about human skin color, in which they inferred that darker skin color dominates lighter shades, older children consistently believed that the offspring of animals of different colors would be an intermediate blend of their parents’ colors. Experiments 6 and 7 demonstrate that older school-age children expect

Child’s Representation of Human Groups

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TABLE VIII

7: MEANPERCENTAGE EACHCOLOROUTCOME EXPERIMENT SKIN,A N D HAIRTASKS WASCHOSENBY AGE GROUP,ANIMAL

tl-12 Years N = 19

7-8 Years N = 19

Skin color Hair color

Light

Blend

Dark

Light

Blend

Dark

,336 ,356

,327 ,298

,336 ,346

.200“ ,225

,625’ ,515‘

.175u

.200“

Significantly below chance at p < .OS. Significantly above chance at p i.01. ‘ Significantly below chance at p < . O l .

“

different races to have distinct innate potentials, in that the physical features associated with one race are thought to dominate over the physical features associated with other races. The notion is specific to beliefs about human race, and does not reflect a general strategy for understanding the biological inheritance of color-related physical properties. Thus, by early adolescence, children have developed the cognitive underpinnings of an important aspect of racism, namely, that a minority race’s innate potential “contaminates” or “overpowers” that of majority culture. Remarkably, this expectation-a subtle but important aspect of racism-develops in a liberal college community where children may well have not received direct tuition in calculating the innate potential of various races. These inferences cannot be attributed to experience, inasmuch as these judgments do not correspond to the biological regularities. Instead, children’s beliefs appear to represent spontaneous inductions, surely promoted by a pervasive racial climate in the United States, but facilitated by the early emerging integration of the child’s theory of biology and theory of society. In arguing that this reflects the internalization of the conceptual underpinnings of racism, am I going beyond the data? Experiments 6 and 7 did not probe children’s knowledge of the way society sorts people racially (i.e., they were not asked about the race of the child), but explored what children believed the offspring of interracial unions would Look like. I concluded that older subjects’ preference for the black-featured outcome is related to prejudice, not because children inferred that the child is racially (i.e., categorically or sociologically) black, but because they inferred that the black physical features dominate. These “biological” expectations about human inheritance are not based on knowledge of the

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physical world, but are governed by the children’s theory of society, shaped by a larger system of prejudiced belief.’ Racism is often linked with the claim that human variation is biologized (Atran, 1990; Banton, 1987; Gould, 1981; Guillaumin, 1980). The results of Experiments 6 and 7 suggest that it also involves the socializing of biology.

C. EXPERIMENT 8: SOCIALENVIRONMENT A N D CHILDREN’S BELIEFSABOUT INNATERACIALPOTENTIAL What makes a person black and what makes a person white, as already observed, involve cultural and legal criteria that may be only marginally linked to biology (Davis, 1991; Molnar, 1992). Thus, a child might believe that an interracial couple’s children were categorically (sociologically) black without expecting the children to have the physical features typically associated with blacks. In fact, a child whose knowledge was derived principally from experience of biological (not social) regularities would conclude precisely this, inasmuch as biologists studying mixed-race children have found that they tend to have skin color intermediate between that of their parents (Bodmer & Cavalli-Sforza, 1976). Although the school in which the testing was conducted was primarily white, there were several black children in each grade, and, importantly, there were mixed-race children in the classes from which participants were drawn. Presumably, then, encounters with mixed-race children are not sufficient to promote the biologically more accurate judgment that children’s skin color is a blend of their parents’. What role does experience play in shaping these beliefs? In particular, what sort of experience is crucial? It is possible that the critical set of experiences involve extended contact with a different subsystem (or subculture) of racial thinking. For example, in some minority communities subtle differences in skin color are highly salient (Russell, Wilson, & Hall, 1992). Does living in an environment where such differences are highly salient influence children’s knowledge of the biology of racially relevant physical features? By extension, are majority and minority children’s biological beliefs equally gov-

’

The question is not whether someone with one black parent is black, but whether someone with one black parent will have black physical features. The issue of social identity is distinct from the question of physical property inheritance. The notion that someone with any black heritage is categorically black is “rooted in the one-drop rule of racial identity, which, more than any other factor, has shaped the development of racial identity in America. Although it had its origins in racism, today the rule is staunchly defended by most members of the Black community” (Russell et al., 1992, p. 74). The issue under discussion in this chapter is whether biological expectations about property inheritance are shaped by sociopolitical factors. The results of Experiments 6 and 7 suggest that they are.

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Child's Representation of Human Groups

erned by dominant social relations? Experiment 8 explores this issue directly. Children attending a largely (54% black) minority school in a blue-collar community near the majority community from which subjects in the earlier studies were drawn participated in the same tasks used in Experiments 6 and 7. Children were asked to infer what the offspring of couples differing in one color property would look like under four conditions: mixed-skin human couples, mixed-hair human couples, mixed-skin animal couples, and mixed-hair animal couples. Table IX summarizes the results. Unlike children living in the majority community, there were no reliable age differences in these children's judgments-older and younger children showed largely the same preferences. In all but one condition, minority community children expected that mixed couples (no matter whether the contrast was hair or skin, or whether the couples were animal or human) would have offspring whose surface color was an intermediate blend of each parent's color. The exception was the human hair color condition, in which younger subjects believed that dark hair predominates, whereas older children believed that light hair dominates. The pattern of judgments is clearer if we directly compare majority and minority community children's performances. Generally, younger majority community children chose at random, hence displaying no particular preference in any condition. In contrast, younger minority community children were more likely to select the intermediate (or blend) outcome, though the effect was strongest in the human skin color condition. Thus,

TABLE IX EXPERIMENT 8: MEANPROPORTION EACHCOLOR OUTCOME WAS COMMUNITY ENVIRONMENT CHOSENBY AGE GROUP,MINORITY

11-12 Years N = 20

7-8 Years N = 17 Light

Blend

Dark

Light

Blend

Dark

,276 .342 ,289 ,237

.487 ,552' ,474 ,237

,276 . I 10' .237 ,526"

,125'

,750" ,475 .750" ,225

,129 ,375

~

Skin color: Animals

Skin color: Humans Hair color: Animals Hair Color: Human

(' significantly above chance at p < .01 * significantly below chance at p < .01. ' significantly above chance at p < .05

. I 50' .112h

,550"

.13?

,225

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Lawrence A. Hirschfeld

younger minority community children chose the dark and light skin infants reliably less often than did their majority community counterparts. There were no other significant differences between younger majority and minority community children’s inferences. Older majority community children overwhelmingly selected the black infant in the human skin color condition, and they were reliably higher in this choice than minority children. Older minority community children were more likely to choose the intermediate infant in the human skin color condition, and they were significantly more likely to do so than majority children. Finally, older majority community children chose the dark hair infant more often than minority community children. The simplest explanation of these differences-that is, differential experience with mixed-race children-is not compelling. Interracial families were uncommon but present in both schools, so that all subjects had direct experience with biracial children. Moreover, it is plausible that children in the majority school had as much or more experience with children whose parents have different hair color. Yet their judgments in the human hair color condition were no more biologically accurate than their inferences about skin color, nor were they more biologically accurate than minority community children. The crucial difference may be different levels of endorsement of a particular (and prejudiced) model of social difference in the two populations. This is not meant to imply that minority children’s beliefs about race are not shaped by the pervasive prejudice of American society. Many researchers have interpreted black children’s repeatedly documented preference for white dolls as evidence of such influence (for a review, see Cross, 1991). Instead, at issue here is whether one aspect of the widespread (and prejudiced) model-that social relations of domination and power shape biological expectations-is equally held by members of the majority and minority communities. The results of Experiment 8 suggest that the notion of uneven racial potential may not be equally distributed. Clearly, more work is needed before this can be established. These results also raise another issue that will require further research to resolve. Most studies on racial concept formation contrast the performance of children of different races on various tasks. The implication is that the relevant level of contrast is between individuals. In Experiment 8, we did not select children on the basis of the experimenter’s intuitions about their race. The reason for this was that race is a variable quality. Contrary to common sense (codified in much legal discourse), an individual’s race is not fixed. Considerable work in the social sciences now agrees that judgments of race and ethnicity are task-specific (Sollors, 1989;Gold-

173

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berg, 1990; Root, 1992). Accordingly, the results from Experiment 8 were reported from the perspective of cultural environment (minority vs. majority community children), and not from the perspective of individuals (specified in terms of their race). We can test whether this is a valid strategy by comparing the performance of black and white children in the minority community (using school records to determine race of subject). If individuals are the appropriate units of comparison, we would expect white subjects, whatever their environment, to respond similarly. If social milieu, and not individual racial status, is the appropriate unit of contrast, then whites and blacks living in the minority environment should not differ in their judgments, and both groups should differ in their inductions from children living in the majority environment. As Table X shows, this is what was obtained: Black and white children living in the minority community did not differ in their expectations. The crucial distinction, accordingly, appears to be between the cultural environments in which the children live, not children’s racial status per se. Again, more research is needed.

TABLE X EXPERIMENT 8: MEANPROPORTION EACHCGLCIR O U T C O M E WAS CHOSEN BY AGE GROUPA N D RACEOF SUBJECT, MINORITY COMMUNITY ENVIRONMENT

11-12 Years N = 20

7-8 Years N = 19

Skin color: Animals Whites Blacks Skin color: Humans Whites Blacks Hair color: Animals Whites Blacks Hair Color: Human Whites Blacks

Light

Blend

Dark

Light

Blend

Dark

,275 .278

,525

,275 ,278

.I50

,444

,675 ,800

,100

.350 ,333

,600 ,500

,050 ,166

,200 ,100

,400

,400

,550

,350

.300 .278

,450 ,500

so0 .222

.I50 .I25

,675 ,825

.I75

,300 . I 67

,250 ,222

,450

,200 ,250

,200 ,250

,600

.610

,100

,175

,050

,500

I74

Lawrence A. Hirschfeld

VII.

Conclusion

A widely accepted view of social development, motivated by Piagetian expectations about children’s conceptual limitations, holds that young children overrely on external appearances when deriving and representing social categories. By the same token, young children are thought incapable of construing social difference in terms of the nonobvious and abstract criteria embedded in adult expectations about social entities. Accordingly, children are viewed as insensitive to the ontological commitment to group o r collective that is integral to an adult understanding of social events. Finally, this view of social development predicts that children will attend to various social dimensions to the extent that these dimensions are physically prominent. Each study reported here challenges some aspect of this view of cognitive development. Experiments 1 and 2 demonstrate that children do not find all conspicuous social properties equally salient, nor do young children construe the conceptual salience of social categories in terms of their physical correlates. The studies establish that perceptual information may not in fact be crucial to the representation and derivation of several important social dimensions, especially race. Experiment 3 explored children’s judgments about the inheritance and growth of social properties, and provides considerable support for the claim that young children have a theory-like and principled understanding of race. The switched-at-birth study in Experiment 4 indicates that this understanding is not only richly structured but involves expectations of a mediating mechanism, specifically a nonobvious essence that is transmitted and fixed through biological reproduction and birth. The results of Experiment 5 suggest that young children also believe that nonracial social categories may be organized around theoretical principles. Experiments 6 and 7 establish that having a biological understanding of race does not mean that all principles governing nonhuman biological reasoning apply to social kinds. In particular, these studies show that social categories are organized around entities, like race, that have social rather than strictly biological relevance. Experiment 8 shows that the environment in which social categories develop influences how biological and social knowledge are integrated. All three studies have direct implications for our understanding of prejudice. Table XI summarizes the main findings. Taken together, these studies provide considerable evidence against the standard Piagetian view, and considerable support for the alternative model of young children’s understanding of human variation and human groupings outlined in the introduction t o this chapter. According to this model, young children’s social cognitions are more richly structured and

TABLE XI SUMMARY OF MAIN FINDINGS ~~

Age Pattern of inference

3-Year-olds

4-Year-olds

5-Year-olds

7-Year-olds

I?-Year-olds

Discriminate between attributes Immutability of relevant physical features Domain-specific causal principles: Growth Inheritance Discriminate between classes of attributes Domain-specific mechanisms of reproduction Uneven innate racial potential

X

X X

X X

X X

X X

X X X X

X X X X

X X X X

X X X X X

X X

x

Lawrence A. Hirschfeld

176

nuanced than earlier research suggested. Two features of the child’s representation of human groups are most striking. First, children appear to recognize that some human collectivities are based on nonobvious commonalities critical to each member’s intrinsic nature. Moreover, young American children’s expectations about what is socially intrinsic seem to be sensitive to a wider array of deep commonalities than American adult belief allows: different cultural traditions construe different social dimensions as intrinsic and others as contingent-in South Asia occupation is an aspect of intrinsic nature, in the United States it appears to be so only for 3-year-olds. Second, young children conceptualize some social categories, notably race, as involving nonobvious mediating mechanisms that are thought to explain the causal relations among members. The young child’s notion of race is not an empirical generalization made from observed differences, but a theory-like account of human differences, whether they are perceptually marked or not. These studies raise a number of questions and possibilities, providing the basis for further research in several areas. These include explorations into the nature of prejudice, the relationship between individual social representations and aggregate historical and social phenomena, and issues in cognitive representation, particularly the relationship between cognitive domains and the most appropriate level of domain analysis. A.

AND CONCEPTUAL ORGANIZATION PREJUDICE

THE

NATURE

OF

Psychologists have long argued that general categorization processes contribute to the formation of social stereotypes (Hamilton & Trolier, 1986; Fiske & Taylor, 1991; Rothbart & Taylor, 1990). A major tradition in social psychology has sought to understand what individual differences predict differences in stereotyping and prejudice. Some researchers have preferred personality factors, some macro-social factors, and some information processing factors. Still, no consensus on the roots of stereotyping and prejudice has emerged. One reason may be the peculiar nature of early prejudices. Several studies have uncovered a paradoxical relationship between young children’s racial prejudices and their behaviors. Although preschool children maintain quite strident ethnic and racial biases (Clark & Clark, 1940; Horowitz, 1939; Aboud, 1988; Katz, 1983), in everyday contexts, preschool children do not find race a particularly relevant dimension on which to sort people, particularly when it comes to choosing playmates (Doyle, 1983; Finkelstein & Haskins, 1983; Singleton & Asher, 1979; McCandless & Hoyt, 1961; Lambert & Tachuchi, 1956).No compelling explanation for this lack of correlation between attitude and behavior

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177

has been offered. Clearly it is not because young children’s attitudes generally do not motivate behavior, because gender stereotypes have been shown to shape playmate choice (Fagot, Leinbach, & Hagan, 1986; Katz, 1983). The alternative model of young children’s racial categories 1 propose may provide an explanation. Young children’s racial categories do not involve a discovery of perceptual regularities, but are initially aimed at specifying a social ontology. In this regard, young children seem to be more concerned with elaborating concepts at a higher level of generality that are relevant to a theory of society and developing a conceptual vocabulary for racial variation, than differentiating specific concepts and cataloging physical differences. Racial prejudice may not translate into biased patterns of behavior because young children may be unsure what the membership of any given racial category is. In short, young children’s racial beliefs might not influence behavior because they may fail to unambiguously instantiate racial category membership. By recasting our view of children’s racial cognitions, we may be in a position to resolve a number of the questions about children’s social cognition that have long remained open. BETWEEN MODELS OF SOCIAL COGNITION B. RELATIONSHIP A N D HISTORICAL A N D ANTHROPOLOGICAL SCHOLARSHIP

Increasingly, social historians and anthropologists argue that race is a constructed phenomenon, varying according to the social and political context in which it is embedded (Stoler, 1992; Goldberg, 1990; Sollors, 1989). Such claims do not imply that there is no common thread in culturally and historical specific racial discourses. Rather, these proposals suggest that race is largely independent of the regularities in physiognomy that adults typically associate with it. From a historical perspective, race is not a visual ideology (Stoler, 1992). Clearly, this view of race resonates with the one proposed here. According to my model, young children do not discover races because they are there to be found at the raw perceptual level, they discover them because they are integral to finding the sorts of things there are in everyday social experience. That this everyday social experience is influenced by historical and cultural forces is not surprising. What is interesting is that conceptual organization (on the individual level) and culture (on the aggregate level) seem to dovetail in this instance: An essentialist view of race permits a flexibility in reasoning that a strictly physicalist interpretation does not allow. As social theorists have established, this flexibility is crucial to adult conceptualizations of race: Physical features are diagnostic

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but not defining of race because one can be “mistaken,” on the basis of physical inspection alone, about the race of another. Intriguingly, this flexibility seems to have its roots in children’s beliefs about the same phenomenon. Further research would be well worth pursuing. C.

A N D LEVELSOF SOCIALCOGNITION CONCEPTUAL ANALYSIS

Recent work in conceptual development provides compelling evidence that knowledge of the world develops through the agency of specialized faculties for understanding. Often, these specialized devices are identified with naive theories (Carey, 1985; Wellman & Gelman, 1992). According to this view, each domain (or naive theory) is characterized by a unique set of causal principles and ontological commitments (Carey, 1985; R. Gelman. 1990). One means by which knowledge is extended (and conceptual change occurs) is through integration of domain-specific principles of causality across kinds of phenomena, where better-grounded domains become the explanatory model for less well-grounded ones (Carey & Spelke, 1994). For example, Carey (1985) has argued that preschoolers do not have an autonomous domain of biology. Instead, grade-school children’s naive biology emerges out of an “intuitive theory of behavior” (i.e., a naive psychology). The “causal structure in which [biological] phenomena are embedded is social and psychological” (Carey 1985, p. 188). As observed earlier, this proposal explains parallel patterns of reasoning in different areas of conceptualization as a function of knowledge transfer. The mechanism that produces knowledge transfer is analogical reasoning. As was also observed earlier, scholars from disparate traditions have proposed that the biological understanding of race results from just such analogical reasoning, in which human racial variation becomes identified with morphological regularities in nonhuman species. Neither of these claims has proved to be compelling. The problem with the general claim that knowledge transfer results from analogical reasoning is that such transfers have been as difficult to replicate under controlled conditions as they are easy to identify in natural thought. The problem with the specific claim that principles of biology infuse racial understanding is that biological reasoning appears to be subsumed to racial thinking, not vice versa. Recent proposals about conceptual architecture are informative here. Keil (1994), Sperber (1994), and Leslie (1994) have suggested that naive theories do not derive from the child’s initial state so much as they are precipitates of more abstract mechanisms of conceptual organization. Keil (1994, p. 252) calls such mechanisms modes of consfrual, defining them as “opportunistic, exploratory entities that are constantly trying to find

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I79

resonances with aspects of real world structure.” This model has implications for the way knowledge is coordinated and integrated across domains. For example, the notion of modes of construal raises the possibility that natural knowledge transfer may be more apparent than real. Essentialist reasoning is characteristic of both biological and racial thinking. Studies reviewed in this chapter strongly indicate that essentialist reasoning is not a late, but an early emerging feature of beliefs about race. This finding suggests that essentialist principles of folk biology either transfer spontaneously to racial thinking or that there is a parallel (and almost simultaneous) instantiation of essentialist reasoning in both folk biology and racial belief. Studies discussed in this chapter lend some support to the second claim. The notion of knowledge transfer in the biology-to-race model relies on the recognition of a physical analogue across two comparable clusters of perceptual features. These readily perceptible phenotypic gaps are then taken as evidence of different populations. Yet the studies presented earlier indicate that perceptual information does not enjoy the same primacy in the development of racial thinking that it does in the emergence of naive biology. Thus, parallels in biological and racial reasoning may not represent an easy transfer of knowledge at all. Instead, essentialist reasoning in the two domains may signal the parallel endorsement of an essential mode of construal in distinct faculties, suggesting that essentialism, as Medin (1989)and later Keil(1994) supposed, is initially independent of biology. Again, only further research can resolve this issue. As with the earlier questions, this discussion points to the possibility that a more detailed understanding of the child’s theory of society may yield insights that have far-reaching implications. Race and other aspects of the child’s model of society are not marginal to the study of cognition, but represent a richly structured and extensively elaborated series of common sense beliefs about the nature of difference and similitude. This should not come as a surprise; after all, our principal metaphor for similarity is kinship, our primary metaphors of difference are race and species. In all these instances, it is the notion of corporate collectivity, linked by an intrinsic commonality, that motivates the image. Understanding where such beliefs come from and how they develop is squarely within cognitive psychology’s mission.

ACKNOWLEDGMENTS Research described in this chapter was supported by grants from the National Science Foundation ( I N T 8814397 and RCD 8751136). the Fondation Fyssen, and the Office of Vice President. University of Michigan. I am grateful to Evan Heit. Doug Medin. Heidi Schweingruber. and Ann Stoler for their comments on earlier drafts of this chapter.

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Ross, L. (1981). The “intuitive scientists” formulation and its developmental implications. In J. Flavell & L. Ross (Eds.), Social cognitive development: Frontiers and possible futures. New York: Cambridge University Press. Rothbart, M. (1981). Memory processes and social beliefs. In D. Hamilton (Ed.), Cognitive processes in stereotyping and intergroup behauior. Hillsdale, NJ: Erlbaum. Rothbart, M., & Taylor, M. (1990). Category labels and social reality: Do we view social categories as natural kinds? In G. Semin & K. Fiedler (Eds.), Language and social cognition. London: Sage. Russell, K., Wilson, M., & Hall, R. (1992). The color complex: The politics ofskin color among African Americans. New York: Harcourt Brace Jovanovich. Sartre, J.-P. (1948). Anti-semite and Jew. New York: Schocken Press. Semaj, L. (1980).The development of racial evaluation and preference: Acognitive approach. The Journal of Black Psychology, 6 , 59-79. Singleton, L., and Asher, S. (1979). Racial integration and children’s peer preferences: An investigation of developmental and cohort differences. Child Development, 50(4), 936-941. Slaby, R., & Frey, K . (1975). Development of gender constancy and selective attention to same-sex models. Child Development, 46, 849-856. Sollors, W. (1989). The invention of ethnicitv. New York: Oxford University Press. Solomon, G., Johnson, S., Zaitchik, D., & Carey, S. (1993). The young child’s conception of inheritance. Paper presented at Society for Research on Child Development, New Orleans. April. Sorce, J . (1979). The role of physiognomy in the development of racial awareness. The Journal of Genetic Psychology, 134, 33-41, Spelke, E. (1991).Physical knowledge in infancy: Reflections on Piaget’s theory. In S. Carey & R. Gelman (Eds.), The epigenesis of mind: Essays on biology and cognition. Hillsdale, NJ: Erlbaum. Sperber, D. (1994). The modularity of thought and the epidemiology of representations. In L. Hirschfeld & S. Gelman (Eds.), Mapping [he mind: Domain speciJicity in cognition and culture. New York: Cambridge University Press. Springer. K., & Keil, F. (1989). On the development of biologically specific beliefs: The case of inheritance. Child Development, 60, 637-648. Springer, K., & Keil, F. (1991). Early differentiation of causal mechanisms appropriate t o biological and nonbiological kinds. Child Development, 62. 767-781. Stoler, A. (1992). Sexual affronts and racial frontiers: European identities and the cultural politics of exclusions in colonial Southeast Asia. Comparative Study in Society and History, 34, 514-551. Taylor, S . (1981). A categorization approach to stereotyping. In D. Hamilton (Ed.), Cognitive processes in stereotyping and intergroup behavior. Hillsdale, NJ: Erlbaum. Turiel, E. (1983). Interaction and development in social cognition. In E. T. Higgins, D. Ruble, & W. Hartup (Eds.), Social cognition and social developmen!: A sociocultural perspective. New York: Cambridge University Press. van den Berghe, P. (1967). Race and racism: A comparative perspective, New York: Wiley. Vaughan, G . (1987). A social psychological model of ethnic identity. In J. Phinney & M. Rotheram (Eds.), Children’s ethnic socialization. Beverly Hills, CA: Sage. Vosniadou. S., & Ortony, A. (1989). Similarity and analogical reasoning. New York: Cambridge University Press. Watson-Gegeo. A,, & Gegeo, D. (1986). Calling-out and repeating routines in Kwara’ae children’s language socialization. In B. Schieffelin & E. Ochs (Eds.), Language socialization across cultures. New York: Cambridge University Press.

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DIAGNOSTIC REASONING AND MEDICAL EXPERTISE Vimla L. Patel, Jose F . Arocha, and David R . Kaufman

1. Introduction

The study of medical cognition has been the subject of formal inquiry for more than 30 years. The term medical cognition refers to studies of cognitive processes, such as perception, comprehension, reasoning, decision making, and problem solving in medical practice itself or in tasks representative of medical practice. These studies use subjects who work in medicine, including medical students, physicians, and biomedical scientists. Cognitive psychologists have often used artificial medical stimuli (arbitrary disease and symptom pairings) to study categorization and prediction in general (e.g., Medin & Edelson, 1988; Gluck & Bower, 1988), without studying medical practitioners or realistic tasks. Although this work has been very influential, these efforts are not studies of medical cognition. Cognitive science in medicine refers to a broader discipline encompassing medical artificial intelligence (Clancey & Shortliffe, 1984), philosophy in medicine (Schaffner, 1986), medical linguistics (Baud, Rassinoux, & Scherrer, 1992), and medical cognition (Evans & Patel, 1992). There are two principal experimental approaches that have been used to study medical cognition: a decision making and judgment perspective in which a subjects’ decisions are contrasted with a normative model, based on probability theory, indicating optimal choices under conditions of uncertainty (Dawes, 1988); and a problem-solving cognitive science THE PSYCHOLOGY OF LEARNING 4 N D MOTIVATION VOL 7 1

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approach in which the focus is on a description of cognitive processes in reasoning tasks, making use of protocol-analytic techniques (Ericsson & Simon, 1993) and the development of cognitive models of performance. The focus in this chapter is on the cognitive science perspective. In addition, we can characterize two categories of research within this perspective, research targeted at understanding the structure and use of basic science knowledge in medical tasks (Patel, Evans, & Groen, 1989; Feltovich, Spiro, & Coulson, 1989; Boshuizen & Schmidt, 1992) and research investigating the process of diagnostic reasoning (Elstein, Shulman, & Sprafka, 1978; Feltovich, Johnson, Moller, & Swanson, 1984; Patel & Groen, 1986; Joseph & Patel, 1990; Arocha, Patel, & Patel, 1993). Cognitive science research in medicine has a dual purpose similar to that in other complex domains. The first purpose is to develop theoretical models of cognition and computational models unique to medicine. The second purpose is to engage in empirical research, exploiting the domain of medicine as a knowledge-rich and semantically complex domain to further our understanding of cognition and computation. For example, cognitive research into medical expertise and medical artificial intelligence have made significant contributions to understanding the nature of expertise and artificial intelligence, respectively. The primary purpose of this chapter is to characterize the current state of knowledge in diagnostic reasoning. The study of expertise is one of the principal paradigms in problemsolving research. Investigators in this area seek to understand what distinguishes outstanding individuals in a domain from less outstanding individuals (Ericsson & Smith, 1991). Since the pioneering research of deGroot (1965) and the subsequent studies of Chase and Simon (1973) in chess, investigations of expertise have spanned the range of content domains including physics (e.g., Larkin, McDermott, Simon, & Simon, 1980; Anzai, 1991), sports (Allard & Starkes, 1991), music (Sloboda, 19911, and medicine (Patel & Groen, 1991a). Two edited books-Chi, Glaser, and Farr (1988) and Ericsson and Smith (1991)-provide excellent overviews of the area. The designation of “expert” can be a function of achieving a certain level of performance, as exemplified by Elo ratings in chess, or of being certified by a sanctioned licensing body, as is characteristic of medicine. In either case, the achievement of expertise requires about 10 years of full-time performance (Hayes, 1981). In a complex domain, expertise is not a monolithic entity, there is considerable variation and specialization. In many disciplines, individuals can be expected to perform at an expert level, only within a very narrow context. Expert knowledge is organized functionally in such a way to support specific reasoning tasks. This explains why performance differ-

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ences are observed between genetic counselors and academic geneticists (Smith, 1990), and between cardiology researchers and practitioners (Patel & Groen, 1991a). The issue of between-expert variability is an important one in investigations of medical expertise. Expert specialists often have a particular subspecialization. For example, certain endocrinologists are diabetes specialists and others are authorities on lipid metabolism disorders. Although many studies contrast an expert group with a novice group, expertise is best viewed as a continuum with a number of intermediate levels that result in unique performance characteristics. The development of expertise is marked by specific transitions corresponding to reorganizations of knowledge and nonmonotonic increases in mastery of domainspecific tasks (Patel & Groen, 1991b). There are a number of expert characteristics that have a certain degree of generality across domains. A consistent theme across studies of the development of expertise has been the role that the evolution of knowledge structures has in facilitating the recognition of significant objects within a problem and in enhancing the ability to recognize typical situations. The superior organization of expert knowledge is manifested in experts’ ability to categorize stimuli at a highly principled and functional level of abstraction (Chi, Feltovich, & Glaser, 1981), exhibit enhanced recall for domain information (Chase & Simon, 1973), and engage in forward-directed reasoning, from givens in the problem to unknowns, in routine problemsolving tasks (Larkin et al., 1980). The latter two aspects, enhanced recall and directionatity of reasoning, have been of particular importance and a source of some controversy in medical diagnostic reasoning. These issues are dealt with in some detail in this chapter.

11. The Task Domain of Medical Diagnosis

Medical knowledge consists of two types of knowledge: clinical knowledge, including knowledge of disease entities and associated findings, and basic science knowledge, incorporating subject matter such as biochemistry, anatomy, and physiology. There are two broad perspectives on the nature of diagnostic reasoning: the fault model and the heuristic classi$cation model. The first model suggests that medical diagnosis is akin to diagnostic troubleshooting in electronics, with a primary goal of finding the structural fault or systemic perturbation (Feinstein, 1973; Boshuizen & Schmidt, 1992). From this perspective, clinical and biomedical knowledge become intricately intertwined, providing medical practice with a sound scientific basis. This model suggests that biomedical and clinical knowledge could be seamlessly integrated into a coherent knowledge structure

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that supports all cognitive aspects of medical practice, such as diagnostic and therapeutic reasoning. The second model views diagnostic reasoning as a process of heuristic classification involving the instantiation of specific slots in a disease schema (Clancey, 1988). The primary goal of diagnostic reasoning is to classify a cluster of patient findings as belonging to a specific disease category. From this perspective, the diagnostic reasoning process can be viewed as a process of coordinating theory and evidence rather than one of finding fault in the system. As expertise develops, the disease knowledge of a clinician becomes more dependent on clinical experience; clinical problem solving is increasingly guided by the use of exemplars and analogy, and is less dependent on a functional understanding of the system in question. That is not to say that basic science does not play an important role in medicine, rather, the process of diagnosis, particularly in dealing with routine problems, is essentially one of classification. Basic science knowledge is important in resolving anomalies and is essential in therapeutic contexts. These two models of reasoning have been discussed in medical artificial intelligence in the context of deep and shallow systems of reasoning (Chandrasakeran, Smith, & Sticklen, 1989). A shallow system, such as MYCIN and INTERNIST, reasons by relating observations to intermediate hypotheses, which partition the problem space, and further associating intermediate hypotheses with diagnostic hypotheses. The knowledge base would include only entities related to taxonomic classification: diagnostic hypotheses and clinical findings. The deep and shallow distinction has been raised in the context of the control architecture differences between first and second generation expert systems. Therefore, a system such as NEOMYCIN, which has a complex control architecture but which is guided by heuristic classification, can be classified as being of intermediate depth. Chandrasakeran (Chandrasakeran et al., 1989) characterizes a deep system as similar to systems used in qualitative physics (Bobrow, 1985) that embody causal mental models. Systems such as MDX-2 (Chandrasakeran et al., 1989) or QSlM (Kuipers, 1987) have explicit representations of structural components and their relations, the functions of these components (in essence their purpose), and their relationship to behavioral states. The causal and diagnostic knowledge can be generated by running o r simulating the system and qualitatively deriving behavioral sequences that can identify and explain the malfunction. We adhere to the second model, which suggests that diagnostic reasoning is a process of heuristic classification. It is our belief that biomedical knowledge and clinical knowledge constitute two distinct “worlds” that bridge at discrete points of correspondence rather than form one integrated

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knowledge base (Patel, Evans, & Groen, 1989). Our conclusion is based on an ontological characterization of the domains and on psychological evidence suggesting that basic science knowledge is not easily integrated in clinical contexts. The clinical description and physiological or biochemical description are at different levels of abstraction (detail)-they belong to different ontological categories. Biomedical knowledge is of a qualitatively different nature, embodying elements of causal mechanisms and characterizing patterns of perturbation in function and structure (Schaffner, 1986). Medicine draws on different sources of knowledge from the biomedical and to a lesser degree the physical sciences. This knowledge can be arranged in a hierarchical schema of the scientific sources (Blois, 1990). At the bottom is atomic physics, where matter is described with reference to atoms and their constituent properties. At each higher level in the hierarchy, there are newly emergent properties not entirely predictable from lower levels. Each new level has different conceptual entities and a unique language of description. Higher levels introduce more uncertainty and a greater degree of inexactness in ascribing causality. At the clinical level, models of disease are commonly described in terms of associations between clinical findings and diagnoses. The physician is not merely matching findings to diagnostic categories, rather, he or she is developing a model of the patient, based on a sequence of findings with a specific temporal order, and coupling this information with the patient’s past medical history, family history, physical examination, and laboratory findings. Investigations of medical problem solving provide some evidence to support the contention that biomedical knowledge is not used optimally in clinical contexts (cf. Patel, Evans, & Groen, 1989).The research findings suggest that basic science is used differentially in different tasks and in different medical domains (cf. Patel & Groen, 1986; Lesgold et al., 1988), experts and novices differ in their use of basic science, and, in many instances, basic science knowledge may actually interfere with clinical problem solving (Patel, Evans, & Groen, 1989). This is most apparent in tasks where subjects, students in particular, are explicitly asked to provide a basic science explanation of a clinical problem. For example, we have found that the coherence of an explanation is often reduced when basic science concepts are employed. That is to say, the overall explanation becomes fragmented into discrete and isolated chains of inference, some correct, others partially correct, and others incorrect. The evidence also suggests that students possess substantial inert knowledge that frustrates their ability to apply specific biomedical concepts to clinical problemsolving tasks (Patel, Kaufman, & Magder, 1991). Inert knowledge refers to domain knowledge acquired from texts or lectures that has not yet been exercised in problem-solving situations. The problems associated with the

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use of basic science knowledge appear to be at least equally pervasive in problem-based medical schools that have an integrated curricula (basic science and clinical content are taught together) as compared with medical schools that partition their curriculum into a basic science section followed by a clinical section (Patel, Groen, & Norman, 1993). Medical problems can be characterized as ill-structured, in the sense that the initial states, the definite goal state, and the necessary constraints are unknown at the beginning of the problem-solving process (Simon, 1973). Cognitive domains or tasks can be characterized on a continuum from well-structured tasks, where the problem space is well delineated to ill-structured domains, which necessitate considerable structuring of the space of possibilities. Physics is an example of a well-structured domain where the constraints and possible operations are well understood. There are, of course, problem-solving tasks and content areas within any domain that are somewhat less structured. Writing or discourse production can be construed as an ill-structured task at the other end of the continuum, in which there are few initial constraints. In a diagnostic situation, the problem space of potential findings and associated diagnoses is enormous. The problem space becomes defined through the imposition of a set of plausible constraints that facilitate the application of specific decision strategies (Pople, 1982). Plausible constraints are produced, for example, by narrowing the range of possible diagnostic solutions by evoking categories of disorders (e.g., cardiovascular problems) or through the elimination of classes of problems. Given the complexity of the domain of medicine, there is a need for a framework for differentiating between different classes of conceptual entities. Evans and Gadd (1989) propose an epistemological framework that differentiates four levels into which clinical knowledge is organized in a medical problem-solving context. Observations are units of information that are recognized as potentially relevant in the problem-solving context; however, they do not constitute clinically useful facts. Findings are composed of observations that have potential clinical significance. Establishing a finding reflects a decision made by a physician that an array of data contains a significant cue or cues that need to be taken into account. Facets consist of clusters of findings that are suggestive of prediagnostic interpretations. They reflect general pathological descriptions, such as aortic insufficiency, or categorical descriptions, such as endocrine problem. They are also interim hypotheses that divide the information in the problem into sets of manageable subproblems and suggest possible solutions. Facets vary in terms of their levels of abstraction. A high-level facet may partition the problem space and may be a reasonable approximation to

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a candidate solution. A low-level facet may involve a more local inference that may explain one or two findings and would not advance the problemsolving process to the same extent. Diagnosis is the level of classification that encompasses and explains all levels beneath it. The model is hierarchical, with facets and diagnoses serving both to establish a context in which observations and findings are interpreted, and also to provide a basis for anticipating and searching for confirming or discriminating findings. This framework has been used to code the inferences generated from doctor-patient dialogue during the clinical interview (Kaufman & Patel, 1991). In our earlier research, we conceived of facets as prestored knowledge structures, derived from the experience of solving many cases, much in the same way diagnoses are stored in individual knowledge bases (Patel, Evans, & Kaufman, 1989). There are several problems with such an approach (Groen & Patel, 1988). The first problem is of a representational nature. Facets can subsume diagnoses, in cases where they are of a more general nature (e.g., cardiovascular disorders), and they can be subsumed by diagnoses, in situations where they are a subcomponent of a diagnosis (e.g., the facet of myxedema is subsumed by the diagnosis hypothyroidism associated with myxedema precoma). This precludes a strictly hierarchical ontological scheme. The second problem with regarding facets as prestored knowledge structures is that it is inconsistent with some perspectives on skilled memory. There is substantial evidence that experts develop skilled memory to store and retrieve information rapidly in their domains of expertise (Ericsson & Smith, 1991; Ericsson & Polson, 1988). Skilled memory theory proposes that at the time of encoding, experts acquire a set of retrieval cues that are associated in a constructive or functional manner with the information to be stored in memory (Ericsson, Krampe, & Tesch-Romer, 1993; Ericsson & Staszewski, 1989). In a problem-solving situation, an expert can use these retrieval structures to provide selective and rapid access to longterm memory. These retrieval structures are sensitive to domain-specific information and can be used in problem-solving situations to organize stimulus material (e.g., a set of clinical findings) and to compute the intermediate results necessary in finding a solution. The use of retrieval structures was proposed to account for the remarkable performance of chess masters to generate mentally a sequence of intermediate chess positions and mental calculators’ ability to store and compute intermediate results in multidigit multiplication (Ericsson & Staszewski, 1989). In our view, a facet can be construed as a retrieval structure that can be used to access rapidly schemata from long-term memory (LTM) and partition a medical problem into manageable units to facilitate the instantiation of a

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dignostic hypothesis. If a facet can be constructed dynamically in working memory, then subjects can compose an infinite number of combinatorial possibilities, unbounded by the limitations of prestored schemata. The next section summarizes research pertaining to diagnostic reasoning with particular emphasis on our own investigations. To maintain continuity, the methodological approach is discussed in some detail before raising specific conceptual issues.

111. Methodological Approach and Conceptual Issues

We began our studies of medical expertise with investigations of recall and comprehension of medical texts (Patel, Groen, & Frederiksen, 1986). This, in part, explains our commitment to methods of discourse analysis and semantic representation. We have since extended our research both methodologically and conceptually (Groen & Patel, 1988). From a methodological perspective, experimental paradigms and methods of analysis have been developed that are suitable for the study of expertise in medical reasoning and problem solving. On the conceptual front, we have investigated the reasoning processes characteristic of experts and novices. In this regard, we have devoted considerable effort toward investigating the directionality of reasoning and the time-course of hypothesis generation and evaluation. The theoretical perspective we have been developing has consistently attempted to reconcile theories of semantic representation (reflecting the fact that medicine is a semantically complex domain) with a problem-solving/reasoning perspective typical of most expertise research. This approach is also supported by the finding that much of expert reasoning involves the development of an initial problem representation rather than sequential problem-solving operations. In this regard, we have recently attempted to consolidate the construction-integration theory proposed by Kintsch (1988) with a schema-theoretic perspective. We elaborate in detail on this matter in a subsequent section. In studies of problem solving, the primary method of data acquisition is the “think-aloud protocol” (Ericsson & Simon, 1993). In these studies, subjects are instructed to think aloud as they perform a particular task. In a typical straightforward problem-solving task, in which subjects are asked to think aloud as they make a diagnosis, the protocols generated tend to produce unsatisfactory information regarding the nature of the knowledge being used. For example, in a routine case, experts tended to produce very sparse protocols which did not provide us with much of a basis for characterizing reasoning patterns. Kuipers and Kassirer (1984) suggest that expert knowledge is so compiled that it is difficult to articulate

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intermediate steps. Compiled knowledge refers to knowledge of causal expectations that people compile directly from experience and partly by chunking results from previous problem-solving endeavors (Chandrasakeran, 1991). This is manifested in very short chains of inference by experienced problem solvers. A widely adopted solution has been to use various kinds of probing tasks to elicit a more detailed reasoning process. A probe that we have found useful is a diagnostic explanation task, in which the subject is asked to “explain the underlying pathophysiology” of the patient’s condition (Patel & Groen, 1986). Physicians typically respond to this question by explaining the patient’s symptoms in terms of a diagnosis. The diagnostic explanation task has proven to be useful for the study of medical expertise in verbal tasks; however, it seems to be less of a predictor of expert performance in perceptual domains, such as radiology and dermatology. For example, in radiologic diagnosis, the subjects who provide the best explanations are not necessarily those who are most accurate in detecting abnormalities from visual cues in X rays (Lesgold, personal communication). An experimental procedure we have repeatedly used (Patel & Groen, 1986; Patel, Groen, & Arocha, 1990) involves asking subjects to read a written description of a clinical case for a specific period of time and to provide subsequently a free recall of the case. The subjects are then asked to explain the underlying pathophysiology of the case, without reference to either the text or the previous recall response. Lastly, the subjects are asked to provide a diagnosis. The diagnosis is requested after the diagnostic explanation to give the subject the opportunity to provide a diagnosis during the explanation task, such that the resulting protocol may reflect elements of the solution process. This is referred to as the immediate presentation paradigm. In a variation of this paradigm (Joseph & Patel, 19901, subjects are presented clinical information sequentially, one sentence at a time, and are asked to explain the incoming information and suggest a diagnosis. This kind of experimental approach is designated as sequential presentation. Diagnostic reasoning in real-life situations is an interactive task involving a dialogue between a physician and a patient in which the physician gathers the appropriate information.

A.

PROPOSITIONAL A N D SEMANTIC REPRESENTATIONS

The methods we have used include various techniques of semantic and conceptual analysis, which allow us to characterize the structures of subjects’ knowledge and the fine-grained differences in problem representations. These methods also provide relatively precise measures of coherence in explanations (Groen & Patel, 1988).

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We have made use of two kinds of representational formalisms: propositional representations and semantic networks. Intuitively, a proposition is an idea underlying the surface structure of a text. The notion’s usefulness arises from the fact that a given piece of discourse may have many related ideas embedded within it. A propositional representation provides a means of representing these ideas, and the relationships between them, in an explicit fashion. In addition, it provides a way of classifying and labeling these ideas. Systems of propositional analysis (Kintsch, 1974; Frederiksen, 1975) are essentially languages that provide a uniform notation and classification for propositional representations. In all these approaches, as in case grammars, a proposition is denoted as a relation or predicate (usually called the head element) over a set of arguments (frequently referred to as concepts). Sowa’s (1984) system provides another example of a language of this type. Although there are notational differences in the formalisms, the underlying assumption is that propositions correspond to the basic units of the representation of discourse and form manageable units of knowledge representations. The method for generating semantic network representations is described in detail in Groen and Patel (1988). The primary concern is with representing the structure of verbal or written data arising from explanation tasks in which subjects are presented with a situation involving a clinical case and are asked to explain aspects of the situation. The first stage of analysis involves generating a propositional representation of the response protocol. This is then transformed into a semantic network representation. The network consists of propositions that describe attribute information, which form the nodes of the network, and those that describe relational information, which form the links. We then distinguish between attributes that appear in the description of the clinical case and those that are spontaneously generated by subjects. The semantics of the links are derived from Frederiksen’s (1975) propositional grammar. The primary relations of interest in these networks are binary dependency relations, specifically, causal, conditional, and Boolean connectives-and, alternating or, and exclusive or relations. In addition, algebraic relations (e.g., greater than), identifying relations, and categorical relations (i.e., category membership, part-whole relations) can be expressed. We also distinguish between the source of a process and the result of a process. Uncertainty in relations can be represented by modal qualifiers (e.g., can) and truth values can be indicated when they deviate from the default truth value-positive. The causal and conditional relationships that form a major part of the semantic network arising from our data closely resemble the rules in an expert system such as NEOMYCIN (Clancey, 1988). This system consists of data-directed and

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hypothesis-directed rules, which can be viewed as equivalent to forward and backward reasoning. It can be shown that there is an isomorphism between this semantic network and a set of related production rules (Patel & Groen, 1986). To be more precise about our analysis, some notions from graph theory (Sowa, 1984) are used to divide the semantic network into components. These are described in considerable detail in Groen and Patel (1988). In summary, a graph is defined as a nonempty set of nodes and a set of arcs leading from node N to a node N ' . A graph is connected if there exists a path, directed or undirected, between any two nodes. In a directed path, every node has a source and a target connecting it to its immediate successor. If the graph is not connected, then it breaks down into disjoint components. A semantic network is a directed graph formed by nodes and labeled connecting paths. Nodes may represent either clinical findings or hypotheses, whereas the paths represent directed connections between nodes. These networks provide a relatively precise means for characterizing the directionality of reasoning. Forward reasoning corresponds to an oriented path from data to a hypothesis. Thus, forward-directed rules are identified whenever a physician attempts to generate a hypothesis from the findings in a case. Backward-directed rules correspond to an oriented path from a hypothesis to data. Pure forward reasoning refers to a network where all paths are oriented from data to hypothesis. Pure backward reasoning refers to a network where all paths are oriented from hypothesis to data. Our use of the term coherence is also defined in terms of the connectivity of the graph corresponding to graph theory. Coherence can be estimated from the number of disjoint or disconnected components in a semantic network representation. In our analysis, we make use of two notions of coherence. Global coherence refers to the internal consistency of the overall explanation. An explanation exhibiting maximum global coherence would include connections between all subcomponents of the problem without any apparent contradictions. Local coherence refers to the consistency in a component that explains parts of the clinical problem. An explanation that exhibits local coherence without global coherence would include isolated aspects of the problem that are well understood, but not interrelated. It is important to note that coherency does not necessarily imply accuracy. The use of these definitions is illustrated in Fig. 1. This is the network representation of the diagnostic explanation by a psychiatrist of a case in cardiology described by Patel, Groen, and Arocha (1990). The case is not within the subject's domain of specialization, and the diagnosis of a shock slate is inaccurate. Because of this, the representation is lacking in coher-

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ence and contains one possible inconsistency, that is, a patient cannot have both high and low blood pressure at the same time. Furthermore, the underlying mechanism that explains the signs and symptoms in this patient is attributed to toxicity of drugs that are injected by the patient as a reaction to stress. This is an inaccurate description of the patient problem. However, this makes it useful for illustrative purposes because it contains a mixture of forward and backward reasoning. The diagram consists of nodes linked by arrows. The arrows have labels indicating the relationship between nodes. The two most important are C A U , which means that the source node causes the target (e.g., upsurge in blood pressure causes @ame-shaped hemorrhage), and COND:, which means that the source node is an indicator of the target (e.g., tachycardia indicates shock state). The arrows labeled CAU: represent causal relations, whereas those labeled COND: represent conditional relations. A difference between the two is in the strength of implication: COND: expresses a directional conditionality, P1 P2, that implies if proposition PI is true then P2 is true. CAU: P1 3 P2, is of a stronger relation indicating that one variable, P2 is a function of another, P1. Although we can define these terms formally, it is important to note that subjects’ attributions of causality or conditionality may deviate from appropriate usage. We code protocols on the basis of subjects’ attributions even if there are

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apparent inconsistencies. The nodes containing facts from the problem text are enclosed in boxes. The diagram also contains three “AND” nodes (conjunction), indicated by forks in the arrows. One of these is a target (tachycardia,fall in blood pressure, and elevated temperature indicate shock state). The remaining two nodes are sources (injection of drug causes tachycardia, fall in blood pressure and elevated temperrtture). If we compare the text and the semantic network, it is apparent that the network does not necessarily reflect the temporal sequence of concepts referred to in the text. For example, the same concept can be referred to repeatedly, but it is represented as inferences on a single node. This is evident in the example. This graph contains several different lines of reasoning, which can be rendered precise by introducing a few more concepts from graph theory. We define a “cut-point” of a graph to be a node that, when removed together with all arrows leading to or from it, causes a graph to separate into disjoint components that are themselves graphs. Conversely, if two graphs have a common node, then their join is the new graph formed by joining them at that node. This suggests that an algorithm for finding the components can be defined in terms of generating, at each cut-point, two graphs whose join is the original graph (intuitively, we obtain two graphs by removing the cut-point and then reattaching it into each graph). What results from applying this algorithm is a hierarchy of components, some of which are uninteresting and some of which actually distort the logic of the process we are attempting to represent. Because of this, we prohibit the application of the algorithm in the following two cases: (a) ANDnodes, and (b) graphs that consist of a single path without any branches. Carrying out this procedure results in the minimal components shown in Fig. 2. Although other components exist, they are alljoins of these minimal components. The directionality of these components is mixed. The component where the inference is made in the direction of data to hypothesis is coded as forward-driven inference. For example, tachycardia, fall in blood pressure, and elevuted temperature indicate the diagnosis of shock state. Conversely, a component where the inference is made in the direction from hypotheses to data is coded as backward-directed inference. An example here is injection of drug causesflame-shaped hemorrhage. We can also obtain quantitative measures of directionality of reasoning by coding inference as forward or backward. In a given protocol, we can determine the percentage of forward and backward reasoning inferences. To have a reference point that indicates a certain standard of performance, we develop a reference model for each case that is presented to the subjects. The reference model reflects an idealized knowledge repre-

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Fig. 2. Components of semantic representation given in Fig. I . COND: conditional I diagnostic relation; CAU: causal relation; RSLT: resultive relation; 0 text cues; i hypothesis; arrows, directionality.

sentation that is constructed with the assistance of domain experts and pertinent medical texts (Patel & Groen, 1986;Joseph & Patel, 1990; Patel, Evans, & Kaufrnan, 1989). It serves as a benchmark or gold standard for certain types of analyses we perform on subjects’ response protocols. The construction of a reference model is similar in nature to a knowledge engineering task used in the development of expert systems. The goal is not to develop necessarily a faithful cognitive representation of an expert, rather, it is to develop something like an ideal model of the problem. It is an iterative process that begins with asking an expert not participating in our studies to explain the findings in the case. We can then consult other sources to verify and add to this information, at which point we can then go back to the expert and probe for specific details. B.

NOVICE-EXPERTCONTINUUM

As noted earlier, expertise reflects a continuum from genuine beginner to the highly trained specialist. The time period from entering medical school to becoming a board certified specialist is, on average, 10 years. This provides a basis for differentiating between subjects at various levels in the acquisition of expertise. Expert physicians have extensive knowledge of medicine (acquired through medical school and residency training), but

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only a relatively narrow area of specialization. It is therefore possible to distinguish between specific (e.g., cardiology) and generic (e.g., general medicine) expertise. An individual may possess both, or only generic expertise. Early medical training, through medical school and internship, involves the acquisition of generic expertise. At the point physicians enter a residency training program they can choose to specialize, in which case they would acquire specific expertise as well as continue to develop generic expertise. The development of both kinds of expertise overlap considerably. However, a medical resident would continue to acquire generic expertise through rotations in areas outside his or her area of specialization. To clarify some terminology, we provide the following definitions:

Nouice: An individual who has only everyday knowledge of a domain or one who has the prerequisite knowledge assumed by the domain. Intermediate: An individual who is above the beginner level but below the subexpert level. Subexpert: An individual with generic knowledge but inadequate specialized knowledge of the domain. Experf: An individual with a specialized knowledge of the domain.

A few things should be noted about these definitions. First, it is assumed that the domain is sufficiently well defined that it is possible to distinguish between generic and specific knowledge. Second, the notion of intermediate is not well defined at all. We will clarify this notion later. Every expert (e.g., endocrinologist) is a subexpert outside of his domain of specialization (e.g., in cardiology).

IV. Expert Diagnostic Reasoning A.

DIRECTIONALITY OF REASONING

The innovative research of Elstein, Shulman, and Sprafka (1978) represented the first efforts to use information processing theories to study medical cognition. This work was enormously influential in that Elstein et al. were the first researchers to use experimental methods and theories of cognitive psychology to study clinical reasoning. This work, which commenced in the late 1960s, was strongly influenced by the early work of Newell and Simon (1972) on simple knowledge-lean tasks such as the Tower of Hanoi and Cryptarithmetic. The principal goal of the studies by Elstein and colleagues was to characterize the general aspects of medical problem solving in terms of the cognitive processes used to acquire and manipulate patient data. The emphasis was on process parameters, such

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as the number of hypotheses generated, rather than on content or domain knowledge. On the basis of this research, Elstein et al. (1978) suggested that clinical reasoning is characterized by a hypothetico-deductive process. The model identifies four stages in the diagnostic process: cue acquisition, hypothesis generation, cue interpretation, and hypothesis evaluation. This process begins with attention to initial cues which lead to the rapid generation of a few select hypotheses, on average five in a single workup. In the process of cue interpretation, each subsequent cue is designated as positive, negative, or noncontributory with respect to the hypotheses under consideration. The findings did not discriminate between physicians judged by their peers to be superior physicians and other physicians (Elstein et al., 1978). In a related study, using a very similar approach, Barrows, Feightner, Neufeld, and Norman (1978) found no differences between students and clinicians in their use of diagnostic strategies, except for the quality of the hypotheses considered and the accuracy of diagnosis. The characterization of hypothetico-deductive reasoning, essentially a form of backward reasoning, as an expert strategy seemed anomalous in view of the fact that it was widely regarded as a weak and inefficient method of problem solving (Groen & Patel, 1985), more characteristic of novices in domains such as physics (Simon & Simon, 1978).As a cognitive model, it necessitates the use of subgoals to test and evaluate each hypotheses. This makes unreasonable demands on working memory. Subsequent findings in the domain of medical cognition suggested that experts have an elaborate, highly structured knowledge base capable of supporting highly efficient reasoning (Feltovich et al., 1984; Bordage & Zacks, 1984). In the domain of radiology, Lesgold and colleagues (1988) found that expert radiologists have superior perceptual pattern recognition capabilities as compared to novices. This allows the expert to rapidly detect gross anatomical abnormalities. Gale and Marsden (1983)provided an additional insight when they suggested that Elstein et al. (1978) had erred when they collapsed all hypotheses into the category of diagnostic hypothesis. They suggested that if it is recognized that most hypotheses are prediagnostic interpretations (e.g., potential cardiac problem) rather than actual diagnostic hypotheses, then the diagnostic reasoning process would appear to involve a more incremental partitioning of the problem space than that suggested by the hypothetico-deductive model. In fact, this issue is still a point of controversy. Patel and Groen (1993) demonstrated that if you do not discriminate between diagnostic and subdiagnostic hypotheses, then you are likely to interpret the same data in a very different manner and reach fundamentally different conclusions concerning the diagnostic

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reasoning process (cf. Elstein et al., 1993). This distinction is crucial in detecting differences in reasoning strategies between subjects of different levels of expertise. Pate1 and Groen (1986) were motivated by the seemingly anomalous description of the diagnostic reasoning process to characterize precisely the directionality of reasoning used by expert physicians in a diagnostic explanation task. The results indicated that expert cardiologists’ explanations could be accounted for by a forward-reasoning strategy that involved moving from propositions in the stimulus text, to conditions that suggested components of the diagnosis, to an accurate diagnostic solution. This is in contrast to cardiologists who misdiagnosed the case, who used a backward reasoning strategy that involved generating and testing hypotheses and the invocation of causal explanations. As discussed previously, forward reasoning is usually contrasted with backward reasoning, where the problem solver works from a hypothesis regarding the unknown back to the given information. It might be noted that the distinction is frequently made, perhaps more generally, in terms of goal-based (backward) versus knowledge-based (forward) heuristic search (e.g., Hunt, 1989). We phrase the distinction in terms of data and hypotheses because it relates more clearly to the types of empirical paradigms in which we are most interested. The distinction between forward and backward reasoning is closely related to another distinction made in problem-solving research between sfrong methods, which are highly constrained by the problem-solving environment, and weak methods, which are only minimally constrained. In fact, the two distinctions are logically independent (Hunt, 1989). Forward reasoning is highly error prone in the absence of adequate domain knowledge because there are no built-in checks on the legitimacy of the inferences that subjects make. Pure forward reasoning is only successful in constrained situations, where knowledge of a problem can result in a chain of inferences from the initial problem statement to the problem solution. In contrast, backward reasoning is slower and may make heavy demands on working memory, because one has to keep track of such things as goals and hypotheses; it is therefore most likely to be used when domain knowledge is inadequate. Backward reasoning is usually a symptom of a weak method. It is important to note that the term weak is being used in the technical sense of “weak constraints” as opposed to “strong constraints.” This does not imply that it is a weak way of solving a problem. In fact, a weak method is preferable when relevant prior knowledge is lacking, as is likely to be the case with anyone but an expert. We view our research program as a theory-building enterprise. Experimental research in psychology is somewhat skewed toward the use of

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quantitative methods for confirming or disconfirming hypotheses that are assumed to be well formulated, rather than toward providing a foundation for theory development which can then provide a basis for hypothesis testing (diSessa, 1991). The work presented in this chapter focuses on indepth qualitative analysis of individual protocols, as well as on quantitative analysis of group differences. The goal of this work is to establish and explain empirical phenomena pertaining to reasoning, the structure of knowledge, and the nature of expertise, rather than to develop broad categorical generalizations. B. EXPERTREASONING A N D DOMAIN SPECIFICITY This section focuses on a series of investigations concerning the effects of problem difficulty and relevance of domain knowledge on expert reasoning, and examines how these effects differ as a function of immediate versus sequential problem presentation. In the last few years, the notion has become increasingly accepted in the study of expert-novice differences that expert reasoning is schema driven (Koedinger & Anderson, 1990; Hunt, 1989). A schema is assumed to be a structure that represents generic knowledge and is instantiated with specific new information in a given situation (Brewer, 1987). Schemata, which are built up as a function of experience within a domain of expertise, guide a subject to key elements in a problem and serve to filter out irrelevant information. In a complex medical problem, there are an inordinate amount of potentially significant findings. An experienced physican can rapidly access appropriate schemata and delineate a problem into something manageable. The notion of schema is often used in cognitive psychology to explain phenomena of pattern recognition o r something that is triggered by stimulus conditions. However, medical problem solving, except in the most routine sort of problems, necessitates more than a pattern recognition capability. We have to allow for composability of schemata in real time, not just in terms of fixed structure in which slots are filled. Schemata can be viewed as a set of pointers from a problem encoding, residing in short-term or working memory, to relevant knowledge in long-term memory. Schemata also filter out the volume of irrelevant information present in the space of a typical medical problem. In the context of a semantic network representation, a schema can be described in terms of a cluster of connected propositions (nodes and labeled links), for example, diagnostic hypotheses, and the clinical findings that are explained by the hypothesis (van Dijk & Kintsch, 1983). As discussed earlier, diagnostic schemata are accessed from LTM, and facets or subdiagnostic hypotheses can be constructed or composed dynamically. It has been suggested that the rapid evocation of appropriate schemata is a hallmark

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of expertise (Chi et al., 1981). Schemata serve a dual purpose of organizing stimulus material through hypothesis generation and guiding the search for further information. In our research, we assume that the generation of a hypotheses is indicative of the access or construction of a schema or multiple schemata. Studies of expertise in medicine and other domains have indicated that the early generation of diagnostic hypotheses is an important indicator of the existence of schemata. The rapidity with which the initial hypotheses are generated constitutes a striking feature of the behavior of experts. With minimal information, the expert unhesitatingly selects a single working hypothesis. Studies have shown that the earlier a good hypothesis set is created, the more predictive it is of the quality of the solution (Elstein, et al., 1978). In the case of routine problems, this is accompanied by forward reasoning in which the direction of inferencing proceeds from data to hypotheses (Patel & Groen, 1986). This finding is consistent with expert schema-driven reasoning and results in a number of important conclusions that appear to apply across domains. The most important, for the purposes of this chapter, is that routine problems are solved by experts via a data-driven process of forward reasoning, which directly infers solutions from the facts in the problem without backtracking or generating subgoals. This raises the question of what variables might affect schema formation. Certain methods of problem presentation are more likely to elicit schemata than others. If the entire context of the problem is presented simultaneously to the subject as continuous text, then the text is more likely to generate a schema. This form of text presentation will be termed immediate paradigm. On the other hand, if the information is split into segments and sequentially presented, such schemata cannot be initially accessed. Instead, the subject must generate a schema on the basis of partial information and this schema must be modified or abandoned as the information becomes more complete. This form of text presentation will be called sequential paradigm. An additional impediment to schema formation results from short-term memory limitations. Using standard assumptions in this regard, it can be assumed that propositions will only remain in short-term memory (STM) for a limited amount of time. If they vanish from STM, then the pointers can be expected to vanish with them, unless some indexing process exists that allows a new proposition to be substituted for the old one. We know from other domains that experts are highly skilled at developing temporary memory structures for maintaining intermediate results, such as unaccounted for, but significant findings (Ericsson & Staszewski, 1989).This memory encoding and retrieval ability is critical for maintaining and evaluating hypotheses in working memory, especially in the sequential paradigm.

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1 . Hypothesis Formution and Tusk DifJiculty

Two factors are associated with task difficulty. The first is the relevance of the expert’s domain knowledge. If this domain knowledge is lacking, then schemata might point to irrelevant knowledge or might be fragmented. However, a subexpert can draw on a vast medical knowledge base, which can partially compensate for the absence of relevant domain knowledge. Thus, it is useful to distinguish between generic and specific expertise. Specific expertise refers to the possession of knowledge specific to a domain. Thus, board-certified cardiologists have specific expertise about cardiology; they do not necessarily have specific expertise about endocrinology. The reverse, obviously, holds true for endocrinologists. However, both cardiologists and endocrinologists know a considerable amount about clinical medicine by virtue of the fact that they are physicians trained in general medicine. This latter is what we mean by generic expertise. Empirical support for the notion that it is an important factor comes from results from our laboratory (Patel & Groen, 1991a) that indicates a ceiling effect on recall of clinical cases. Both subexperts and experts can recall all or most relevant case material. Analogous to the results in other domains, such as chess (Charness, 1991), experts recall clinical cases far better than novices (Patel, Groen, & Frederiksen, 1986). However, experts (regardless of domain) tend to recall all information relevant to the diagnosis of a clinical case. Moreover, this enhanced recall is unrelated to diagnostic accuracy. Further indications of generic expertise may come from a comparison with intermediates (i.e., subjects who are not novices, but who have not reached the expert level). Intermediates perform considerable irrelevant search of hypotheses, order irrelevant tests, and generally show an inability to cope economically with new information (Patel, Groen, & Patel, in press). Although experts outside their domain may be viewed as intermediates, they do not perform the same as intermediates. Experts do not engage in such extraneous search, even when performing outside their domain. These results point to the need to differentiate between generic and specific experts, rather than consider the former as a type of intermediate. The second factor is the intrinsic difficulty of the problem regardless of domain knowledge. As already indicated, it is well known that experts solve routine problems by forward reasoning (e.g., Larkin et al., 1980). In a study designed to extend the findings of Patel and Groen (1986), Patel, Groen, and Arocha (1990) examined the extent to which the pattern of forward reasoning would break down when subjects were confronted with more difficult cases. They varied task difficulty by using two texts describing the history, physical examinations, and laboratory tests of two

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patients. The first text described a case of a 63-year-old woman who suffered from an endocrine disorder called Hashimoto’s thyroiditis precipitated to myxedema precoma. The second clinical text describes a case of a 62-year-old man, who suffered from a cardiac disorder. This case is more difficult than the endocrinology case because there is considerable overlap of schematic knowledge, in the sense that the data are suggestive of multiple competing diagnostic possibilities. We will describe this case in more detail later. A glossary of medical terms used in this chapter is given in the Appendix (Section VII). According to the expert reference model of the Hashimoto’s thyroiditis case, the diagnosis can be decomposed into three diagnostic components. The most general and prototypical component is hypothyroidism. This is indicated by the textual cues suggestingfluid accumulation and decreased thyroid function. The second component, myxedema, indicates that the patient is in an advanced state of hypothyroidism. The clinical cues that constitute the third component suggest a very specific origin for the disease process: the autoimmune process known as Hashimoto’s thyroiditis. Cues from each of the three components need to be recognized to accurately diagnose the problem. It was found that the existence of loose ends in the endocrine problem tended to disrupt the pattern of forward reasoning, even when the diagnosis was correct. Loose ends are anomalous cues that are not directly related to the main diagnosis. Figure 3 gives the semantic network representation of the protocol of an endocrinology practitioner explaining the endocrinology problem. The information used from the given clinical text is shown in solid blocks and the diagnostic components that are generated or inferred are given in dashed blocks. The other information are either inferences generated from the given information, which are intermediary constructs in generating diagnostic components, or inferences generated to explain a given clinical information. The input nodes are those that correspond to propositions in the text. The relations that connect the propositions are linking propositions (e.g., CAU:, a causal relation, or COND:. a conditional relation). In much of our analysis, CAU: refers to some statement regarding the functioning of the underlying pathophysiology. It is convenient to consider these rules as production (if-then) rules. The causal rules can be reversed into conditional, but the conditional rules leading to the actual diagnosis cannot be reversed in this fashion. For example, in Fig. 3, the causal rule hypoventilation, leads to respirutoiy failure implies the conditional rule, if respiratory failure then hypoventilation. On the other hand, the purely conditional rule ifvitiligo then autoimmune thyroidism cannot be reversed because autoimmune thyroidism does not cause vitiligo.

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L Progressive Disease

Of Thyroid Function

tm

Iodine Administration I

, _ _ _ _----. -

Galactorrhea Further Blockage

__-----------: Autoimmune : Thyroiditiswith : MyxedemaComa :

tMlh

1

Long Standing Nature of Disease I

u

Prolactin

Inappropriate Diuretic Hormone

HYPOmtabolic State

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Fig. 3. Semantic network representation of the protocol of an endocrinology practitioner explaining the endocrinology problem adapted from Patel, Groen, & Arocha ( 1990. p. 4). 0 text cues; i 3 diagnostic component.

The explanation provided by the endocrinologist in Fig. 3 presents very little text information and it is constructed to justify a diagnosis. There is one large component with forward reasoning and two small components with backward reasoning to explain the textual cues. The large component consists of three subcomponents: Subcomponent 1

patient's current leads to, COND: condition

autoimmune thyroiditis

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Given the information vitiligo, thyromegaly (enlargement of thyroid), and progressive decrease in thyroid function indicates a diagnosis of autoimmune thyroiditis. This is a forward-directed rule from the given information to a diagnostic inference. Subcomponent 2 autoimmune thyroiditis and iodine

w

i

further blockage of thyroxine release

Here, the consequence of the rule in the subcomponent 1, autoimmune thyroiditis together with a new given information, administration of iodine mixture, indicates further blockage of thyroxine release. This is also a forward-directed rule, where the given information is used to generate an inference. Subcomponent 3 blockage of thyroxine release

+

galactorrhea jC -

myxedema

Once again a new given information galactorrhea is used together with the consequence of the rule in subcomponent 2 to generate a diagnostic inference of myxedema. In the node-link structure, the input nodes are those that correspond to propositions in the text. When one of these nodes is “fired,” the node to which it is linked together registers this fact. However, because there is an AND-node, it does not fire until all its other antecedents fire. In terms of the graph theoretic interpretation, this results in a coherent semantic network leading to the correct diagnosis. The two additional components of Fig. 3 are generated in the direction of the causal rule, where the given information respiratory failure and hyponatremia are explained using information that is inferred, such as hypometabolic state and inappropriate diuretic hormones, respectively. This is termed backward reasoning where the direction of inferences is toward the explanation of given information. In the explanation generated by the endocrinologist, the direction of reasoning is completely forward except at the very end of the protocol, where a problem related to a slowed metabolism (hypometabolic stare) is explained in terms of a possible outcome (respiratory failures), and the existence of a low serum level of sodium ions (hyponatremia) is explained

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in terms of impaired water excretion. There is one large component with forward reasoning leading to a diagnosis and two small components with backward reasoning to explain the anomalous textual cues. The amount of information unaccounted for (anomalous data) increased as experts moved out of their domains. A cardiologist practitioner’s explanation of the same endocrine problem led to a greater amount of loose ends, although the main diagnostic component was still generated by forward reasoning, as shown in Fig. 4. A greater use of textual information is seen in this explanation in contrast to the explanation generated by the expert given in Fig. 3. The relational network in Fig. 4 can be decomposed into a number of components, the first of which is generated by the use of forward-directed reasoning: Component 1

patient’s current condition

COND.

j-

long standing primary hypothyroidism

A list of given clinical signs of symptoms regarding the patient leads the physician to generate a diagnostic inference of long standing primary hypothyroidism. All the other four components are generated by backward directional reasoning, where the given information such as edema, pleural effusion, skin pallor, EKG changes, low body temperature, and so on, are explained with the use of inferred information. Thus the direction of reasoning is from inference to given information. These four components constitute loose ends because they are not connected to the main diagnostic component. It should be noted that although the diagnosis provided is accurate, it is only partial. The tasks used in the preceding studies were explanation-based in the sense that the subjects read the complete text (i.e., immediate presentation) before explaining the problem. Joseph and Patel (1990) studied the performance of experts and subexperts on the endocrine case using sequential rather than immediate presentation. Their results showed no significant differences between the groups in terms of selection of relevant and critical cues from the case. The experts generated accurate diagnostic hypotheses early in the problem encounter and spent the rest of the time evaluating (confirming and refining) the diagnosis by explaining the patient cues. Although the subexperts also generated the accurate diagnosis, they did so later in the case presentation. These subjects had difficulty in evaluating the hypotheses against the given information, which resulted

21 I

Diagnostic Reasoning and Medical Expertise

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Fig. 4. Semantic network representation of the protocol of a cardiology practitioner explaining the endocrinology problem. 0 text cues; i l 3 diagnostic component.

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in the inability to discriminate between and eliminate alternative diagnoses. This is consistent with the finding that if an expert includes the correct diagnostic hypothesis in the initial hypothesis set, the subsequent evaluation of data is almost always directed at the confirmation of the hypothesis, rather than the generation of any alternative possibilities (Patel, Evans, & Kaufman, 1989).This is equivalent to generating an accurate diagnosis using pure forward reasoning when all the information is given at one time. However, if the diagnosis is not accurate, then at the evaluation phase of the problem solving, alternative diagnoses may be generated or some additional patient cues will have to be explained. This results in backward reasoning. The most surprising aspect of this result was that sequential presentation did not have a disruptive effect. In fact, the diagnostic accuracy of subexperts was higher in the sequential than in the immediate presentation. The main difference was that experts generated relevant hypotheses sooner than subexperts. However, this may have been due to the relatively simple nature of the endocrine problem. This leads us to consider the effects of sequential presentation of the considerably more difficult cardiology problem. In a recent study presented at the 1992 Meeting of the Psychonomic Society, Patel, Groen, and Arocha tested eight senior physicians (four cardiologists and four endocrinologists) in the sequential presentation task. Given that the task was the diagnosis of a patient with a cardiac disorder, the expert group consisted of four cardiologists and the subexpert group consisted of four endocrinologists. The physicians were all board-certified, practicing physicians with 5 to 10 years of experience in their respective fields. The stimulus text described a cardiology problem, based on a real patient and modified by a cardiologist for the purposes of this study. This problem is identical to the one used in Patel, Groen and Arocha (1990). The clinical information in the case was arranged in the typical order of the patient’s medical history, findings from physical examination, and Xray and laboratory test results. The problem describes the case of a 62-year-old man who was diagnosed as having pericardial effusion with pretamponade. This is a condition in which there is a compression of the heart produced by the accumulation of fluid in the pericardial sac (i.e., a fibrous sac that surrounds the heart) to the extent that the normal expansion of the heart is prevented. The fluid may result from the rupture of a blood vessel of the heart. To diagnose the case, the physician must decide whether the problem is caused by a failure of the left-side or the right-side of the heart, and then identify the presence of pericardial effusion (i.e., accumulation of fluid within the pericardial sac) and cardiac tamponade (i.e., compression of the heart by

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the accumulation of fluid). Determining the actual causal process of specific heart failure (i.e., right or left) is a difficult task because many of the clinical features are common to different diagnostic possibilities. There are, however, afew cues that either serve to rule out alternative diagnoses or are not related to the main diagnosis. Based on the reference model, five major (sub)diagnostic components or facets were identified in the case that would lead to an accurate diagnosis. These include, right heart failure, left heart failure, cardiac tamponade, hepatic congestion (accumulation of blood in the liver vessels due to lack of circulation), and pericardial effusion. This information was used to evaluate the subjects’ interpretations of the problem. The basic procedure was similar to that used by Joseph and Pate1 (1990). Each subject was tested individually in his or her office at the hospital. Before the presentation of the case, subjects were given a short practice session to familiarize themselves with the experimental apparatus and procedure. The stimulus material was presented to subjects on a microcomputer one segment at a time. Subjects controlled the rate of presentation of each segment by pressing the mouse button, and then each segment was replaced by the next on the display. However, the sequence of information was fixed and not under the subjects’ control. It was not possible to access information presented in prior segments at any time during the presentation of the case. Subjects were instructed to verbalize their thoughts about the role and importance of the information in each segment in reaching the correct diagnosis. After presentation of the entire case, subjects were asked to provide a summary of the case and then to offer their final diagnoses. The protocols were analyzed in terms of (a) diagnostic accuracy, (b) the number and type of hypotheses generated, and (c) the representation of the problem over the time-course, using a combination of protocol and discourse analyses techniques. Protocol analysis allows one to study the problem-solving moves in relation to transitions in knowledge states. Discourse analysis allows one to study detailed semantic descriptions that capture complex relations in the protocols and allows characterization of the use of specific knowledge, the nature of inferences, and the overall pattern of reasoning. It also provides u s with a basis for measuring coherence in subjects’ explanations. Diagnostic accuracy was assessed in terms of specific diagnostic components that were either included or not included in the verbalization process. In addition, a diagnosis was characterized as accurate when it was completely correct (i.e., all of its components were included), partially accurate (when some components of the diagnosis were included), and inaccurate (when no diagnostic components were included).

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2 . Diagnostic Accuracy All the expert subjects generated completely accurate diagnoses, whereas all the subexperts generated partially accurate diagnoses. One subject generated an inaccurate diagnosis. Table I gives the list of major diagnostic components generated by the experts and subexperts. All experts generated each of the five major diagnostic components. The subexperts generated all but one major component, that of cardiac tamponade. Subject 6 was the exception, in that he did not generate the components of left and right heart failure. This is a serious enough error to make the diagnosis inaccurate. 3 . The Time-Course of Hypothesis Generation

Analysis of the time-course production of diagnostic hypotheses focused on differences between expert and subexperts in (a) the relationship between the number of hypotheses generated in the course of presentation of the case description, and (b) the time and the order of production of accurate diagnostic components. The pattern of the cumulative mean sum of new hypotheses produced with each new segment of information for experts and subexperts is presented in Fig. 5 . Each point on the figure represents the cumulative total number of new hypotheses generated as each segment is presented. The slope of the lines represents the pattern of hypothesis generation: the larger the slope, the greater the number of new hypotheses being generated; zero

TABLE I

DIAGNOSTIC COMPONENTS GENERATED BY EXPERTS (CARDIOLOGISTS) A N D SUBEXPERTS (ENDOCRINOLOGISTS), SOLVING T H E CARDIOLOGY PROBLEM Diagnostic components generated by subjects List of major diagnostic components from reference model A Heart failure

B C D E F

Right heart failure Left heart failure Cardiac-tarnponade Hepatic constriction Pericardial effusion

SI

Experts S3 S5

x x x x

x x x x

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x

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x X 0 X X

Subexperts S4 S6

S8

x x x

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Diagnostic Reasoning and Medical Expertise

30

20

1

t

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P EL

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0 2 4 6 8 101214161820222426283032343638404244464850

Sentence Number

Fig. 5 . Hypothesis generation over the tirne-course of information presented by expert and subexpert subjects in the cardiology case. Letters A-F correspond to six diagnostic subcornponents as listed in Table 1.

slope indicates that no new hypotheses were generated. There is a clear difference in the patterns of hypothesis generation for the two groups. Before the presentation of segment 8, the two groups of subjects had generated approximately the same number of hypotheses. After segment 8, the expert subjects used more of the incoming information to confirm the diagnostic components that had already been produced rather than generating new diagnostic hypotheses. Subexperts, in contrast, continued to generate alternative components, as reflected in the differences between the two slopes of experts and subexperts from segments 10 to 48. The subexperts produced the diagnostic components later than did the expert subjects. The two major diagnostic components, heart failure, and, specifically, right heart failure, were generated by both groups of subjects by the presentation of information in segment 8. However, all of the experts had identified the third component, left heart failure, also by segment 8. Specific individual differences in the generation of hypotheses were found in expert subjects with respect to the diagnostic component of tamponade. Two subjects generated this component before the component of pericardial effusion. N o subexperts generated the tamponade compo-

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nent. They generated a number of intermediate diagnostic components that linked one component to another by an extended chain of inferences. As a mode of comparison, Fig. 6 gives the pattern of the cumulative mean sum of new hypotheses produced with each new segment of the information for experts (endocrinologist) and subexperts (cardiologist) using the less complex endocrinology problem presented in the study by Joseph and Patel (I990),described earlier. Both the experts and subexperts produced accurate diagnostic components. However, the subexperts produced them later than did the expert subjects. A comparison of the change of slopes of the cumulative lines of new hypotheses as a function of segment number reveals that hypotheses increase at the same rate up to the point where five hypotheses have been produced. After this point, experts produced very few new hypotheses, whereas for the subexperts, the rate of hypothesis production continued to rise. The experts most often used new findings from the physical examination and laboratory test results to confirm the diagnoses generated earlier and to determine secondary problems. The subexperts, on the other hand, focused on associating the findings from physical examination and the laboratory test results with new diagnostic possibilities. Thus, the pattern of results from the use of a less complex problem are similar to the findings in the context of more complex problems. The difference is in the total number of new hypotheses generated, where subjects using the complex

1 2 3 4 5 6 7 8 9101112131415161718192021222~~~~~

Sentence number Fig. 6. Hypothesis generation over time-course of information presented by expert and subexpert subjects in an endocrinology case (easier case). Adapted from Joseph & Patel ( 1990, p. 37). A-C: production of diagnostic subcomponents hyopthyroidism, myxedema, and Hashimoto’s thyroiditis.

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(x

text generate more hypotheses Expert = 14, subjects using the less complex problem @Expert

x Subexpert = 21) than = 6 , x Subexpert = 9).

4. Patterns of Reasoning Table I1 gives the directionality of reasoning in generating and evaluating the hypotheses by experts and subexperts. The results show that all subjects used more forward-directed reasoning than backward-directed reasoning. Experts used more backward reasoning than subexperts. The reason for this was that experts evaluated their hypotheses more often than subexperts. At a certain point it was evident that experts had already concluded the correct diagnoses and the additional information presented to them after this point was superfluous. They also used more confirmation strategies, the majority of which were forward directed. Subexperts generated a greater number of intermediate components that linked one hypothesis to another. These links were uncoded because they are neither forward- nor backward-directed links. 5 . Detailed Analvsis of Protocols

Figures 7-9 present schematic representations of the subjects’ explanations of specific segments of the cardiology case. We concentrate on the TABLE I1 DIRECTIONALITY OF REASONING BY EXPERTSA N D SUBEXPERTS I N SOLVING A COMPLEX PROBLEM SEQUENTIALLY Directionality of reasoning ~~

Subiects

Forward

Backward

Confirmation”

Subexperts (endocrinologists) I6

s4 s2b s7

39 20 24

Sl I h Experts (cardiologists) s9 SI S6

7 47 IS 17

s10 ~~~

19 7 9 8

~

‘’ Confirmation includes both forward and backward confirmations as well as rule-out strategy. Unencoded hyotheses ( 6 in S2 and 5 in S11) were identified.

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protocols of four subjects, two from the expert and two from the subexpert subjects, which present the details of the changing representations of the problem and their relationship to the pattern of reasoning. Both subjects, Expert 9 (E9) and Subexpert 4 (SE4), begin in the same way by generating the low-level facet hypotheses of left sided heart failure in segment 3. Previously, we distinguished between low-level facets, which result in local inferences, and high-level facet hypotheses, which partition the problem space. Soon after the introduction of the first low-level facet hypothesis, the expert introduces three high-level facet hypotheses: heart disease, liver disease, and kidney disease. These facets help partition the problem space into three potential causes. Figure 7 presents the hypotheses generated by the expert and the subexpert at segments 7,8, and 18. In segment 7, the patient presents the cue of increased appetite and loss of weight. Whereas the expert generates three high-level facets which partition the problem space into three distinguishable classes of problems that can account for the cues (kidney, heart, or liver problems), the subexpert interprets the cue in terms of a generalized swelling of the body. The potential diagnoses that may account for anasarca, however, are not specified. This inference does not tie together any of the other findings and is therefore not used to delineate further the space of possibilities. This pattern is repeated in subsequent segments, with the expert maintaining high-level facet hypotheses and the subexpert generating new lowlevel facet hypotheses. Furthermore, the expert begins evaluating these hypotheses immediately after their introduction. For instance, in segment 8, the expert generates two hypotheses, swelling of the bowel and liver disease. The subject accomplishes this in a backward-directed manner by generating a low-level facet (swelling of the bowel) which is caused by the high-level facet (liver disease). In this way, the expert confirms the involvement of the liver in the patient problem. The subexpert, in contrast, generates two new low-level facet hypotheses, accounting for the segment information in a local manner (biventricular heart failure and loss of protein), as is evident in segment 8. It is important to note that the hypotheses generated by the subexpert are meaningful hypotheses in the context of the presented data (i.e., one cannot discount them as irrelevant or incorrect), but they are not conducive to a partitioning of the problem space. Once the expert seemed to have confirmed the involvement of the liver in the process, the problem was to figure out whether this involvement is primary (i.e., the cause of the problem is hepatic) rather than secondary to a cardiac problem, and to differentiate between the three high-level facet hypotheses he generated earlier (i.e., heart, kidney, and liver disease). This is what the expert does at segment 18. This subject generates the first two diagnostic hypotheses, constrictive pericarditis and nephrotic

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Diagnostic Reasoning and Medical Expertise

EXPERT

SUBEXPERT SEGMENT 7

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1111.

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no food tastes nausea

hoarse voice swelling of bowel

I

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liver disease

bi-ventricular heart failure

abdominal wall

abdominal wall

Icono:

t extreme edematous state

loss of

cono: liver disease

ascites

Fig. 7. Schematic representation of explanations of segments 7 , 8. and 18 of the more difficult case, pericardial effusion with cardiac pretamponade. by subexpert SE4 and expert E9. 0 given information: i 3 diagnostic component.

syndrome. In this way, the expert narrows the problem space from highlevel facets to specific diagnoses. The subexpert interprets the same information in a local manner, as in segments 7 and 8, by generating another low-level facet hypothesis, namely, ascites. To examine the extent to which the amount of verbalization influences this pattern, let us turn to one expert and one subexpert who produced highly verbal protocols and who maintained the same pattern of hypothesis

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activation throughout the problem-solving process. We can examine specifically what can be interpreted as the early generation of partial schemata and the use of confirmation strategies by experts in contrast to subexperts. Of the two subjects we present next, El generated 20 hypotheses and subject SE2 generated 29 hypotheses throughout their problem-solving process. E l , as did the rest of the experts, made a completely accurate diagnosis, whereas SE2 made a partially accurate diagnosis. Figure 8 presents the schematic representation of the segments 2, 3, and 7 by subjects, E l and SE2. Both subjects begin by considering more than one hypothesis, three by the expert and two by the subexpert. A difference between these two subjects is that the subexpert introduces a specific diagnostic hypothesis (i.e., emphysema) at the beginning (segment 2). Also in segment 3, the subexpert generates another diagnostic hypothesis, rheumatic heart fever, besides the high-level facet hypotheses of cardiac and pulmonary problems mentioned earlier. The generation of further diagnostic hypotheses is also repeated in segment 5, where the subexpert produces mitral stenosis and in segement 7, cor-pulmonale. The expert, on the contrary, generates high-level facet hypotheses (cardiac problem and respiratory problem) and a low-level hypothesis (i.e., anemia) and evaluates the hypothesis of liver involvement (segment 7). Although the subexpert seems to be able to interpret most of the relevant findings correctly, he fails to see the relevance of some key findings, as is evident in Fig. 9. In segment 28, information that is interpreted by the expert as evidence against valvular heart disease is interpreted by the subexpert as consistent with left ventricular failure secondary to aortic stenosis and, in turn, consistent with pericardial effusion. As noted previously, the expert mostly generates high-level facet hypotheses, such as the ones mentioned of cardiac, respiratory, and general systemic problem. However, when the subject generates these, as in segment 3, he generates pericardial problem, which leads him in the right direction to conclude finally the diagnosis of pericardial effusion with tamponade (segment 48). Up to segment 9, the expert subject generates high-level facet hypotheses: valvular heart disease, cardiomyopathy, and coronary artery disease. From then on, the subject evaluates the findings without introducing any more distinct hypotheses. He interprets some findings in terms of rightor left-sided heart failure and, in the process, discounts several hypotheses, such as valvular heart disease (see segment 28), primary liver problem, and ischemic heart disease. This process of generation of facet-level hypotheses leads him to conclude the final diagnosis through the evaluation of pericardial effusion and cardiac tamponade against further information. In summary, although the subjects’ protocols presented are unique in many respects, two features are salient. The first one is that experts

22 I

Diagnostic Reasoning and Medical Expertise

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lr

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I

SEGMENT7

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increased

cono:

lesslikely Ascites

1

fluid retention

marked enlargement of liver

cor-oulmonale

severe rieht ventricuiar failure

Fig. 8 . Schematic representation of explanations of segments 2, 3. and 7 of the more difficult case. pericardial effusion with cardiac pretamponade, by subexpert SEI and expert E2 (highly verbal subjects). 0 given information; i l l diagnostic component.

interpret case data from the first few segments in terms of high-level facet hypotheses, which later they evaluate. This serves to partition the problem into manageable units, thus reducing the load of working memory. The second is that once the experts have generated these hypotheses, they use them as a basis for evaluating subsequently presented data without introducing any new hypotheses. In contrast, subexperts generated

Vimla L. Patel, Jose F. Arocha, and David R. Kaufman

222

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Fig. 9. Schematic representation of explanations of segments 28 and 48 of the more difficult case, pericardial effusion with cardiac pretamponade, by subjects subexpert SEI and I 7 diagnostic component. expert E2 (highly verbal subjects). 0 given information; i

hypotheses mostly at the low-level facet, with some high-level facets and diagnostic hypotheses. Also, they kept generating new hypotheses even after producing most of the diagnostic components needed for the final diagnosis. The experts used forward-directed reasoning to generate initial diagnostic hypotheses. Once the diagnosis was generated, confirmation strategies were used to evaluate earlier diagnoses. This made them generate a high number of backward-directed hypotheses to tie up loose ends. Although the subexperts also used forward reasoning to generate the hypotheses as new text information was presented, they did not use confirmation strategies, making far fewer hypothesis evaluations after most diagnostic components had been produced.

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Using a simple problem in the domain of endocrinology presented sequentially, Joseph and Patel (1990) showed that experts and subexperts were able to keep track of their schemata because the problem was well structured and without too many diagnostic alternatives. Using the complex case in the domain of cardiology, experts used forward reasoning to rule out alternative diagnoses before presenting the main diagnostic components (Patel, Groen, & Arocha, 1990). Backward reasoning was used to tie up loose ends. For experts, immediate problem presentation appears to encourage forward-directed reasoning because all the information is available at one time. In contrast, sequential presentation seems to encourage the generation of hypotheses and subsequent conformation, refinement, and modification. B.

A N D TASKDIFFICULTY EXPERTREASONING

This section addresses the effects of problem difficulty and relevance of domain knowledge on clinical reasoning and how these effects differ as a function of immediate versus sequential problem presentation. To clarify the following discussion, the relationship between these studies is shown in Table 111, which shows the format of presentation and level of difficulty used in each study. The results with the more difficult case of pericardial effusion with pretamponade resemble those of Joseph and Patel (1990). using the less difficult case of Hashimoto's thyroiditis, in two ways. The first way is in the time-course of hypothesis formation. In both studies, we can observe two distinct phases: a hypothesis generation phase and an evaluation phase, in which the graphs, shown in Figs. 5 and 6, reach an asymptote. Subexperts take longer to reach the evaluation stage than experts. The second resemblance is that there is a predominance of forward reasoning as

TABLE 111 STUDIES

RELATING MEDICAL EXPERTISE, TASKDIFFICULTY, AND PROBLEM PRESENTATION FORMAT Problem

Format of presentation Immediate Sequential

Simple

Difficult

Patel. Groen. & Arocha (1990) endocrinology problem Joseph & Patel ( 1990) endocrinology problem

Patel. Groen. & Arocha (1990) cardiology problem Patel & Groen (unpublished study) cardiology problem

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opposed to backward reasoning with both subexpert and expert subjects. These results, which resemble those found in many other domains, can be interpreted in terms of a distinction between partial and total schemata. A total schema exists when a set of coherent propositions that accounts for the entire case is retrieved and linked to a text base in working memory (the active problem representation). This total schema is built when immediate presentation is used. When sequential presentation is used, however, it is only possible to build partial schemata, which at best account for the information presented thus far, or more accurately, for those elements that remain in working memory. In this sense, the first stage of the hypothesis formation graphs indicates the elicitation or development of partial schemata. The second stage, when t h e curve flattens out, indicates the completion of a total schema. That is to say, experts complete the schema earlier and solve the problem at an earlier stage than subexperts, and use additional information to evaluate and confirm the hypotheses thus far generated. The fact that experts develop a total schema more quickly than subexperts is taken as an indication of the distinction between generic and specific expertise. There are also some important differences in the results of the two studies that allow for a greater differentiation between generic and specific expertise. As mentioned previously, all subjects made an accurate diagnosis in the endocrine case (used by Joseph and Patel, 1990),but only experts made completely accurate diagnoses in the considerably more difficult cardiology case. This difference is taken as an indication that even though total schemata are also formed by subexperts, these schemata are not necessarily adequate. The fact that subexperts tend to generate new hypotheses, rather than confirm old ones, suggests that this inadequacy may reside in the maintenance of the irrelevant propositions from the clinical case in working memory, which in turn suggests that total schemata are not fully completed. This would seem to imply that the generic expertise of subexperts may result in a tendency to use schema-driven reasoning, even when the schemata are inadequate to solve the problem. It is of interest to note that in the cardiology case under immediate presentation (Patel, Groen, & Arocha, 1990),no subject made a completely accurate diagnosis. It is possible that although sequential presentation appears to enhance performance in both easy and difficult cases, the nature of this enhancement is different. Sequential presentation may enhance the accuracy of subexperts on easy cases and of experts on hard cases. There may be a connection between this and the notion proposed by Patel and Groen (1992) that coherence is a principal criterion for the adequacy of a schema. That coherence plays a role in the present study is indicated by the fact that there is a tendency of experts to use backward reasoning,

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which is associated with a process that Patel and Groen (1986) characterize as connecting loose ends unrelated to the main diagnosis. The cardiology case contains a number of loose ends that lead to conflicting conclusions when globally combined. Immediate presentation may result in a subject focusing on global coherence which might result in attempts to reconcile these conflicting cues. On the other hand, sequential presentation necessitates a focus on local coherence, in which such cue combinations do not play a role. When comparing the results across studies (cf. Patel, Groen, & Arocha, 1990; Joseph & Patel, 1990) to evaluate the effects of the presentation paradigm on diagnostic reasoning performance, we find that diagnostic accuracy interacted with case difficulty. In the simple case of endocrinology, subexperts did better with sequential than with immediate presentation. Experts were perfectly accurate in both situations, showing a ceiling effect. For the more difficult case (cardiology), subexperts performed better with the immediate than with the sequential presentation, whereas the experts performed better with sequential presentation than with immediate presentation. The results are presented in Table IV. In these experiments, we also asked the subjects to recall the clinical cases. It should be noted that there were no recall differences between experts and subexperts in either case. There was a ceiling effect on the recall of relevant material. Both subexperts and experts exhibit superior recall performance when compared with either intermediates or novices. These results suggest that recall measures do not discriminate at this level of expertise and perhaps that the recall of relevant clinical material is a function of generic expertise. TABLE IV DIAGNOSTIC ACCURACYB Y EXPERTISE, CASE TYPE,A N D TEXT PRESENTATION FORMAT Level of expertise and diagnostic accuracy Experts Subexperts Case type Cardiology Accurate Partially accurate Inaccurate Endocrinology Accurate Partially accurate Inaccurate

Sequential

Immediate

Sequential

Immediate

4 0 0

0 6 2

0 1

0 5 3

4 0 0

I I 0

4 0 0

0 7 1

3

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Virnla L. Patel, Jose F. Arocha, and David R. Kaufman

These results are partially counterintuitive in relation to what we know about working memory. Both sequential and immediate tasks impose a memory load, but sequential presentation should make more demands on working memory. However, this does not seem to be the case for the experts working on a difficult problem. There could be two possible reasons for these results: the first is that explanations are generated on the fly and the second is that a schema is constructed rather than retrieved. These reasons emphasize the dynamic aspects of schema construction. In the medical domain, knowledge is organized in a hierarchy of the form observations j findings +facets 3 diagnosis, where facets are intermediate constructs related to diagnostic hypotheses. Within these intermediate constructs, there are many possible permutations which lead to specific diagnoses. Furthermore, specific diagnostic hypotheses are elicited via the use of retrieval structures which provide dynamic flexibility in their ability to partition the space of possible diagnoses. These structures provide access to information in long-term memory. Physicians may be able to generate a set of relatively stable structures for maintaining intermediate results depending on the representation of the problem and then complete the diagnostic process on the basis of the information maintained in these structures. The differential use of such structure is evident in the experts’ repetitive use of the same intermediate constructs or high-level facets, such as liver involvement, which they use in a systematic way to organize and methodically evaluate patient data. The subexperts exhibit no such systematicity in their use of retrieval structures. This is analogous to the retrieval structures used by mental calculators, who set up intermediate results in working memory (Ericsson & Staszewski, 1989). These structures alleviate some of the burden imposed on working memory.

V. A.

Novice Diagnostic Reasoning

NONMONOTONICITY I N THE DEVELOPMENT OF EXPERTISE

In much of the research characterizing the nature of expertise, the focal point is the “expert” and the novice is used merely as a basis of comparison. The theory of expertise really becomes a theory of the expert, in terms of the structure of knowledge and the various performance parameters. What is needed is a more elaborate theory of the progression from novice to expert. We particularly need to understand more about novice development to advance learning theories and promote more effective instructional approaches. In recent years, there have been a number of studies that have extended the expertise approach to study novices of

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different ability levels and at different levels of training (e.g., Thibodeau, Hardiman, Dufresne, & Mestre, 1989; Chi, Bassok, Lewis, Reiman, & Glaser, 1989). In the following section, we discuss several studies of novice diagnostic reasoning. Like experts, novices can be considered on a continuum from beginners, who have just started their medical training, to advanced novices, who are in the final stages of medical school but have had relatively little practical experience. It is also important to distinguish between intermediates and beginners, possibly on the basis of time devoted to learning the domain, or having passed appropriate courses. If the category of subexperts is excluded, then a distinctive developmental phenomenon emerges, which has been termed the intermediate ejfect. This refers to the fact that, although it seems reasonable to assume that performance improves with training or time on task, there appear to be particular transitions in which subjects exhibit a certain drop in performance. This is an example of what is referred to as nonmonotonicity in the developmental literature (Strauss & Stavy, 1982) and is also observed in skill acquisition. The symptom is a learning curve or developmental pattern that is shaped like either a U or an inverted U. It should be noted that not all intermediate performance is nonmonotonic; for example, on some global criteria such as diagnostic accuracy, there appears to be a steady improvement. The intermediate effect occurs with many tasks and at various levels of expertise. The tasks vary from comprehension of clinical cases and explanation of clinical problems, to problem solving, to generating laboratory data. The subjects vary from students at different levels of training to medical residents and senior physicians. The common finding is that intermediate performance involves extraneous search. For example, in recall tasks, intermediates recall detailed information (Patel, Groen, & Frederiksen, 1986), provide extensive elaborations in explaining a patient problem (Arocha, Patel, & Patel, 1993), elicit considerable amounts of information from a patient to make a diagnosis (Kaufman & Patel, 1991), or request many extraneous laboratory tests (Patel et al., in press). The phenomenon may be because intermediates have acquired an extensive body of knowledge but have not yet reorganized this knowledge in a functional manner to perform various tasks. Thus the knowledge has a sort of heterarchical or flat structure that results in considerable search and would also make it more difficult for intermediates to set up structures for rapid encoding and selective retrieval of information. Experts knowledge is finely tuned to perform various tasks and they can readily filter out irrelevant information using their hierarchically organized schemata. The difference is reflected both in the structural organization of knowledge and the extent to which it is proceduralized to perform different tasks.

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Both of these interrelated factors are responsible for this extraneous search and the accessing of irrelevant prior knowledge. Schmidt and Boshuizen ( 1993)reported that intermediate nonmonotonicity recall effects disappear by using short exposure times (about 30 sec). This suggests that under time restricted conditions, intermediates cannot engage in extraneous search. In other words, intermediates process too much “garbage,” whereas experts do not. Novices, on the other hand, do not conduct irrelevant searches, simply because they lack a knowledge base rich enough t o search. Whereas a novice’s knowledge base is likely to be sparse and an expert’s is intricately interconnected, an intermediate may have a lot of the pieces of knowledge in place, but lack the extensive connectedness of an expert. Until this knowledge becomes further consolidated, the intermediate is more likely to engage in unnecessary search. The intermediate effect is not a one-time phenomenon, rather, it occurs repeatedly at strategic points in a student’s or physician’s training that follow periods in which large bodies of new knowledge or complex skills are acquired. These periods are followed by intervals in which there is a decrement in performance until a new level of mastery is achieved. B.

NOVICEREASONING A N D COORDINATION OF HYPOTHESES A N D EVIDENCE

It has long been recognized that, when solving problems, some subjects explore one solution path at a time, whereas others explore several solution paths simultaneously (Bruner, Goodnow, & Austin, 1956). This distinction is similar to the one made in the artificial intelligence (AI) literature between breadth-first and depth-first search procedures (see Fig. 10).

Fig. 10. Example of depth-first search ( I ) and breadth-first search (2). I n ( I ) , one hypothesis (hl) is explored in detail until it is either discarded (found not to account for the data) or accepted. In breadth-first search, two hypotheses are explored at the same time, until a decision is made between either of the two hypotheses. The symbols a1 and bl represent hypotheses being evaluated, fl and f 2 represent case data (findings).

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Depth search is a search procedure in which one path is traversed in depth. This path is traversed first, and when the end of that path has been reached, it backtracks to the closest branching node in the tree and traverses the new path in the same way (Harel, 1987). This is equivalent to exploring a single hypothesis in depth until all the important consequences have been explored. The other search procedure is a breadthfirst procedure, in which all nodes at one level are explored before going to deeper levels. In this context, several hypotheses are explored simultaneously, rather than attempting to rule out, modify, or confirm any specific hypothesis. In machine problem-solving research, both of these procedures are considered to have their drawbacks (Charniak & McDermott, 1987). On the one hand, depth-first search may lead one through the wrong path, resulting in a loss of time and resources in the process; on the other hand, breadth-first search is highly inefficient because one explores all possible paths, good or bad, equally. In AI, these strategies are considered as blind or brute force strategies, in the sense that they do not make use of knowledge t o guide their search. Algorithms have been developed to overcome these limitations by using evaluation functions of the search path taken (e.g., heuristic search). The use of search strategies by human problem solvers is, most of the time, informed by the knowledge possessed by the subjects, such that (a) not all paths are explored, but only the most relevant ones, and (b) a single path is not explored in its entirety, rather, it is explored until sufficient information has been obtained that satisfies the goal. A study by Arocha et al. (1993) extended the research of Joseph and Pate1 (1990) on hypothesis generation and evaluation to novice subjects. They compared the performance of novice medical students at three levels of training in solving two cardiology problems. The subjects were medical students in the second (early novices), third (intermediate novices), and fourth (advanced novices) year of medical training. The objective of the research was to investigate the strategies novices used in coordinating the hypotheses they had generated to account for the case data. Clinical problems were designed so that a particular hypothesis (i.e., myocardial infarction) was suggested by the initial case presentation as the medical trainees thought aloud while solving the problems. The first segments of both cases were consistent with a typical presentation of myocardial infarction. Segments 2 and 3 presented information that was inconsistent with myocardial infarction, and was suggestive of viral pericarditis in case I , and aortic dissection in case 2. The case was segmented into a standard format in which a clinical interview is usually conducted, including the presenting complaint, the patient’s history, and the results from the physi-

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cal examination. The subjects were presented with information, one segment at a time, on a microcomputer and asked to think aloud while they read and interpreted the problem. At the end of each segment, the subjects made tentative diagnoses. Unlike the Joseph and Patel study, in which segments consisted of single sentences, the segments presented to the subjects were in the form of paragraphs. Finally, they were asked to provide a final diagnosis and an explanation of the patient problem. The verbal data was analyzed for diagnostic accuracy and the utilization and mapping of clinical cues in generating the diagnostic hypotheses. The results showed that early novices (second-year students) generated and retained their initial hypothesis, which corresponded to the most common disease, using a strategy similar to depth-first search, in the sense that these subjects tended to generate the initially suggested hypothesis and to maintain this hypothesis throughout the whole case. They did so despite the presence of inconsistent evidence with the initial hypothesis. Early novices activated the fewest number of hypotheses and generated very little search. Intermediate novices (third-year students) used a similar strategy to breadth-first search, in the sense that several hypotheses were generated and evaluated in parallel, without resolving or eliminating them. That is, they tended to generate several diagnostic hypotheses to account for different findings, which resulted in a larger hypothesis space, a great deal of extraneous search, and a less coherent problem representation. This strategy is quite consistent with a hypothetico-deductive model of diagnosis described by Elstein et al. (1978). Like the intermediate subjects, advanced novices (fourth-year students) initially generated and evaluated multiple hypotheses, using a strategy similar to breadth-first search. However, they did so to account for the same set of findings, generating competing hypotheses. This resulted in a decrease of irrelevant search as compared with the intermediates. Then, they evaluated these hypotheses against the data. In this way, advanced novices narrowed down the initial pool of hypotheses to only one or a few at the end of the problem. Figure 11 presents an illustration of the types of strategies used by three subjects at different levels of training. The figure shows schematic examples of the initial hypotheses generated by the three subjects. The early novice’s response to presentation of the first segment of the case consisted of the common and most typical disease (myocardial infarction). This subject evaluated only this single hypothesis against the subsequent information in the problem, changing the hypothesis only at the last segment. The intermediate subject generated various hypotheses to account for several cues in the case. Like the intermediate subject, the advanced novice also generated several hypotheses, with some differences: first, the hypotheses were produced to account for the same set of data; second,

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the hypotheses generated were all of the high-level facet type. Once these hypotheses were generated, they were evaluated at the same time against subsequent data. In summary, whereas early novices generated very few hypotheses, which mainly corresponded to the most common and typical diagnostic hypothesis, intermediate novices generated many different hypotheses,

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activating a great deal of irrelevant prior knowledge. This resulted in larger problem spaces and a great deal of unnecessary search. Advanced novices also generated a variety of hypotheses, but narrowed them down to the most relevant as new information was processed. The question that remains is whether these differences can be accounted for by the conscious deployment of different search strategies or as a side effect of differential knowledge activation. Previous research has found that a powerful strategy used by successful problem solvers in science domains is a form of breadth-first search. In their research on scientific reasoning, Klahr and Dunbar (1988) concluded that subjects who were successful in problem solving considered multiple initial hypotheses from different contexts to reach the correct solution to the problem. This is compatible with the findings of our study: by generating a breadth-first search, the subjects considered a large space of hypotheses, which later they pruned as new evidence was presented. This allowed the evaluation of competing hypotheses, which resulted in the discarding of those hypotheses that were less coherent with the data early in the problem-solving process. Because of a lack of discriminative capabilities and schema that lack the appropriate tests for determining the veracity of hypotheses, it is constructive for advanced novices to generate multiple hypotheses. This is in contrast to experts, who need not consider more than a few hypotheses. Intermediate subjects, however, tended to employ a different form of breadth-first-like search, in which one hypothesis was specifically generated to account for a particular case finding. This strategy resulted in a situation where the space of possible hypotheses remained large because several parallel hypotheses were generated to account for discrete pieces of data. Finally, early novices displayed a form of depthfirst search, which resulted in sparse representations and served as an obstacle to considering alternative hypotheses. Although we have not tested the hypothesis concerning the source of the strategies, we have hypothesized that it is due to the process of knowledge activation, rather than to a conscious strategic deployment. More specifically, it may be a function of the knowledge that subjects have available for constructing a partial case representation (Arocha & Patel, in press-a; Groen & Patel, 1988). Given that a problem representation may activate different hypotheses, many of them irrelevant, and the fact that subjects fail to coordinate the data with a minimal hypothesis set, they are more likely to use available (somewhat blind) search strategies to account for all or most of the information in the problem. Although, in general, with an increase in training there is a corresponding increase in diagnostic accuracy, there does not seem to be an enhanced ability to coordinate evidence with hypotheses. As a matter of fact, the progression in coordination of theory and evidence shows the same intermediate effect

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that has been found in other tasks; that is, a degradation in performance by the intermediate subjects. At a more detailed level of analysis, then, the differences between subjects at various levels of training suggest differences in the use of coordination operations. We characterize these as a set of operations that transform a given knowledge state into a subsequent knowledge state (Hassebrock & Prietula, 1992;Chinn & Brewer, 1993). These are particular classes of inferential operations that can be performed to generate, modify, confirm, or eliminate hypotheses and to evaluate the significance of a piece of data. Arocha and colleagues (Arocha et al., 1993; Arocha & Patel, in pressb) studied the coordination of hypotheses and data by presenting novices with a seemingly typical case accompanied in subsequent segments by anomalous or inconsistent data. Inconsistent information includes pieces of evidence that point to alternative diagnoses. The data suggest a tendency for the early and the intermediate novices to ignore or reinterpret inconsistent data, and a tendency for the advanced novices to change their hypotheses. However, intermediate subjects misrepresented more pieces of evidence (in terms of reinterpretations of data) than early novices. With training, subjects become more sensitive to what their data “tells” them. This is particularly evident of the advanced novices, as they changed their hypotheses more frequently than either early or inermediate novices. This may be taken as an indication of the development of schemata that subjects use to test the appropriateness of hypotheses when reasoning under conditions of uncertainty. Novices at earlier stages in their training may, in fact, have some of the prerequisite knowledge, but may not have the ability to recognize the appropriate context to use it in. In other words, this schematic knowledge is rather inert. As suggested earlier, inert knowledge may in part explain the nonmonotonicity of development of diagnostic reasoning skills. Experience interacting with real patients provides students with an opportunity to tune their knowledge. In the previously described study, intermediate novices have considerably more knowledge than the early novices, but considerably less experience than advanced novices.

VI. General Discussion A. SUMMARY This section presents a synopsis of the research into diagnostic reasoning discussed in this chapter. The studies of diagnostic reasoning were, in part, motivated by a somewhat anomalous description of the reasoning

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process by Elstein and colleagues (1978). They suggested that expert (and novice) reasoning was characterized by a hypothetico-deductive model of reasoning in which multiple hypotheses were explored in parallel. This was anomalous given that it was generally considered to be a weak strategy in other domains. Patel and Groen (1986) found that experts who accurately diagnosed a problem used a forward-directed reasoning process from cues in the case toward a diagnostic hypothesis. Experts who misdiagnosed the case used a mixture offorward and backward reasoning. Patel, Groen, and Arocha (1990) found that the pattern of forward reasoning was disrupted and that even experts use some backward reasoning when confronted with loose ends in a more difficult case. Expertise is not an all-or-none phenomenon; rather, it reflects an extended continuum, particularly in a complex domain like medicine. We distinguish between two kinds of expertise: generic and specific (Patel & Groen, 1991a). Every physician who is a specialist has a specific area of expertise that is rather well delimited (e.g., cardiology). In addition, he or she has a substantial body of knowledge pertaining to other areas of medicine, which we describe as generic expertise. It was demonstrated that experts at high levels of the novice-expert continuum, including specific and generic expertise, can identify, recall, and draw inferences from relevant information. Similarly, in relatively uncomplicated problems, we observe no differences in diagnostic accuracy. We did find that when the case is more difficult, specific experts exhibit a higher level of diagnostic accuracy than the subexperts (generic experts). In addition, diagnostic accuracy is strongly associated with directionality of reasoning. We also varied the format of presentation, immediate versus sequential, across different studies. The subexperts appeared to perform better, in terms of diagnostic accuracy, in the sequential presentation condition. The experts performed equally well on both tasks. We found that experts generated higher level hypotheses or facets early on in the diagnostic process, which served to partition the problem space and allowed them to generate an accurate diagnosis more rapidly than subexperts. These subexperts made many lower level or local inferences that did not advance them closer to the solution state. Like experts, novices can be considered on a continuum from beginners to advanced novices, who are more advanced in their training. We have found that novices seem to deploy different search strategies as their training in medical school increases. Whereas early novices use a strategy similar to depth-first search, considering and evaluating a single hypothesis, intermediate and advanced novices use a form of breadth-first search, as they consider and evaluate several hypotheses concurrently. Intermediate novices conduct more unnecessary search than either early or ad-

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vanced novices, activating more irrelevant knowledge and failing to coordinate efficiently their hypotheses with their data. Furthermore, novices differentially use coordinating operations between hypotheses and data. Whereas early novices tend to ignore anomalous data, advanced novices tend to change their hypotheses to fit the data. Intermediate subjects ignored the anomalous data, but also made slight modifications to their hypotheses. We have suggested that these differences may be a function of the novices' knowledge structures rather than an explicit choice to pursue a particular strategy. It is apparent that the development of expertise does not follow a strictly monotonic pattern. There are periods in which we can observe a decrement in performance with advances in training. This is, in part, due to a stage in learning when one has accumulated a considerable body of knowledge but not consolidated it or put it to use. That is to say, the knowledge is inert-with additional experience it becomes compiled, resulting in superior and more efficient performance.

B.

REASONING AND

THE

CONSTRUCTION-INTEGRATION MODEL Medicine is a complex, semantically rich domain. Methods and theories embodied in our work have been predicated on the claim that there is a close relationship in medicine between comprehension and problemsolving processes (Groen & Patel, 1988). Arocha and Patel (in press-a) have recently attempted to account for some of the preceding findings with a simulation model based on the construction-integration theory of Walter Kintsch (1988). The construction-integration theory is implemented in a hybrid symbolic-connectionist computational model of comprehension. Our efforts to use this theory originate from earlier attempts in our laboratory (Groen & Jerney, 1988; Groen & Patel, 1988) to advance a model of medical reasoning based on an application of the Kintsch-Greeno (Kintsch & Greeno, 1985) theory of comprehension in problem solving. We feel that the construction-integration theory accounts for important aspects of diagnostic reasoning, which the earlier model failed to account for. One such aspect is the intermediate effect. Our current model overcomes some of the limitations of the earlier model and accounts for the comprehension aspects of medical reasoning and the dynamic aspects of doctor-patient interaction. We now present a description of the construction-integration theory as applied to the medical domain. Comprehension is conceived as a cyclical process involving two phases: a rule-based construction process and an integration process. The construction phase involves (a) activation of text concepts and propositions connecting to the text, and (b) elaboration of these concepts by activating

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associated concepts or propositions in long-term memory. A characteristic of rules used in the construction phase is that they are “weak,” that is, they are rules that do not optimally derive from the input, but that fire even if they are not relevant to the task at hand. In this way, the construction process generates relevant as well as irrelevant concepts or propositions. The process of integration occurs by spreading activation, which generates a situation model by strengthening the activation values of relevant propositions and deactivating those of irrelevant propositions. A situation model is a cognitive representation of the events, actions, and, more generally, the circumstances of the problem (van Dijk & Kintsch, 1983). A partial situation model gets completed through multiple cycles of processing. Relevancy in the model is determined by the extent to which the new information “coheres” with the existing information. Furthermore, this phase also involves some constraints that have to be satisfied. The concepts that are not integrated are maintained in working memory for a specified number of cycles, at which point they are dropped. An interpretation of medical expertise suggests that increasing expertise involves the construction of smaller, and more abstract, subsets of relevant partial schemata in the form of high-level, facet-type hypotheses. This construction process has the advantage that only small numbers of activations are generated in the activation phase and that a more efficient and systematic integration process takes place. Two consequences can be derived from the results of this process. The first concerns the organization of medical knowledge of experts. Their schemata are organized in a manner that resembles closely the hierarchical structure of medical knowledge hypothesized in Patel, Evans, and Kaufman (1989). This allows them first to activate elements of competing high-level facets before more specific schemata linked to specific diagnostic hypotheses are activated. Research in other domains (Doane, MacNamara, Kintsch, Polson, & Clawson, 1992) shows that one important difference between experts and less-than-experts is that an expert’s knowledge base contains more abstract rules and concepts than those of novices. In terms of our research, this suggests that the difference between subexperts and experts in the activation process is that the experts’ comprehension process activates case cues to highlevel facets, whereas subexperts activate concepts at the low facet level and, sometimes, at the diagnostic level. The second consequence is that the experts’ process of hypotheses generation results in the activation of elements of a schemata that are more discriminating, because the schemata are at the same level of the knowledge hierarchy. These hypotheses can be generated faster because of their more discrete nature. Discrete hypotheses belong to the same level in the medical knowledge hierarchy, they minimally overlap, and

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are more likely to compete. This is not the case of subexperts, who seem to activate elements that are farther apart in the hierarchical knowledge base and, therefore, have lesser discriminative value. As our results show, the generation of hypotheses by subexperts is characterized by the activation of hypotheses at different levels of the knowledge hierarchy; for instance, the activation of low-level hypotheses leading to disparate diagnostic hypotheses or the concurrent activation of hypotheses at the diagnostic and low facet levels. This resulted in subexperts reaching their final diagnosis at a later point in time than the experts and the continuing evaluation of further hypotheses after all the diagnostic components necessary for the correct diagnosis had been generated. It seemed that they had failed to integrate the generated knowledge into a coherent hypothesis. Intermediates activated many disparate and irrelevant hypotheses at different levels of the knowledge hierarchy. They generated many parallel hypotheses to account for different case findings without discriminating between them. Often, these hypotheses originated at the facet and diagnosis levels. This had the effect of producing a large problem space that was the result of considerable extraneous search. The differences between the more expert subjects and the novices may rely on the extent and relative accuracy of the hypotheses generated, simply reflecting differences in knowledge activation. We assume that the size of the network, that is, the breadth of activation, parallels the expanse of the problem space. These results suggest that both aspects of the construction-integration process may play a crucial role in diagnostic reasoning, but that the relative importance of either of the phases may depend on the training level of the subjects. Unlike the subexperts, who failed to integrate their generated schema once all the correct diagnostic components had been generated, the main failures of novices seem to be in both phases of the process: in the activation of relevant hypotheses and in the integration of these hypotheses into a single explanation for the case. Another important aspect common to both subexperts and novices is the generation of multiple hypotheses at different levels of the knowledge hierarchy. Kintsch’s construction-integration theory (CI) provides a framework for explaining those aspects of medical reasoning that rely heavily on comprehension processes (Arocha & Patel, in press-a), but because it is a comprehension theory, it alone cannot account for the circumstances when forward reasoning breaks down and the strategies diagnosticians use to repair comprehension breaks down. This breakdown signals a transition from forward to backward reasoning. What subjects do in these circumstances is to attempt to tie up loose ends and develop an adequately coherent explanation. Thus, one assumption is that backward reasoning emerges to make a coherent explanation of the case.

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C. EXPERTISE A N D “DEEP“ REASONING In the A1 literature, deep reasoning has often been equated with reasoning with a causal representation of the internal workings of a system that adds significant explanatory and predictive capabilities to the problem solver. This is an issue of some controversy in medical A1 (Keravnou & Washbrook, 1989). Cognitive psychology contains little information that specifically corresponds to the distinction between deep and shallow reasoning in AI. There is, of course, research on areas such as explanation and causal reasoning that are directly relevant to the corresponding issues in AI. There is also the issue pertaining to the depth of representation, as indicated by the finding that experts categorize problems according to “deeper” domain principles, whereas novices organize problems according to their surface structure (Chi et al., 1981). We can assume that individuals are capable of reasoning at different levels of abstraction, that methods exist for switching between levels depending on task demands, and that knowledge representations are accessible at all levels to varying extents. The level of the representation is likely to be highly task and domain specific, and is also likely to be influenced by availability of knowledge and reasoning skill. Given the complex ontological structure of medical knowledge and the task of medical diagnosis, it would seem reasonable that knowledge of disease classification and knowledge of underlying causal pathophysiology overlap in the context of diagnostic reasoning. However, the scope of the application of basic science principles is not as evident in the practice of medicine as in the applied physical domains (e.g., engineering). One can view diagnostic reasoning as a continuum from disease classification to more complex forms of causal reasoning. From this perspective, experts can be viewed as having deep knowledge, and this knowledge is exploited when the conditions of applicability warrant its use. As discussed previously, Chandrasakeran et al. (1989) distinguish different categories of diagnostic reasoning of varying levels of depth. Kaufman and Patel (1994) adapted these distinctions to characterize three ways in which different forms of causal knowledge can influence diagnostic reasoning. The first method involves some form of heuristic classification relating findings to hypotheses. This would also include case-based or exemplar-based reasoning. Experienced physicians have access to a wide range of patient exemplars, which can result in rapid classification of a patient problem. Heuristic classification and cased-based reasoning may be better attuned to reasoning in routine nonproblematic situations. In this case, the inference would be operating on knowledge elements of findings, hypotheses, and patient exemplars. A second method involves

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causal reasoning using compiled causal knowledge. This refers to knowledge of causal expectations at various levels of detail (e.g., biochemical level) that people compile directly from experience and partly by chunking results from previous problem-solving endeavors (Chandrasakeran, 1991). This sort of knowledge is retrieved from memory and can be used to extend a chain of inference when loose ends or anomalous cues resist a simple classification scheme. The third method involves the use of “runnable” mental models. When a patient problem appears to be rather unusual or involve multiple systems, it seems likely that experts can access to mental models (Gentner & Stevens, 1983) or the strucrure-functionbehavior models that characterize qualitative reasoning systems in A1 (Chandrasakeran, 199 1). These models have an explicit representation of structural components and their relations, the functions of these components (in essence their purpose). and their relationship to behavioral states. The causal and diagnostic knowledge can be generated by running or simulating the system and qualitatively deriving behavioral sequences that can identify and explain the malfunction. The knowledge is not prestored but can be generated in run time to find fault in a system. The three models of diagnostic reasoning-heuristic classification, compiled causal knowledge, and structure-function-behavior models-correspond to ascending levels of explanatory and predictive power, but are also incrementally computationally expensive. Our results indicate that experts, when asked to explain or justify a diagnosis, make little use of causal reasoning. Instead, they provide a minimal justification consisting of data-driven, forward inferencing, based on finding hypothesis associations rather than causes. This pattern changes only if the explanation is inadequate. In particular, any explanation that is inadequate has “loose ends” unconnected to the final diagnosis and tends to generate far more elaborate explanations that might be considered to satisfy some of the criteria of “depth” (Patel, Groen, Ramoni, & Kaufman, 1992). The importance of loose ends in diagnostic explanation indicates that all subjects seem to use some kind of satisficing heuristic based on a coherence criterion, where coherence is defined as the extent to which every finding is linked to a diagnostic hypothesis or, more technically, in terms of the connectedness of a semantic network representing a subject’s protocol. The coherence criterion is similar to the one used by Thagard (1988) for distinguishing between competing hypotheses. The term satisficing refers to the use of a stop rule based on satisfaction with the pattern of one’s reasoning (Simon, 1989). The rule-out strategies of the expert guarantee early satisficing, and the pure forward reasoning characterizing accurate solutions of routine problems may simply reflect this satisficing. The processing of forward reasoning in generating incorrect diagnoses by

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novices results from a different kind of satisficing based on the lack of knowledge and the lack of sophisticated rule-out strategies. We can speculate that physicians may be guided by coherence constraints in determining the appropriate level of depth. Therefore, if one can determine the veracity of a hypothesis merely on the basis of the extent to which the patient findings cohere with a given hypothesis, then there is no need to delve deeper. If the problem involves one or more significant anomalous findings that resist classification, then one can attempt to develop an explanation at an appropriately deeper level of abstraction. As for the intermediates, it seems unreasonable to assume that they lack a stop rule based on coherence. Rather, it seems that in cycling through the steps of hypothesis generation and evaluation, they generate hypotheses that work against the generation of coherent chains of inference. It is plausible that transitions in learning and knowledge acquisition are marked by periods in which there is an observed diminishing of coherence, followed by a restructuring of knowledge in which coherence reemerges. This explanation is consistent with one offered by Lesgold et al. (1988), suggesting that intermediate radiologists oscillate between familiar and inadequate representations of a problem and improved but still unstable representations. They also observed that intermediate explanations are marked by clashes between deeper and more superficial representations. D.

REASONING, EXPLANATION, A N D LEARNING

Our use of the diagnostic explanation paradigm has implications for theories of learning. In the case of our own paradigm, we find that forward reasoning is associated with successful performance and tends to disappear when a subject is uncertain or unsuccessful. This can be generalized in terms of the distinction between strong methods and weak methods. The former might be viewed as efficient ways of utilizing existing knowledge, whereas the latter are less efficient ways of proceeding when knowledge is inadequate. Newell presents an elaborate typology of weak methods (Newell, 1980): some are simply heuristics for achieving an approximate solution; others, however, are means of attaining new knowledge, for example, in the case of resolving impasses. The presence of backward reasoning in an explanation paradigm may therefore imply that the subject is attempting to learn something new from the explanation. These results have implications for understanding the performance of novices and intermediates. Even though a novice’s explanation may be riddled with bugs and misconceptions, its harmful effects may depend on the direction of reasoning. If the reasoning is forward, then the subject

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is likely to view his o r her existing knowledge base as adequate within a particular context. This may result in misconceptions, perhaps long lasting and difficult to eradicate. If the reasoning is predominantly backward, then the misconceptions might best be viewed as transient hypotheses which are later either refuted or modified in the light of experience. With intermediate subjects, the reason for errors does not lie with serious misconceptions as much as with a lack of selectivity, resulting in an excessive use of irrelevant information. The fundamental issue may lie in how this information is being used. The use of forward reasoning may indicate a dogmatic reliance on existing knowledge. Backward reasoning may indicate that the subject is attempting to learn how to separate out the relevant from the irrelevant by testing the adequacy of hypotheses to explain data. However, Sweller (1988) has found that backward reasoning can increase the cognitive load on novices and thus impede learning. Because weak methods tend to make use of some form of backward reasoning, evidence that they are, in fact, being used comes from the points in our protocols where forward reasoning breaks down. This seems to be associated with two kinds of events: One is an incomplete or inaccurate diagnosis; the other is the existence of loose ends. As we have pointed out, these may be related, since incomplete diagnostic explanations have loose ends. Thus it seems reasonable to argue that loose ends give rise to weak methods. It seems reasonable to assume that an expert may not be aware that his or her knowledge is leading to an inaccurate diagnosis, but is simply aware of the existence of the nonsalient cues that cannot be linked to the main diagnosis. In other words, the only difference between accurate and inaccurate diagnosis is the presence of loose ends. A diagnosis might be viewed as a theory about a clinical case. The loose ends, then, are essentially anomalies. Much as anomalies are highly correlated with an inaccurate theory, so loose ends are correlated with an inaccurate diagnosis. It is interesting to note that it is usually when faced with anomalies that scientists resort to the classical hypothetico-deductive method, which, as indicated previously, is a form of backward reasoning. It should also be noted, however, that scientific theories persist despite anomalies, hence inaccurate diagnoses may persist in an analogous fashion. It is well known that anomalies d o not necessarily result in a change in theory-thus the transition from forward to backward reasoning may be motivated more by the desire to "patch" a diagnosis by reinterpreting data that is inconsistent with it-but it may also result in the further development of expertise. This development might occur when an expert attempts to constructively explain the loose ends, rather than simply justifying an existing hypothesis.

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In general, our discussion supports the relevance to medicine of the classical theory of expertise as far as the reasoning of expert physicians is concerned. In routine situations, medical specialists appear t o use the same knowledge-based inductive strategies used by experts in other areas. However, it gives a highly over-simplified view of the development of expertise. In particular, it is clear that expert reasoning is not something that can be taught. Instead, it seems likely that forward reasoning is a strategy that appears whenever an individual possesses a highly developed knowledge base that can be brought to bear on a problem-solving situation. As Hunt (1989) suggests, such knowledge may be in the form of organized schemata rather than the arbitrary collection of rules postulated by the classical theory. The issue of how such schemata develop is one of the major unsolved problems of cognitive psychology. It follows that the classical theory of expert-novice differences is largely inapplicable to the issues faced in learning, with two exceptions. First, it defines the end point of a continuum. Second, it provides a basis for flatly rejecting the notion that a method of reasoning should be taught to students because i t is used by experts. Forward reasoning is too fragile for nonexperts. Even though our research yields some evidence that experts resort to some form of hypothetico-deductive reasoning in nonroutine situations, the nature of this reasoning is highly constrained by the expert’s knowledge base. A medical student does not have this knowledge base and will take many years to develop it. Hence his or her use of hypothetico-deductive reasoning is likely to be completely different. On the other hand, our research indicates that it may be possible to shed light on the potential usefulness to students of hypothetico-deductive reasoning and other weak methods. The key seems to be to shift the focus of research from problem solving to explanation. Problem solving, by itself, may yield little learning. In contrast, important learning may take place when one tries to explain a problem either to oneself or t o others. It seems unlikely that learning will take place if such an explanation is completely coherent with no loose ends. Hence, perceived incoherence may be necessary for learning from one’s explanation. Loose ends define incoherence and it is precisely at this point that backward reasoning appears to take hold. All this may indicate a certain complementarity between elaboration generated during explanation and hypothetico-deductive reasoning (Patel, Groen, & Norman, 1993). Hypothetico-deductive reasoning ties may generate a false sense of coherence if the deductions are inadequately tested. Even if they are, the issue of how individuals handle anomalies is obscure, t o say the least. Loose ends are essentially gaps in coherence. The disruption of forward reasoning reflects an attempt to repair a gap in coherence.

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In SOAR, an architecture for general cognition, learning begins with the recognition of impasse when the current problem representation is inadequate (Newell, 1990). A subgoal is set up to resolve the impasse and, if successful, the solution strategy is chunked and stored in LTM for future problem solving. Analogously, backward reasoning can lead to a resolution of an impasse and lead to the development of more effective solution strategies. E.

FURTHER THOUGHTS

Recently, we have attempted to extend theoretical accounts of comprehension, directionality of reasoning, and the coordination of evidence and hypotheses as a function of expertise beyond the domain of medicine. Toward this end, we have endeavored to adapt computational models that have a certain generality to them. In particular, we have been working with Kintsch’s (1988) construction-integration computational model, which was briefly described in this chapter. If we assume that the pursuit of coherence determines much of the reasoning process, then clearly we need a theory of explanatory coherence. We have also started to explore Thagard’s (1988) theory and computational model of explanatory coherence (ECHO) to consolidate various strands of our empirical research. Recent work has shown that the problem of evaluating competing explanations, can be addressed by a symbolic-connectionist model, ECHO. This model takes as inputs symbolic representations of relations among propositions that correspond to data and hypotheses. Then, the model builds a constraint network linking units representing the propositions, which support the hypothesis with greater explanatory breadth and simplicity. Interestingly, Britton and Eisenhart presented a paper at the meeting of the 1993 Cognitive Science Society where they attempted to use the CI model in conjunction with a harmony statistic to develop a coherence metric. The harmony score represents a mathematical analysis of the internal consistency of a possible state of the cognitive system (Smolensky, 1986). Britton and Eisenhart have applied this analysis to characterize differences between experts and novices in comprehension tasks. They report somewhat analogous findings to our own. The harmony measure in the context of the CI framework provides an alternative to ECHO to account for explanatory coherence. The advantage of adapting a domain-independent computational model is the uniform representational approach and common architectural assumptions that constrains a theory in specific ways. The ECHO and CI models have the added advantage of embracing symbolic-connectionist approaches which merge the expressive capabilities of symbolic systems

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with the flexibility of parallel constraint satisfaction architectures (Holyoak & Spellman, 1993). It is very likely that any comprehensive theory of expertise will need to take into account both the symbolic and subsymbolic nature of human cognition. There has been a great deal of research into understanding the nature of expertise over the past 20 years. However, most theories have tended to be local, focusing on specific phenomena and have attempted to explain domain-specific empirical regularities. This has resulted in a pool of theoretical constructs that constitute a framework for rather than a comprehensive theory of the nature of expertise. We recognize the very same limitation in our research. The research reported in this chapter has emphasized cognitive and epistemological perspectives on reasoning and medical expertise. In particular, we have examined aspects of memory, knowledge structures, and reasoning strategies. One of the central goals of research on expertise has been to discover how knowledge of complex domains is represented and how to facilitate the acquisition of this knowledge through both formal and informal instruction. However, models of human reasoning have generally been based on laboratory studies. Although, these studies have been influential in developing theoretical models of expertise, reasoning, and, more recently, learning, they have not been adequately tested in ecologically valid contexts. Investigations in naturalistic settings are particularly critical for the development of ecologically valid learning theories and innovative instructional approaches. In recent years, there has been a movement in cognitive psychology toward studies in naturalistic contexts (Brown, 1992; Dunbar, 1993; Leprohon & Patel, (in press); Rogoff & Lave, 1983). This transition has necessitated an expanded theoretical framework and the development of methods to characterize the complex dynamics of real-world cognition to determine how knowledge is applied in real-world situations (Levine, Resnick, & Higgins, 1993). This development has resulted in a shift in emphasis from studying the cognitive processes of the solitary individual to the investigation of the collective problem-solving processes involved in work groups. It is apparent that much of medical problem solving is in fact a collective endeavor distributed among expert specialists, residents, medical students, nurses, and other personnel such as laboratory technicians. We now have sufficiently well-developed theoretical models and analytical methods to conduct experiments in narturalistic settings. This in turn will refine our intuitions for conducting further laboratory studies. This will also provide us with a basis for iterative theory development that is based on both rigorous methods and observations in ecologically valid contexts. Future research and related cognitive theories will need to take a broader range of factors into account. This point has been

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underlined by the recent challenge from situativity theory (cf. Vera & Simon, 1993; Greeno & Moore, 1993; Cognitive Science Special Issue: Situated Action, 1993). While an extended discussion of the representational issues is beyond the scope of this chapter, it is our belief that theories of cognition will need to more seriously consider the social context and the role of knowledge constructed in context. There has been considerable progress made in understanding the process of diagnostic reasoning and medical expertise, and more generally, into understanding the nature of expertise. However, there is a need for considerable theoretical integration of disparate empirical phenomena. In addition, there is a need for more longitudinal studies to better understand the progression of knowledge and reasoning skills. It is our belief that the study of expertise in concert with recent advancements in learning theories suggests that we are progressing rapidly into understanding the development of outstanding human performance.

Appendix: Glossary of Medical Terms Anasarca Generalized swelling of the body. Anemia Decrease in the number of red blood cells below the normal range. Aortic stenosis Narrowing of the aortic valve causing an obstruction of the blood flow from the left ventricle into the aorta. Ascites Accumulation of fluid in the abdomen. Autoimmune process Related to diseases of the immune system. Cardiac tamponade Compression of the heart to the extent that it impedes its normal expansion and contraction. Cardiomyopathy Any disease affecting the myocardium. Cor pulmonale Heart disease secondary to disease of the lungs or their blood vessels. Edema Swelling of a part of the body. Emphysema Condition characterized by overinflation and destruction of alveolar walls of the lungs. Galactorrhea Abnormal lactation not related to pregnancy or childbirth. Usually a sign of an endocrine disorder. Hashimoto’s thyroiditis A disease of the thyroid of genetic origin that affects mostly women. It is characterized by the production of antibodies in response to thyroid antigens.

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Heart failure Inability of the heart to pump enough blood to meet the requirements of the body. It may have its source either in the right or the left side of the heart. Hepatic congestion Accumulation of blood in the liver. Hypothyroidism Disease characterized by decreased activity of the thyroid gland. Hypoventilation Lack of oxygen in the blood caused by a respiratory problem. Mitral stenosis Narrowing of the mitral valve causing obstruction of the blood flow between the left auricle and ventricle. Myocardium Cardiac muscle o r middle layer of the heart. Myxedema Severe form of hypothyroidism. Nephrotic syndrome Disease of the kidneys, specially degenerative, characterized by swelling (edema) and large quantities of protein in the urine. Pericardial effusion Accumulation of fluid in the pericardial sac. Pericardial sac Layered envelop that surrounds the heart. Pericarditis Inflammation of the pericardium or pericardial sac. Pleural effusion Accumulation of fluid in the lungs. Rheumatic heart fever Inflammatory disease caused by infection which may produce damage to the heart wall or the heart valves. Thyromegaly Increased size of the thyroid gland. Vitiligo Skin disorder of unknown origin characterized by patches of unpigmented skin over the body. ACKNOWLEDGMENTS This chapter reports a decade of research conducted by Vimla Patel and Guy Groen, who passed away August 1992. This chapter is dedicated to his memory. The research reported in this chapter was supported by grants from three Canadian granting agencies, Medical Research Council, Social Sciences and Humanities Research Council, and Natural Sciences and Engineering Research Council, to Vimla L. Patel. We thank Earl Hunt, Douglas Medin. Walter Kintsch, Alan Lesgold, Keith Holyoak. and Andre Kushniruk for their constructive comments and valuable suggestions on the earlier draft of this paper.

REFERENCES Allard, F., & Starkes, J. L. (1991). Motor skill experts in sports, dance and other domains. I n A. Ericsson & J. Smith (Eds.), Toward a general theory of experfise: Prospects and limifs (pp. 126-152). New York: Cambridge University Press.

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OBJECT SHAPE, OBJECT NAME, AND OBJECT KIND: REPRESENTATION AND DEVELOPMENT Burbara Landau

I.

Introduction

Recent work in the acquisition of object naming has been framed by two critical problems, both concerning how learners generalize from object names on the basis of limited data. The first problem concerns how learners fix the meaning of a novel word on the basis of their observations of the world. As described by Quine (19601, the problem is one of indeterminacy: There exists an infinite number of possible meanings consistent with a learner’s observations of the context in which an utterance occurs. For example, having heard the word rabbit while observing some object in motion, learners could assume that ruhbit means “rabbit,” but they could also assume that it means “furry thing,” “moving object,” “part of ear and tail,” and so on. The second problem concerns how learners make inferences about new properties of category members. Among adults, certain kinds of inferences seem to follow naturally from category membership even though they may be unsupported b y specific experience. For example, if we observe a new animal and hear it labeled “dog,” we would all assume that it shares many characteristics (such as diet, expectancy, sound production) with other members of the category “dog” (see Putnam, 1977; Schwartz, 1977; Medin & Ortony, 1989; Shipley, 1993). This occurs quite naturally despite the sometimes radical differences in perceptual similarity between THE PSYCHOLOGY OF LEARNING A N D M O T I V A T I O N . VOI.. 31

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the new and old members. Just as with Quine’s problem, the question arises how learners come to make such inferences if experience per se does not provide the answer. Despite the logic of these problems, first-language learners seem to solve both. They acquire a large vocabulary of object names within the first few years, and their use of this vocabulary indicates that they quickly fix on meanings that are shared by the rest of the speaking community. Furthermore, despite the fact that any learner can obtain only limited information about the kinds of properties that are common to category members, young children appear to share with adults the belief that the members of named categories share many properties (Medin & Ortony, 1989). This belief supports their inferences that objects with the same name also share novel unobserved properties (Carey, 1985; Gelman & Markman, 1986; Gelman, 1988). These two facts of generalization suggest that, for learners, a great deal follows from the fact that an object has a name. Specifically, there appear to be assumptions on the part of the learner than can significantly alter the course of learning, speeding it enormously and rendering it more accurate. Because such assumptions-called constraints or himrs-would have powerful effects on learning, investigators have naturally sought to discover what kinds of assumptions exist, how they guide learning, and how they might change over development (see, e.g., Carey, 1994; Keil, 1994; Markman, 1994; Waxman, 1994). Entwined with the search for such biases has been a separate (though related) issue concerning the role of perception in developing and sustaining principles of generalization about object names. A tension has existed in the developmental literature between those who argue against a reliance on perception or action as the foundation for object naming and category inferences, and those who argue in favor of a definite role for perception. For example, a number of investigators concerned with Quine’s problem have suggested that children never utilize an unbiased (perceptual) similarity space as the basis for inductions; rather, they argue that knowledge of basic ontological kinds constrains any inductions that the child might make (Soja, Carey, & Spelke, 1991, 1992). As another example, a large body of empirical work has been designed to argue that children do not rely on perceptual similarity in making inductions about novel object properties (e.g., Gelman & Markman, 1986). In these approaches, the investigators implicitly seem to assume that perception is inherently unbiased, and that any biases must therefore come from sources other than perception. However, in defense of perception, other investigators have argued that perception itself is never unbiased, that it is flexible and “intelligent,” and even that it serves as an essential tool in the very

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construction of higher-level knowledge (Smith & Heise, 1992; Jones & Smith, 1993). In this chapter, I hope to reconcile these two themes-the need for constraints on the development of object naming and the role of perception in doing so-and suggest that a strong role for perception does not vitiate a role for other constraints, nor vice versa. Instead, I will argue that the perceptual system, especially object perception, serves as a vital force in early development, guiding initial mappings between the different levels of representation involved in object naming. 1 will suggest that children’s early object naming is supported by an object recognition system whose richness has been overlooked by many students of language learning. Perhaps more controversially, I will suggest that the design of this system entails representation of object properties that are both significant and reliable guides in the task of object naming. Of chief importance here is object shape, which appears t o dominate other object properties early in development, which may never become completely subsumed in later life by higher-level knowledge, and which may reliably form a foundation for other tasks such as inferences about novel category members. By focusing on the role of object shape in naming, 1 do not mean to claim that object perception (or specifically, object shape) is the only system supporting object naming. Rather, I will argue that there exist those from the beginning different levels of representation-including traditionally thought of as ”perceptual” and those traditionally thought of as “non-perceptual”-and that each of these plays a role in constraining induction. In the sections that follow, 1 will first describe a basic pattern that has been uncovered that suggests strong reliance on object shape among children and adults in the specific context of object naming. Following this, I will explore several effects that help in understanding both the strengths and limitations of object shape in the development of object naming. These include effects of varying syntactic context, varying perceptual information about object shape, and varying higher-level knowledge such as taxonomic or functional knowledge. For each of these, my focus will be on studies in which children and adults are asked to generalize a novel name from a standard to new objects-a case related to the Quinean problem of induction previously described. In a final section. I will propose a specific role and course of development for object shape in object naming, focussing on why and how shape is important. I will also suggest its relevance for the second induction problem described earlier-how learners come to make inferences about new properties of category members.

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11.

Object Shape and Object Name: Basic Patterns of Development

In a variety of studies, we have investigated the role of object shape in young children’s object naming (Landau, Smith, 8~Jones, 1988).Our work has been framed by Quine’s problem of indeterminacy, and our interest has been the role of different perceptual properties in the specific task of object naming. Quine’s position, at least in some writings, was that the child comes to the task of language learning with no a priori assumptions about the relative roles of different perceptual properties.’ Quine proposed that the perceptual similarity space in young children is “unbiased”-that there are no special principles determining which of the many observable properties will guide learning in one ontological domain compared with any other. For example, the Quinean child learning the word bottle would see no distinction between a construal of the word as referring to an individual countable object versus a (nonindividuated) portion of experience-“bottle-stuff.” According to Quine, the child only comes to appreciate such distinctions as he or she learns language, a formal system that naturally encodes distinctions such as those embodied by count versus mass nouns. Such a radical hypothesis is not likely to be true. Soja et al. (1991) showed that young children make different kinds of inferences about the quantificational properties of objects versus substances, suggesting that different similarity metrics underlie inferences for the two ontological kinds. Specifically, shape and numerosity matter in children’s generalizations about objects but not substances. However, even assuming that children possess representations of objects, there remains more to the Quinean problem for the word learner. The problem in mapping object names to objects is determining how to generalize the name to new objects never encountered before. Our own question was which of the many properties present to the preceiver would play the most primary role in generalizing the object’s name. A number

’

Quine shifted his position somewhat in some writings. For example. Quine (1973) did not reject the idea that there existed distinctions between kinds, like object and mass, prior to language learning that presumably could guide learning: Another difference between Mama and the masses is that the masses are amorphous. The similarity basis of the mass term “sugar,” or “water,” or “red” has nothing to do with shape. On the other hand the similarity that links the various presentations of Mama is very much a matter of shape. (p. 53) See Landau, Jones. and Smith (1992) for further discussion.

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of approaches to this problem have emphasized that young children generalize an object’s name to other objects of “like kind” (Markman, 1989), and that “like kind” can be conveyed through an object’s name (Gelman & Markman, 1986). Experiments have shown that children will generalize novel properties to a new object on the basis of its name. For example, if told that a dolphin “pops above the water to breathe,” children will assume that other animals called “dolphin” also pop above the water to breathe. This evidence suggests that like kind can be conveyed through the object’s name itself. However, labeling information surely has its limits: children who hear an animal and a wooden object both labeled “bat” are unlikely to collapse the two together as examples of a single category (Peters & Zaidel, 1980; Landau & Gleitman, 1985). Furthermore, studies have indicated that, for very young children, appearance is important in guiding inferences about novel object properties (Davidson & Gelman, 1990). This suggests an important role for what the object looks like, as well as what it is called. Do perceptual properties guide young children’s object naming‘? And if so, is there a hierarchy such that certain properties play a special role? Landau et al. (1988) addressed this question by considering the relative roles of shape, size, and surface texture. In a first experiment, we showed 2- and 3-year-old children and adults a small novel object made of wood, such as the “standards” shown in Fig. 1 . We labeled this object with a count noun (e.g., “See this‘? This is a dax”). Then, we set the object aside in front of the child and brought out a range of test objects one at a time, each time asking the child “Is this a dax’?” The test objects varied either in shape, texture, or size from the standard, with different objects varying in minor, moderate, or extreme magnitudes along these dirnensions (see Fig. 1). The results showed that of the three dimensions, shape was given greater priority than size or texture in extensions of the novel count noun to new objects. For example, subjects tended to say “Yes, that’s a dax” to objects that were the same shape as the standard “dax” even though they were very different from it in size or texture. They tended to reject objects that were the same size or texture as the standard if it was a different shape. This pattern of performance was weak in 2-year-olds, stronger in 3-yearolds, and extremely strong in adults (see Fig. 2 for a schematization of this developmental profile). In detail, when tested with either minor or moderate variations on the standard, 2-year-olds accepted same-shape objects (with different texture o r size) 92% of the time, whereas they accepted different-shape objects (with the same size or texture) only 78% of the time. When tested with minor, moderate, or extreme variations, 3-year-olds accepted same-shape

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Is this a dax?

Shape Changes

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Fig. 1. Object sets used in naming study (Landau. Smith, 8r Jones, 1988). Each set included a single standard (shown at top). Test objects included a replica of the standard plus three shape changes, three texture changes. and three size changes. Subjects were shown the standard and heard it labeled, e.g., "This is a dax/rif." With the standard still in view, they then were asked to judge whether each of the test objects was also a "dax"

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Standard

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Fig. 2. Developmental profile of the shape bias. The four boxes illustrate the increasing strength of the shape bias under the conditions described in Landau et al. (1988). that is, when a rigid object is introduced with a novel count noun. Choice of objects with the same shape as the standard (with different texture or size) is plotted along the ordinate; choice of same-texture or same-size objects (with a different shape) is plotted along the abcissa. Areas above the diagonal show a shape bias (greater choice of same-shape than same-texture or same-size objects): areas below the diagnonal represent a texture or size bias. The bias for same-shape objects is present in weak form in 2-year-olds and it becomes stronger over age. culminating in an extremely strong bias among adults.

objects 95% of the time, whereas they accepted different-shape objects only 60% of the time. Adults accepted same-shape objects 100% of the time, whereas they rejected objects of a different shape close to 90% of the time. In a separate experiment, 5-year-olds showed a bias almost as strong as that of adults (Landau, Smith, & Jones, 1992). These findings are compatible with results from object recognition studies, which show that shape-based elements are critical to time-constrained recognition, but surface properties such as color are not (see, e.g., Biederman & Ju, 1988). or a "rif." Two-year-olds, 3-year-olds. 5-year-olds, and adults all showed a bias to accept objects having the same shape as the standard regardless of texture or size changes. and to reject objects having a shape different from the standard. even though both texture and size were preserved. This "shape bias" is highly context-dependent. however, varying with the syntactic context used and with the perceptual properties of the test objects.

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Why should shape be primary in this context? One possibility is that shape is dominant in all contexts where similarity is the basis for grouping. That is, regardless of the kind of generalization task, the child might be completely “shape-bound”-a specific version of being more generally “perception-bound” (Bruner, Olver, & Greenfield, 1966; Inhelder & Piaget, 1964). For adults, perceptual similarity-or indeed any kind of similarity-is entirely relative; similarity judgments vary depending on context and task (Shepard, 1964; Nosofsky, 1984; see Smith & Heise, 1992, and Medin, Goldstone. & Gentner, 1993, for more recent discussion). This raises the question of whether children’s judgments of perceptual similarity are also context-dependent, and if so, whether there is a particular similarity metric-perhaps shape-based-that is specially relevant for object naming. In exploring the parameters of the shape bias, we have consistently found that a generalized bias for shape does not exist across all contexts. Rather, the shape bias is highly context-dependent and the precise parameters of its variability are revealing. A first finding concerns the specificity of the shape bias in the context of object labeling. A variety of studies have shown that the principles guiding young children’s generalization of a novel word are qualitatively different from those guiding other kinds of comparisons. For example, when preschool children are asked to “put together the things that go together,” they often group objects on the basis of thematic relations; these are temporary relationships such as that between a dog and its bone (rather than between a cat and a dog) or a cup and some milk (rather than a cup and aglass) (lnhelder & Piaget, 1964).Yet Markman and Hutchinson (1984) found that this thematic bias was reduced when objects were given a novel name, that is, when the same-aged children were introduced to an object and told “This is afep,” then were asked to “Show me another fep.” In this context, children preferred to group on a taxonomic basis, for example, grouping together the dog with a cat, rather than with a bone. Although the exact basis for taxonomic responding is a difficult issue (see Sections V and VI), Markrnan and Hutchinson’s finding illustrates two important facts. One is that judgments of similarity in children largely depend on the particular “grouping” or categorization question that is posed. The second is that novel object names offer an especially precise barometer for a particular kind of similarity-that underlying object naming. The presence of an object name guides children to generalize on the basis of certain kinds of properties and relationships and not others. Given this, Landau et al. (1988) asked whether children would show a shape bias in a No-Lube1 task similar to the one that elicits a thematic

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bias. Following Markman and Hutchinson, we presented children and adults with the same standards and test objects as in the original experiment. In one condition, we showed children the standard, labeled it (“This is a dax”), and then showed children pairs of test objects (different shape, size, or texture), asking “Which one is also a dax?” In another condition, we showed children the standard, but did not label it. Rather, we showed children the same pairs of test objects and asked “Which one goes with (the standard)?” In this No-Label context, children showed either a reliably weaker shape bias (3-year-olds) or no shape bias at all (2-year-olds). For example, whereas 3-year-olds in the Label task chose same-shape objects on 81% of the test trials, they did so only on 63% of the trials of the NoLabel task. Two-year-olds in the Label task chose same-shape objects on 71% of the trials, whereas they did so on only 56% of the trials in the NoLabel task (see Fig. 3). This weakening or absence of a shape bias in the No-Label context has been found by a number of other investigators (Baldwin, 1992; Imai, Gentner, & Uchida, 1994; Poulin-Dubois, Klein, Graham, & Frank, 1993), including one cross-linguistic comparison among children whose native languages were Greek, Chinese (Cantonese), or English (Bavin, Ng, Brimmell, & Gabriel, 1993). Thus, the shape bias Label

No Label

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Fig. 3. Comparison of results for the Label and No-Label conditions among 2- and 3year-olds (Landau et al., 1988). There is a greater shape bias when the novel object is labeled (e.g.. “This is a dax”) and subjects are asked “Which one is a dax?” than when the novel object is not labeled and subjects are asked “Which one goes with this (the standard)?” As in Fig. 2, the shape bias in the Label condition increases between 2 and 3 years of age.

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appears to emerge most reliably in the context of object naming, and does not appear to reflect a generalized preference for grouping objects on the basis of shape. Further, this tendency may be universal.

111. Syntactic Effects and the Shape Bias

If the shape bias is strictly linked to object naming, there should be a particular mapping between the bias and different syntactic contexts. Because objects typically are named by count nouns (and specifically, in answer to the question What is that?; see Shipley, 1993), the shape bias should emerge in the context of count nouns (that label objects), and should be weak or should disappear entirely in other syntactic contexts-ones in which objects are not typically named. Further, the shape bias should appear in the count noun context even if other perceptual properties of the object are more salient in nonlabeling contexts. These predictions have been confirmed. In one set of experiments, the original set of objects from Landau et al. (1988) were used, but 3-yearolds, 5-year-olds, and adults were shown the standard as it was introduced in a context appropriate for superordinates (“This is a kind of dux.”; Landau, Smith, & Jones, 1992). Whereas 3-year-olds, 5-year-olds, and adults all had shown a (increasingly strong) shape bias in the simple count noun context, the “kind of” context elicited a weaker shape bias. This makes sense, as objects described at the superordinate level are typically more diverse in shape than are those at the basic level. The differences across these linguistic contexts are shown in Fig. 4 as plots of individual subjects’ choices of same-shape versus same-texture objects. Individuals with a shape bias fall above the diagonal, saying yes to more same-shape objects than same-texture objects. Those who have an extremely strong shape bias fall in the upper left-hand corner. For example, the adults hearing “This is a dax” say yes to all same-shape objects, but to none of the same-texture objects (which have different shapes). Note that there is stronger clustering of individuals in the count noun context “This is a dax” than in the context “This is a kind of dax,” and the differentiation between the two syntactic contexts increases over development. In a second experiment of this series, subjects were shown heavily textured objects to determine whether a highly salient texture might alter the shape bias. In this experiment, one half of the subjects heard the standard introduced in a count noun context” (“This is a dax”) and the other half heard it introduced in an adjectival context (“This is a duxy one” or “This is a dux one”). Individual plots are shown in Fig. 4 (bottom

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Fig. 4. Individual subjects’ acceptance of same-shape versus same-texture objects across different linguistic contexts (Landau et al.. 1992). Subjects who show a shape bias accept same-shape objects (with different textures) and reject same-texture objects (with different shapes), placing them above the diagonal. Subjects who show a texture bias accept sametexture objects and reject same-shape objects, placing them below the diagonal. The top panels contrast individuals who were 5hown the standard and were told “This i s a dax” (open circles) with those who were told “This is a kirid ofdax” (filled circles). Subjects tend to show a stronger shape bias in the former condition, and the difference between conditions increases over age. The bottom panels contrast individuals who were shown a heavily textured standard and were told “This is a dax” (open circles) with those who were shown the same standard and were told “This is a duxy one’’ (filled circles). Sub.jects in the count noun condition again show a shape bias, whereas 5-year-olds and adults in the adjective condition show a texture bias.

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panels). Three-year-olds in both contexts showed a shape bias, with most individuals falling above the diagonal, but 5-year-olds and adults now showed a “texture bias,” accepting more objects with the same texture but a different shape, hence falling below the diagonal. In these experiments, 3-year-olds showed little effect of syntax; for example, they did not systematically switch to surface texture in the adjective context, although 5-year-olds did so quite strongly. Further studies indicate that 3-year-olds can be induced to attend to texture in the adjective context if the perceptual salience of the texture is enhanced sufficiently. Smith, Jones, and Landau (1992) manipulated the saliance of surface texture relative to shape by first empirically testing children’s grouping preference in a No-Label task. Three-year-olds were shown an object possessing a novel shape and a sparkling, glittery surface. When asked to judge which other objects “go with” that standard, children chose different-shape objects with the same glittery surface as the standard, showing that the glittery surface of the object was more salient than its shape. A separate group of 3-year-olds were shown the same standard and test objects, but were told either “This is a dax” (count noun) or “This is a dax one” (adjective). In the count noun context, children generalized on the basis of shape-the less “salient” property in the NoLabel context. In the adjective context, children showed a weak shape bias, but, as in Landau, Smith, & Jones (19921, no systematic switch to surface texture. However, when the same glittery objects were placed under illumination, children who heard “This is a dax one” did generalize on the basis of surface texture. This suggests that syntactic context can be effective in modulating generalization in younger children, when it combines with perceptual salience. Thus, the shape bias emerges reliably and systematically in the count noun context-the context of object naming-from the age of 2 (and perhaps even earlier; see Jones, Smith, Landau, & Gershkoff-Stowe, 1992).The shape bias becomes stronger by age 3 in the count noun context, the same age as other syntactic contexts begin to modulate attention to other properties. For example, in the adjective context, 3-year-olds tend to show weaker shape-based generalizations or more diverse attention to other properties, but they do not yet show a systematic switch to surface texture unless that texture is made extremely salient (see also Au & Markman, 1987, for related findings). The syntactic effects described so far suggest that, with development, children’s generalizations increasingly come to be guided by syntactic context, with modulation of which perceptual properties are most “salient” directly affected by changes in syntactic context. But how is this development initiated? One possibility is that the pairing of a count noun

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and rigid object might serve as an initial “canonical” pair that strongly elicits attention to object shape; development would then be a case of learning that count nouns can also describe nonrigid entities (e.g., a “pile” or “stack” of wood chips, a “blob” of glue) and that mass nouns can also describe the substance of which a rigid object is made. Such a canonical pairing should result in a strong shape bias for rigid objects named by count nouns, less of a shape bias (or none at all) for entities such as blobs of glue, mounds of sugar, and so on, and these, too, !should increasingly be subject to syntactic modulation over development. Several studies provide evidence that the combination of perceiving a rigid object and hearing count noun syntax gives rise to a strong bias, that dilution of either of these weakens the bias, and that variations in syntactic context become increasingly strong modulators of interpretation over development. Soja et al. (1991) asked whether 2-year-olds would show different kinds of generalizations for displays perceived primarily as rigid objects versus nonrigid materials. Children were shown either an“object” display (a rigid metal object) or a “substance” display (a blob of cream) and were told “This is my dax” (using syntax that is neutral between a count and mass noun reading). Then, they were given a choice between two test displays and asked “Is this my dax?” One display was a single object with the same shape as the standard but composed of a different rigid material; the other was several items possessing different shapes from the standard but composed of the same material. Soja et al. found that children’s generalizations differed depending on which standard display they viewed. When they viewed the object display, children generalized on the basis of shape and numerosity, choosing a single object of the same shape as the standard (but different material). When they viewed the substance display, they chose the same-shape test object much less often. These results show that learners are biased to use different principles of generalization for different perceptual displays, independent of syntactic context, and were interpreted by Soja et al. as evidence for knowledge of two different ontological kinds (object and substance) prior to the acquisition of relevant syntactic forms. In these studies, the same results emerged when the displays were introduced with syntax that was compatible with the perceptual reading: count noun syntax (“This is a dax”) for the object display and mass noun syntax (“This is some dax”) for the substance display. That is, children’s perceptual and/ or conceptual interpretations were determinate in practice, despite the fact that, logically, one could construe the object display as a mass of a given substance, or the substance display as an object with a particular (somewhat amorphous) structure.

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Following up on these findings, Subrahmanyam (1993) conducted a detailed study of the development of generalization patterns obtained when different kinds of standards-roughly representing “good” cases of object versus substance-were introduced using either count or mass noun syntax. Three- and 5-year-olds and adults were shown either a rigid wooden object or a continuous stream of glue being squirted out into a simple pattern such as a curved U-shape; these displays were introduced using a novel word with either count or mass noun syntax (i.e., “This is a dax” vs. “This is some dax”). Subrahmanyam found that the syntactic contexts strongly modulated the generalization patterns of 5-year-olds and adults: Whether they viewed the rigid object or the stream of glue, these subjects tended to generalize on the basis of shape in the count noun context, but substance in the mass noun context. For example, even if they observed a rigid object, the sentence “This is some dax” led them to generalize on the basis of the object’s substance. And even if they observed a loopy stream of glue, the sentence “This is a dax” led at least some of them to generalize on the basis of the loopy shape. This kind of modulation was observed only weakly among 3-year-olds, whose generalizations appeared to be strongly guided by the perceptual force of the standard, with more minor effects of syntactic context. When they viewed a rigid object, these children generalized on the basis of shape and this was especially pronounced in the count noun context. When they viewed a stream of glue, they generalized on the basis of either shape or substance in the count noun context (apparently guided by either the perceptual or the syntactic context), but they did not generalize on the basis of shape alone in the mass noun context (see also Soja, 1992, for similar results with 2-1 /2-year-olds.) Thus, both Soja and Subrahmanyam found a strong coupling of rigid object and shape early in development, such that the “canonical” pairing of count noun and rigid object gave rise to the strongest shape bias at all ages. Subrahmanyam also found that syntactic context had an increasingly strong effect, with 5-year-olds (and adults) able to construe flexibly a novel word as either referring to the object or the substance, depending on whether it was introduced as a count or mass noun. The overall pattern suggests that the shape bias emerges reliably by about age 2, particularly in the context of rigid objects that are named by count nouns. This is most likely because perceptual and syntactic information about object-ness converge: Both observing a rigid object and hearing it introduced with a count noun suggest that the speaker is indicating the object itself. Children then generalize on the basis of the object’s shape. With development, this strong dominance of the object becomes modulated to a greater and greater extent: Adults and 5-year-olds, but

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not 3-year-olds, are able to observe a rigid object, hear it labeled with a mass noun “This is some dax,” and infer that the word dux must describe the substance from which the object is made. But this is a later development. The coupling of rigid object, count noun, and shape comes first.

IV.

Perceptual Effects: Information about Shape and the Shape Bias

At this point, it is critical to examine more closely what we could mean by the proposal that children generalize new object names on the basis of object shape. Depending on one’s notion of shape and one’s notion of the basis for object naming, the very idea of generalizing on the basis of shape-or on any other perceptual property-might seem quite wrongheaded. After all, the things we call dog or chair can be quite different in shape and, in any case, what makes something a member of the category “dog” or “chair” is surely more than their similarity in shape. Addressing these issues requires us to confront a number of intricate and subtle questions concerning what it means to have the same shape and what role same shape could play in supporting object naming. In this section, I examine the first of these issues: what it means for two objects to have the same shape. Admittedly, making good on the claim that object shape plays a role in the development of object naming ultimately will require an independent theoretical description of “same shape.” Currently, one promising approach to shape equivalence is the componential volumetric one (Binford, 1971; Marr, 1982; Lowe, 1985; Biederman, 1987). Within this general approach, some investigators have shown shape equivalence of volumetric equivalents over parameters such as object size, position, and orientation (e.g., Hummel & Biederman, 19921, whereas others have considered parameters necessary to account for changes in realized configuration over motion (Marr & Vaina, 1982). Generally, however, the question of what constitutes two shape-equivalent objects is still in its infancy. In our own work, we have focused on the question of just how rich and flexible (or impoverished and inflexible) are the object representations underlying children’s early object naming. Traditionally, developmental theorists have viewed children’s perceptions as relatively impoverished and inflexible, something to “go beyond” as one gains deeper knowledge of the world (see Smith & Heise, 1992, for review and comment on this tradition). Our studies of these early object representations suggest to us something quite different, however.

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Our central question has concerned the range of shape transformations that objects can undergo and still be considered a member of the same named object category. This question actually has two prongs, each of which invites investigations of a somewhat different sort. One prong concerns shape equivalence for a single object: What is the range of shape transformations that any individual object can undergo and still be considered that object, say, the different momentary views of agiven dog or cup?* The answer to this question is a necessary but not sufficient component of the second prong of the question, concerning shape equivalence for a set of objects: What is the range of shape transformations characterizing the many different objects that are members of the same named category, say, all the things called “dog” or “cup.” In either case, it is clear that if shape is to be all diagnostic for naming, then what constitutes a sameshaped object will have to be something more abstract than the particular two-dimensional (2D) image or three-dimensional (3D) configuration obtaining from the momentary position of a given object viewed from a given perspective. Consider, first, shape equivalence for objects that can undergo changes in posture or configuration over time. Although rigid objects (especially artifacts) do not move and often are not deformable over time, there exist many objects that undergo changes in configuration as they move through space, as their parts undergo motion, or as they undergo global deformations due to malleability. The same individual dog undergoes changes in configuration as it walks, runs, jumps, or wags its tail, but it is still identified as a dog. A hinged trunk might undergo changes in its static configuration as one raises or lowers (or removes) its lid, but it is still identified as a trunk. Candles drip when heated and sponges can expand or contract. What is interesting about all of these cases, however, is that when an object is locally rigid there always exist constraints on some parameters of the shapes it can obtain under transformation (see, e.g., Leyton, 1992). An important question, therefore, is whether “same-shape’’ for children is confined to single momentary views of an object configuration, or whether young children can make systematic and principled predictions

’

Different momentary views of an object could pertain to the varying two-dimensional retinal patterns arising as viewer and object move through space (as in the case of viewing a cup from different perspectives), and/or it could pertain to the varying three-dimensional configurations arising as individual object parts move relative to each other (as in the case of a dog wagging its tail). Shape equivalence for the former requires shape constancy, which infants possess (see, e g , Slater & Morison. 1991). Shape equivalence for the latter would seem to engage a spatial inference system, which could support predictions of what constitutes legitimate and nonlegitimate shape transformations. This type of shape equivalence is one focus of our current research.

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about the range and type of shape transformations a given object is likely to undergo. If the shape bias were linked only to the static momentary image or configuration of an object, then it would be of little use, for children would misclassify new 2D images and 3D configurations of the same object as different objects. But if young children can make principled and accurate inferences-predictions about the momentary configuration an object might obtain under different circumstances-then the story would be quite different. To examine more closely this aspect of children’s shape bias, we have been investigating whether young children are sensitive to the range of transformations an object can undergo and still be considered a member of the same named category. We have examined this issue with both 2D representations of objects (line drawings) and with 3D objects that can vary in their configuration due to motion among their parts. A.

TWO-DIMENSIONAL REPRESENTATIONS OF OBJECTS: INFERENCES ABOUT CHANGES OF CONFIGURATION DUE TO MALLEABILITY

In one set of experiments, we focused on the kinds of edge-based (contour) information that might guide children’s judgments of which objects can or cannot fall into the same named category (Landau, Leyton, Lynch, & Moore, 1994). We organized our inquiry around a general problem found throughout the object recognition literature: the ability of the visual system to recover objects in the real world from the infinitely ambiguous retinal image. The solution to this problem is considered to depend on constraints within the visual system that lead to interpretation of the retinal image in terms of properties likely to be true in the real world. This process has been described in terms of the system’s tendency to register “nonaccidental” properties-those that do not occur through accidents of viewing position (Lowe, 1985)-or, more broadly, the principle of “genericity,” by which the visual system assumes a generic viewing position (see Albert & Hoffman, 1993). Related to this, the Asymmetry Principle (Leyton, 1992)states that perception of shape is ulwnys recovery of the shape’s history, either through interpretation of the retinal image as that object which could have produced the image, or through interpretation of 3D objects in terms of the causal processes that might have created the object’s present shape. The basic idea across these notions is that the visual system assumes that properties of the image are not due to momentary accidents of viewing, but rather, that the properties bear a principled relationship to the properties of the real object giving rise to a given image. For example, the

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visual system is constrained to assume that straight lines in the image will correspond to straight lines in the world, and that curved lines in the image correspond to curved lines in the world. Straight versus curved lines, as well as other properties such as symmetry and parallelism, are considered “nonaccidental” properties (Lowe, 1985); these have formed the foundation for modern volumetric approaches to visual object recognition (see, e.g., Biederman, 1987). Similarly, by assuming ageneric position of objects relative to each other, the visual system seems to interpret a sharp concave crease in the surface of an object as a likely location for an intersection of two of the object’s parts (Albert & Hoffman, 1993; Hoffman & Richards, 1984). Thus the visual system, behaves as if the properties of 2D images bear a principled “truthful” relationship to the properties of the 3D objects they represent. Do such principled relationships enter into young children’s object naming? More specifically, can young children make use of such nonaccidental properties in determining what range of shape transformations will be relevant to a named category? In the first experiment of our series, we created 2D stimuli (black and white drawings) that possessed many of the contour properties of the 3D stimuli we had used in the original shape bias studies (see Fig. 5). In particular, the original “dax” standard was converted to a 2D outline drawing complete with straight edges and sharp corners. For a second set, the standard (called a “rif ”) possessed similar contour properties. As a whole, these outline objects have several important features: They possess straight, parallel lines and sharp creases. Straightness and parallelism are both nonaccidental properties, that is, they are likely to represent straight, parallel edges in the real world. There are also sharp part boundaries corresponding to the sharp creases on the inner edge of the object; these are discontinuities such as would be produced by the intersection of two objects (Hoffman & Richards, 1984). Our question was whether children’s acceptance of shape transformations would be modulated by varying these nonaccidental properties. In the experiment, 3-year-olds and adults were shown the standard, heard it named, and then saw three shape and three size transformations of the standard, including one minor, one moderate, and one extreme transformation o n each dimension (see Fig. 5 for shape transformations of the “dax” set). Each time subjects viewed a transformation, they were asked “Is this a dax?” We predicted a replication of the original findings, that is, a strong shape bias, including ceiling acceptance of size transformations together with strong rejection of shape transformations. Figure 6 shows what we found, with no developmental differences between children and adults.

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Fig. 5 . An object set used in studies of the role of nonaccidental properties in modulating the shape bias (Landau, Leyton. Lynch & Moore, 1994). Three-year-olds and adults were shown a single line-drawn standard (roughly 2 in. on a side) and heard it labeled, for example, “This is a dax.” They were then shown three shape transformations (as shown) and three size transformations one at a time and were asked for each one, “Is this a dax?” In Experiment I , the standard and test ob.jects possessed straight edges, sharp corners, and deep concavities. In Experiment 2. the edges were curved. In Experiment 3 . the edges were both curved and extremely irregular (“wrinkled”). In Experiment 4,eyes were drawn onto the figures.

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v)

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Fig. 6. Results of the shape transformations studied in Landau, Leyton, Lynch, and Moore, 1994 (see Fig. 5 for stimuli). Both children and adults accepted few shape transformations when they viewed a standard with straight edges and sharp corners, reliably more when they viewed one with either curved or curved and wrinkled edges, and almost all shape transformations when they viewed the standard with curves, wrinkles, and eyes. The results point to important effects of specific elements of object shape, especially nonaccidental properties of the contours.

In a second experiment, we used the same shape sets, but this time we replaced the straight edges and sharp creases with curved contours (see Fig. 5). Curvilinearity, like straightness, is a nonaccidental property; therefore, curved lines are likely to represent curved contours in the real world. Another consequence of curving the contours is substantial weakening of the part boundaries (Hoffman & Singh, in preparation). Note that the curved version of the original “dax” appears to be more a one-part, tubelike object. When a second group of subjects was tested on these curved drawings, the outcome was dramatically different from the first experiment. With the curved sets, 3-year-olds and adults again accepted all size transformations at ceiling level; but this time, they were much more willing to accept the shape transformations (see Fig. 6). In other words, subjects’ shape bias for a curved standard was substantially weakened from that elicited by the straight-edged standard. Virtually identical results were found in a third experiment in which we produced figures with a massive set of irregular contours that would correspond to irregular surfaces, such as those produced by “wrinkles” in the real world (see Figs. 5 and 6). In a final experiment, we attempted to modulate subjects’ responses even further by introducing a single significant feature-eyes-that

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strongly suggests animacy (see Fig. 5). Jones, Smith, and Landau (1991) showed that the shape bias was significantly altered when eyes were added to wooden objects; specifically, children generalized to objects of the same shape and surface texture, as if texture also matters for animate objects. When shown the curved, wrinkled standard with eyes in the present series, both 3-year-olds and adults generalized to all size and shape transformations, as if all the drawings observed were readily interpreted as “legitimate” transformations of the named standard (Fig. 6). Note that this interpretation suggests that both children and adults should reject “nonlegitimate” transformations of the standard, even when the standard possesses curves, wrinkles, and eyes. In a follow-up study, we have confirmed this prediction: When subjects viewed the curved, wrinkled standard with eyes, they generalized to all the “tubelike” objects as shown in Fig. 5, but they did not generalize to radical topological transformations of the standard. for example, an irregular, rounded shape with two protrusions. These results provide strong evidence that both children and adults generalize a novel object’s name to other objects whose momentary configurations can be related to the standard on principled perceptual grounds. It appears that the perceptual system underlying children’s and adults’ shape bias is well-suited to generating accurate judgments about the range of transformations that are possible for objects possessing different shapebased properties such as straight versus curved contours.

B. THREE-DIMENSIONAL OBJECTS: INFERENCES ABOUT CHANGES OF CONFIGURATION DUETO RIGID MECHANICAL MOTION In a separate series of studies, we have examined what children know about the range of transformations that 3D objects can undergo. Recall that in the original shape-bias studies, the standards were either straightedged or rounded, but were rigid objects made of a single piece of solid wood (see Fig. 1). One might suspect, then, that subjects’ shape bias might be the product of certain intelligent predictions. For example, subjects might predict that the standard objects would not be likely to undergo deformations such as bending (because wood is rigid), nor mechanical transformations such as motion at joints (because the objects were in one piece and did not have clearly marked joints). In fact, close examination of the original standards reveals that the shape changes in each set could be produced in principle by different kinds of transformations: In the dax set, the shape changes could result from either bending or rotating one of the legs into a new position. Either

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of these transformations would be unnatural for solid wooden objects, and this could be the source of a strong shape bias for the dax set. In the rif set, the shape changes could result from cutting (or sawing)-each of the shape changes is essentially missing an extra chunk of wood. Although we never found a difference between sets in the original experiments (nor in any others using novel objects), Bavin et al. (1993) conducted a close replication of Landau et al. (1988) and found a reliably stronger shape bias for the dax than the rif set. This could have resulted from the differences between the two sets in what kinds of transformations are possible for each set. Are young children sensitive to the consequences of different kinds of mechanical transformations? We have begun to ask this question by focusing on mechanical transformations produced by motion at an object’s joints. In one experiment, we compared children’s and adults’ pattern of performance under two different conditions, each condition suggesting different potential for shape transformations. In the No-Motion condition, subjects viewed a “standard” object composed of several parts: each of the parts was glued in place, as shown in the example in Fig. 7. The object was labeled, for example, “This is a dax,” and subjects were then shown a range of different-shaped objects; each of the shape changes could have arisen from moving the parts at their joints. In each of these shape transformations, the parts of the standard had been rearranged, but all parts were glued into place. In a second condition-the Motion condition-subjects were again shown a standard composed of the same parts in the same configuration. The only difference was that the new standard possessed metal hinges, visible at the joints, between each part and the body, and the parts were now movable. The standard was labeled, and as it was labeled, we moved one of the parts a small amount, returning it afterward to its original position. Subjects were then shown the test objects, which were identical to those for the first condition except for the addition of hinges. As in the No-Motion condition, the parts of the test objects were glued into place rendering them immobile: therefore, although subjects viewed the standard undergoing a small motion, they never viewed any of the test objects undergoing motion. Results showed massive differences in the range of shape changes accepted as members of the named category (see Fig. 8). Both 3-year-olds and adults accepted many more shape transformations when they saw the standard with hinges and observed one part being moved. Although there were no developmental differences between 3-year-olds and adults in this comparison, preliminary results suggest that the hinges by themselves may have played a role only for adults. When new groups of subjects

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Fig. 7. An object set used in studies of the effects of mechanical motion on the shape bias. In the No-Motion condition. 3-year-olds and adults were shown a standard and heard ir labeled. “For example, “This i s a dax.” The standard possessed several parts that were glued together to prevent motion (see upper panel). I n the Motion condition. subjects viewed a standard with the same parts: however. the upper parts were attached to the body with hinges. When the object was labeled. subjects saw one part being moved approximately 40 degrees. Subjects in both conditions were then shown three test objects (see bottom panel) and were asked whether each was a ”dax.” Each of the test objects possessed the same three parts as the standard, glued into configurations that could arise from moving the parts at their joints.

were tested in a No-Motion condition with hinges present on the standard and test objects, only adults accepted a wide range of shape transformations. Three-year-olds accepted shape changes at about the same level as they had in the No-Motion condition without hinges. In other words, the adults appeared to have specific knowledge of hinges that suggested that hinged objects can undergo changes in configuration; viewing the hinges was sufficient to promote such inferences. In contrast, children appeared to rely primarily on the information provided by motion of the parts relative to each other: Hinges by themselves (without motion) did not promote the inference that the objects could undergo changes of configuration. This finding is especially intriguing in view of the possibledifference between the roles of perceptual-based knowledge and specific knowledge possibly gained through other avenues (see Section VI for discussion).

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100

-

Non-Moving 80

60

40

20

0

Fig. 8. Results of the No-Motion versus Motion experiments. Both 3-year-olds and adults accepted a reliably greater number of shape changes in the Motion condition than in the No-Motion condition, suggesting that the system underlying the shape bias can support inferences about transformations following from mechanical motions.

To summarize, young children appear to be sensitive to the range of transformations possible for objects having moving parts. This sensitivity underlies the modulation in generalization to “same-shape” objects, and hence provides yet more evidence for the intelligence of the learners’ putative shape bias. Although our studies of such sensitivity are just beginning, we anticipate that we will discover that young children’s judgments of what constitutes shape-equivalent objects will be differentially affected by perception and knowledge of different kinds. On the perceptual side, children might be expected to correctly predict outcomes, for example, of the differential effects of transforming rigid versus nonrigid substances, bending versus melting, accidental versus intentional changes, and so forth. On the side of knowledge of different kinds, Keil (1994) has already shown that the shape bias in children and adults can be significantly altered by introducing the very same (drawn) object as a “kind of animal” versus a “kind of rock.” In these studies, shaped-based generalizations emerge for animal kinds but not for rock kinds.3 The question of what role object shape plays in the generalization of names for different object kinds is crucial in understanding how general the shape bias is. Investigation of this question is just beginning; see Keil (1994) and Subrahmanyam (1993) for additional findings.

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Knowing which shape changes are the legitimate products of different transformations is a necessary step toward being able to use the shape bias to extend novel labels accurately. But shape-even when supported by a richly structured system-is not all there is to categorizing named objects, and one must ask what happens when other information relevant to object naming interacts with the same-shape rule.

V. Effects of Knowledge: The Role of Known Taxonomies and Functions The foregoing evidence suggests that young learners use object shape to decide which new objects are members of the same named category. The evidence also suggests that this “shape bias” is supported by a richly structured system that can generate accurate inferences about which objects are the same shape over different transformations. But is such a bias useful for the young learner or does it conflict with other, more significant bases for object naming, leading the learner down a garden path? On ecological grounds, a strong role for object shape might seem beneficial: Objects having the same shape in the real world often also have the same name. This correlation between shape and name has undoubtedly evolved in part because objects of the same shape are also often objects of the same “kind,” as determined by various criteria (both knowledgedriven and perception-driven) for membership in a given taxonomic category. In fact, the strong correlation between same shape and same kind could underly children’s judgments that objects with the same name will often share the same shape. However, it is not always the case that shape, kind, and name coincide, and some investigators have argued that the exceptions challenge the usefulness-even the existence-of a generalized shape bias in object naming. These exceptions must be evaluated before fully understanding the role that shape might play in early object naming. A.

SHAPEA N D ONTOLOGICAL K I N D :WHEN SAMESHAPE CUTSACROSSONTOLOGICAL CATEGORIES

Consider first whether objects with the same shape also share the same name. In the adult lexicon, there clearly are exceptions to this rule; for example, whales have roughly the same shape as fish, yet they are not considered by adults to be members of the same named category. This is because fish and whales are members of different kinds, as determined by various scientific criteria which are not necessarily obvious from initial

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visual inspection, for example, mechanisms of respiration or reproduction. Similarly, not all objects with the same name have the same shape, even under a generous interpretation of “same shape.” As Soja et al. (1992) have noted, the objects named by the count nouns telephone or clock are quite different in shape (e.g., compare the latest bank of airport phones to the Princess phone, circa 1960). Or consider the Pekinese and Great Dane: on some dimensions, the Pekinese looks more like most cats than like other dogs. But as with whales and fish, the Pekinese and Great Dane are both called “dog” because of commonalities other than their appearance. Recent arguments have focused on the importance of “like kind” as a principle of object categorization and naming. Investigators have suggested that young children come to learning knowing that objects with the same name are likely to be of like kind and that like kinds also share many properties, including those that are not easily observable (Markman & Hutchinson, 1984; Gelman, 1988). Because knowledge about which specific properties hold together a given natural category is based on a scientific theory, it is subject to revision, with experts usually considered to have the “final say” (see discussion by Putnam, 1977: Schwartz, 1977). But this revisability also means that knowledge of what it takes to be a member of a given category might vary considerably from individual to individual-and, critically, it might vary dramatically over development. The key idea, however, is that if children have a notion of like kind, then they will share with adults the beliefthat members of like kinds also share many properties. Further, if they believe that object names are labels for specific object kinds, then having a particular name will serve as a powerful guide to learning other properties: Hearing that something is called a dog licenses the belief that that thing-whatever its appearance-shares many important properties with other things called “dog.” Where does this leave the shape bias? If things of like kind always have the same name, and like kinds can share many properties whatever their differences in appearance, then this would seem to challenge the role that shape similarity could play in the development of object naming. At best, shape would be superfluous; at worst, a same-shape rule would be counterproductive, for the child must ultimately come to ignore the information on which she earlier relied. The strongest statement of this position, so far, has been made by Soja et al. (1992), who have suggested that children neuer use a same-shape rule, but rather, always generalize object names on the basis of like kind. We doubt that (same shape) captures the actual basis of the child’s inductions concerning what or how words are used to name. Rather, we suspect that from the very

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beginning a different generalization. which is also consistent with [Landau et al.’s] data, is correct: count nouns referring to objects pick out objects of the same kind. On our view, children and adults show a shape bias in the(ir) studies because shape is often (although not always) a good source of information about object kind. (p. 102)

In fact, this interpretation of the shape bias is not inconsistent with the interpretation given in Landau et al. (1988) and other related papers. Landau et al.’s original arguments about the importance of shape in early object naming drew directly on the idea that shape is an excellent predictor of whether or not objects in the real world do fall in the same named category (see Section VI for further discussion). Still, Soja et al.’s observations certainly challenge the strongest form of the shape bias hypothesis, specifically the idea that object shape-as a perceptual property disembodied from any mapping to other levels of representation-ever really underlies object naming among children. A number of experiments have addressed this issue by pitting shape against kind in a particularly direct way, with somewhat surprising results. In one set of experiments, Baldwin (1992) sought to discover whether the taxonomic constraint described by Markman ( 1 989) might actually reduce to a same-shape bias. Markman and Hutchinson (1984) had found that young children’s thematic bias (i.e., the tendency to group together thematically related items, such as a dog and a bone) was significantly reduced when the objects in question were named. Specifically, when children were shown an object and heard it named (e.g., “This is a fep”), they tended to avoid assigning the same label to another thematically related object, instead assigning the label (a “fep”) to another object that was “taxonomically related,” including objects in the same named basic level category (two dogs), the same named superordinate category (two animals, or “things that swim”), or objects that perform the same function (e.g., things that “poke holes”; see Markman, 1989, Ch. 2). As Baldwin points out, however, many objects of like kind are also similar in shape: Two dogs are more similar in shape to each other than are a dog and its bone; two cups are more similar in shape than are a cup and some milk, and so on. Therefore, the question arises whether children’s naming generalizations might be accounted for by their use of a same-shape rule, independent of any additional knowledge of what constitutes two objects of the “same kind.” Baldwin designed object triads in which a target object could be grouped together with another object that was either a thematic choice, a sameshape choice, or a taxonomic choice-the latter presumably reflecting like kind. For example, one target object was a flower, the thematic choice was a bee, the shape choice was a wheel, and the taxonomic choice a

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tree (see Fig. 9). As another example, the target object was a carrot, the thematic choice was a rabbit, the shape choice a rocket, and the taxonomic choice a squash (pumpkin). Note that in each case, the shape match is in a different taxonomic category from the target object; for example, flowers and wheels are in two distinct ontological categories (plants vs. artifacts) as are carrots and rockets. Thus if the child chooses a same-shape match in preference to a same-taxonomic match, this would indicate his or her willingness to cross category boundaries to group together like-shaped objects. Baldwin’s findings provide evidence for a strong role of shape in extending novel labels, even when some shape crosses usual category boundaries. In one experiment, 3- and 4-year-olds in a Label condition were shown the target object and heard it labeled with a novel label (e.g., “This is a zom”); in a No-Label condition, they saw the same target object but they heard no label. Children then were shown the thematic match and the shape match, and were asked to either “Find another zom (Label) or “Find another one” (No Label). Although children made shape matches on only 27% of the trials in the No-Label condition, they made shape matches on 62% of the trials in the Label condition. For example, if shown a flower that was labeled “a zom,” children were more likely to say that the wheel was also a “zom” than the bee. Baldwin concluded that young children will use shape as the basis for generalizing a novel label even in cases where the shape matches cross taxonomic category. Target

Thematic

Shape

Taxonomic

Flower

Bee

Wheel

Tree

Set A

carrot

Rabbit

Rocket

Squash

Fig. 9. Renderings of two of the object sets used by Baldwin (1992). Children in a Labeling condition were shown a target and heard it labeled with a novel label (e.g.. “This is a zom”). They were then shown either (a) the thematic and shape matches or (b) the shape and taxonomic matches, and were asked which of the two matches was also “a zom.” Subjects tended to choose the shape match when it was pitted against the thematic match, but tended to choose both shape and taxonomic matches equally when they were pitted against each other. The lack of shape dominance in this latter comparison may, however, have been due to certain properties of the stimulus choices.

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In another experiment, Baldwin sought stronger evidence in evaluating the role of shape versus taxonomic category. She asked whether children would be more likely to extend a label to an object that fa) matched the standard in shape but crossed category boundaries, or (b) matched the standard in taxonomic category but did not share the same shape. Children were again tested in a No-Label and a Label condition and were asked to choose between a shape and a taxonomic match to the target object. Baldwin found that 3- and 4-year-olds in the No-Label condition tended to group on the basis of taxonomic category, selecting taxonomic matches on 66% of the trials. However, children in the Label condition showed roughly equal preference for shape and taxonomic matches, selecting taxonomic matches 46% of the time (and, by inference, shape matches 54% of the time). Baldwin concluded that children use a variety of different bases for extending novel labels. Taking a stronger view, the children’s roughly equal preference for shape and taxonomic matches in the Label condition might suggest no special role for shape-based matches in object naming. However, several aspects of Baldwin’s choice of stimuli might have underestimated the role of shape. First, objects designed to match in shape did so at a very coarse level: They were shape equivalent at the level of the “convex hull,” which roughly represents the space that would be subtended if a rubber band were stretched around the object’s contours. Looking at Fig. 9, for example, the flower and the wheel would be shape equivalent as “round things” if all that mattered to the description of shape were the convex hull; the carrot and rocket would be shape equivalent roughly as “oblong things.” This basis for shape equivalence is undoubtedly much coarser than that underlying object naming by children or adults. Classes of round or oblong things are not, in general, named by the basic object vocabulary of most natural language^.^ Although adults in Baldwin’s study agreed that the Although most languages do not appear to have open class object names that describe categories of “round things” or ”oblong things.” these categories are often represented by closed class items in classifier languages (such as Japanese). Shape-based classifers are among the most common classifiers (Allan, 1973). It is of interest that these classifiers typically categorize objects on quite coarse shape properties, such as “long thin thing.” “flat thing,” “lumpy thing,” or “round thing.” In English, these dimensional properties are usually encoded as adjectives. for example, round,J?ut, long, and so on. Thus there is an apparent separation of shape descriptors mapped onto languages; Complex configurations (object shapes) seem to be linked to open class items-primarily object names-whereas simpler dimensions seem to be linked to closed class items such as adjectives or classifiers. Although only a few studies have been conducted on the acquisition of shape-based classifers, evidence suggests that these (coarse shape) classifers are not acquired earlier than object names (which we claim are linked to shape-based representations), and may be acquired later (Erbaugh, 1978).

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shape choices were more similar to the target than either the thematic or taxonomic choices, this does not mean that this description of shape equivalence accurately represents the nature of shape equivalence for perceivers of objects in the real world.5 A second aspect of the stimulus design concerns the taxonomic choices. These objects were members of the same superordinate category as the target; categories included food, plants, household items, animals, tools, and vehicles. Furthermore, all taxonomic choices were presumably familiar to children, including such objects as a tree, a squash (pumpkin), a pig, and a knife (the taxonomic matches for flower, carrot, squirrel, and glass, respectively). Consider what would be required of children in extending the target object’s novel label. First, they are given a novel label for a familiar object (e.g., “This is a dax” for a flower, a carrot, or a car.) Next, they are shown two choices including one that is the coarse shape equivalent of the target and another that is its known superordinate taxonomic equivaThe issue of shape equivalence has many ramifications. For example, Soja et al. (1992) have argued that shape is only tangentially related to object naming, as evidenced by the loose relationship between object shapes and object names in the physical world. They argue first that even objects having the same name are often vastly different shapes (e.g., the objects named by the count nouns telephone, clock, tree, and ruble). Their second point is the symmetrical one: That there are many shape-equivalent objects that have different names (just because they are different kinds). It is of interest that their examples of shapeequivalent objects are all at a similar coarse level: There are many many counter-examples to [Landau et al.’s] generalization that naming behavior depends upon shape. Take the shape sphere and the nouns “orange,” “grapefruit.” ‘‘berry,’’ “apple,” “melon,” “Milkdud,” “rock,” “stone,” “moon,” “sun,” “earth,” “ball bearing,” “ball,” “paperweight,” and “bubble.” Or take the shape torus and the nouns “donut,” “lifesaver,” “bagel,” “innertube,” “tire,” “ring,” “bracelet” and “necklace” . . . This exercise could continue for pages. . . . [Tlhese examples show that even within ontological category, distinct kinds sharing shape are labeled with distinct count nouns. (p. 103) It is significant, I believe, that the examples provided here are shape-equivalent at a very coarse level-for example, the level of a sphere or a torus. But of course, two objects can be shape equivalent on many different scales. For example. at a coarse level, balloons, grapefruits, and human heads are roughly round; logs, pencils, and bananas are roughly linear. But it would be a challenge, I believe, to come up with such an extensive list of shape-equivalent items at a finer level of scale that have different basic names. That is, it would be difficult to find a large set of items that are shape-equivalents such as all horses o r all cows or all tables or all chairs that also are different kinds and hence have different basic names. The challenging empirical question is what level of shape equivalence, exactly, is the relevant one for childrens’ early object naming. Answering this question requires systematic empirical studies, which are currently underway.

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lent. To generalize on the basis of shape, children would have to reject a known superordinate match and accept a very coarse shape match. To generalize on the basis of taxonomic category, children would have to reject the coarse shape match in favor of a known superordinate category match. The latter might be unlikely due to mutal exclusivity, whereby children are reluctant to assign a novel label to a concept that is already lexicalized (Markman, 1989). Neither the coarse shape match nor the (known) superordinate match would seem to be a compelling choice, possibly accounting for the lack of systematic preference for either solution. The fact that such a coarse basis for shape matches still led to substantial shape-based choices suggests that Baldwin’s findings of equivalence between shape and taxonomic matches might represent an underestimate of the role of object shape. An investigation by Imai et al. (1994) circumvented some of these problems. In these experiments, 3- and 5-year-olds were shown a familiar object as the standard (e.g., a cake) and either heard it named (Label condition) or heard no name ( N o Label). They then were presented with a set of three familiar alternatives, including a thematic, a shape, and a taxonomic match (for the cake standard, these were a wrapped gift, a top hat, and a pie, respectively, see Fig. 10).Although the taxonomic matches were again superordinate matches (as in Baldwin’s study), Imai et al. provided the children with pretraining designed to impress on the children that the novel words were from “dinosaur talk,” in which novel labels could apply to objects already encoded by English words. In addition, in one experiment, the pretraining consisted of explicit tutoring to the effect

Target

Set A

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Taxonomic

Gift

Hat

Pie

Feathers

Berries

& Cake

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4 Bi3XlanZI.S

Monkeys

Fig. 10. Renderings of two of the object sets used by Imai et al. (1994). Children were shown a target (standard) and heard it labeled with a novel label. Three- and 5-year-olds most often extended the label to the shape matches (e.g., the cake with the hat, the bananas with the feathers). whereas adults most often extended it to the taxonomic match (e.g., cake-pie. bananas-strawberries).

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that the novel words were the equivalents of English superordinates such as “animal.” These procedures were designed to ensure that the taxonomic match was a viable alternative. The second design difference involved the same-shape stimulus items used. A sampling of the line drawings used by Imai et al. suggests that they were careful to make the shape matches equivalent at a much finer level than Baldwin had (see Fig. For example, the birthday cake and the rigid top hat both possessed the same two principal volumes joined in the same way; the objects differed principally in the fine details of texture and coloration. Imai et al.’s findings suggest a strong role for object shape in name generalization among younger children, yielding to a strong role for taxonomic category in adults. Three- and 5-year-olds showed a pronounced shape bias in the Label condition, choosing the shape match 68% and 56% of the time (3- and 5-year-olds, respectively). In contrast, in the NoLabel condition, they chose the thematic match most of the time, as did adults (44%, 50%, 58%, for 3-year-olds, 5-year-olds, and adults, respectively; see Imai et al., 1994, Table 2). This held even when they were provided pretraining emphasizing that novel words could represent known English superordinates. Adults showed a pronounced taxonomic bias in the Label condition but not in the No-Label condition (64% vs. 33% taxonomic matches in the two conditions). Imai et al. interpreted their findings as evidence for a developmental change: a shape to taxonomic shift in the basis for extending novel object labels. They argue that the shape bias eventually gives way to a taxonomic bias, in which shape is considered to be a secondary property that is sometimes, but not always, a reliable indicator of category membership. They also suggest that the process of development occurs piecemeal, with increasing knowledge of individual objects eventually leading to the adult pattern of a taxonomic basis for extending novel labels. Evidence for this role of increasing knowledge was found in a separate study, in which Imai et al. asked whether variation in children’s knowledge of particular objects would affect their tendency to choose shape versus taxonomic matches. Children were shown the same standards and matches (shape-based, taxonomic) used in the principal experiment and were asked to select those matches that shared certain category properties, for example, putting together things “to eat” or things “that girls wear.” Children’s sorting performance varied over sets, with some sets sorted almost perfectly (by taxonomic criteria) and others quite full of errors. Based on these findings, Imai et al. performed an item analysis on the results of the 1 thank M. lmai and D. Gentner for providing several sets of stimuli to me.

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Labeling task in the principal experiment. They found that taxonomic matches in the Label task were made less often in sets where category knowledge contained errors. For example, children who did not correctly group together two things to eat were less likely to extend the label for that object set on the basis of its taxonomic category. This suggests that shape plays its most primary role in cases where other kinds of knowledge are not available (but see the next section for some counterevidence). In some ways, both Baldwin’s and Imai et al.’s experiments made an overly strong test of the shape bias: The special salience of shape is no doubt due to its correlation with object kind, that captured by taxonomic categories. It would not have been surprising to find that children refused to group systematically on either basis, considering the fact that these two properties are so strongly correlated in the real world. Yet given the findings of the experiments, it seems difficult to dismiss object shape as a weak or unimportant constraint on early object naming. The question remains exactly what role does shape play and how attention to shape interacts with other kinds of knowledge, for example, knowledge that things that have the same name are most often the same kind, and hence share many properties. Before turning to this issue, I consider one final source of knowledge that might challenge the shape bias in young children. WHENFUNCTION, B. SHAPEA N D FUNCTION: NOT SHAPE, DETERMINES MEMBERSHIP IN A NAMED OBJECT CATEGORY The results reviewed so far have indicated a reliable bias for shape over texture or size in the context of novel objects, and a similar bias for shape over superordinate taxonomic members in the context of familiar objects, especially those for which children possess less rich knowledge of the target category. This overall pattern of findings suggests that the shape bias for novel objects might be challenged if children’s general knowledge of an object category could be brought into play. One obvious candidate here is object function. Among adults, an object’s function is often considered to be a critical part of identifying new category members: Chairs are things on which to sit, knives are things used for cutting. Violations of typical object functions create puzzling, sometimes humorous, consequences, as shown by Jacques Carelman’s “Cafetiere pour masochiste” (Coffeepot for the Masochist, 1969; see Fig. 1 1). According to Miller and Johnson-Laird (1976), “function-at least for artifacts-is more basic to the definition than form” (p. 229). Evidence from a range of developmental studies suggests that young children understand object functions, and that functions tend to be salient

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Fig. 11. “Cafetierepour masochisre” (Coffeepot for the Masochist) by Jacques Carelman (1969). When a critical object function is violated, it often produces a puzzling or humorous result, suggesting that an object’s function may be critical to its name.

in children’s everyday interactions with objects. Recent evidence suggests that even prelinguistic infants are sensitive to object function, such as containment of substances by closed objects (Kolstad & Baillargeon, 1991) and retrieval of one object by another (Brown, 1990). Nelson (1974) suggested that young children’s earliest object concepts are fundamentally functional in nature, and that these concepts in turn support some of children’s earliest word meanings. Nelson also proposed that concepts are formed on the basis of function, and then generalized on the basis of form. This position would predict that children’s earliest object names would first be based on an object’s function, and later be generalized on the basis of the object’s shape. An experiment by Gentner (1978) tested this prediction by pitting appearance against function in an object naming task. Young children and adults were introduced t o two different complex novel objects, each of which possessed a highly salient function: In one, pushing a lever dispensed jelly beans, in the other, pushing an identical lever resulted in a complex motion of the object’s parts. Subjects were encouraged to manipulate each object while viewing the function, and during this proce-

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dure, each object was labeled (a “jiggy” and a “zimbo,” respectively). Later, subjects were shown a hybrid object that looked like one but possessed the function of the other. They were asked whether it should be called a “jiggy” (following the object’s appearance) or a “zimbo” (following its function). Gentner found a U-shaped function, with the youngest children (2- and 5-year-olds) and adults choosing strongly on the basis of appearance, but older children (5- to 15-year-olds) predominantly choosing on the basis of function. The strong reliance on appearance among the 2- to 5-yearolds is consistent with the findings described in Section I1 and with other studies, which also have found a tendency for an object’s appearance to dominate its function among young children in object naming tasks (Tomikawa & Dodd, 1982; Merriman, Scott, & Marazita, 1993). These findings might suggest that shape dominates function in early object naming; however, it is unclear from the studies just which aspects of appearance dominated function and how the functionally relevant parts of the objects related to their appearance. For example, in Gentner’s study, the hybrid object possessed the “form” of one standard but the function of the other. The preserved form was actually a highly complex appearance-a configuration with a particular arrangement of specific parts, colors, and textures. In a recent series of studies, Landau, Smith, and Jones (1994) have sought to challenge directly the shape bias by providing explicit information about an object’s function when that function is not related to the object’s shape. In a first experiment, we considered functions that are related to an object’s material. In our previous studies, neither children nor adults regularly generalized a novel count noun on the basis of material. Our question was whether explicit information about object function-when linked to an object’s material-might either weaken or eliminate the shape bias in favor of a bias to generalize on the basis of material, and whether these changes would show any developmental trends. For one set of objects, a standard was made of sponge and its designated function was absorbency; for the other set, the standard was made of cork and its designated function was to support stick pins (Fig. 12). There were two experimental groups, a No-Function group and a Function group. For both groups, we first introduced the standard object to subjects and labeled it (e.g., “This is a dax”), as we had in all previous experiments on the shape bias. Subjects in the Function group, however, also heard explicit information about the standard object’s function. For the standard labeled “rif,” the designated function was absorbency, and we told subjects “This is a rif. And this is what I can d o with it,” following with a demonstration of the function in which we spilled a small amount of water

288 This is a dax. (And this is what 1 can do with it.)

Barbara Landau This is a rif. (And this is what I can do with it.)

Fig. 12. Object displays used in studies of the role of object function in object naming (Landau et al.. 1994). Subjects in the No-Function condition were shown an object and heard it labeled (“This is a dax”). Subjects in the Function condition also were told about the object’s function (“And this is what I can do with it”). Function was always tied to the object’s material: For the dax set, the designated function was to support stick pins; for the rif set, the function was to wipe up water. Subjects were then shown objects different from the standard in either shape or material and were asked “Is this a dax?”

on the table, wiped it up, and stood the bottle of water next to the standard. For the standard labeled “dax,” the designated function was to support stick pins. We told subjects “This is a dax. And this is what I can do with it,” following with a demonstration in which we pushed a stick pin into the front surface of the object and set the entire display aside. Both groups of subjects then saw each of four test objects in each set and were asked “Is this a dax/rif?” Two of the objects in each set were the same shape but were made of a different material that was incapable of supporting the demonstrated function. In the rif set, these objects were either painted Styrofoam or glossy tightly woven fabric. In the dax set, these objects were either glossy hardened plaster or plastic Leg0 pieces. The remaining two objects in each set were different shapes from the standard, but were made of the same material-either sponge for the Rif or cork for the Dax. The results of the No-Function condition echoed those of our previous studies of the shape bias. Two-, 3-, 5-year-olds, and adults all tended to extend the novel label to objects that were the same shape as the standard,

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regardless of material. In contrast, the results of the Function condition showed a massive interaction with age: 2-, 3-, and 5-year-olds still extended the novel label to same-shape objects, but adults in this condition extended the novel label to objects of the same material as the standard, despite the fact that the objects were of different shapes (see Fig. 13). Thus, when told the function of the novel object, adults tended to generalize the object’s name on the basis of that property which supported the object’s function. This set of results agrees with those of Gentner, except that the developmental trend in Landau et al.’s study moves directly from shape to function without Gentner’s U-shaped return to shape among adults. The reason for this difference is not clear; it may be linked to the differences in stimuli between the two studies. However, the developmental change from shape (or overall appearance) to functionally relevant properties in both studies suggests that object function-which, for adults, often serves as a critical

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Fig. 13. Results of the function experiment (Landau et al., 1994). Children and adults in the No-Function condition extended the novel label on the basis of shape. as in previous studies of the shape bias. However. result5 of the Function condition showed a massive interaction with age: Whereas children still extended the label on the basis of shape, adults extended it on the basis of material. The results point to the importance of shape in early lexical learning and an increasing role of specific object knowledge in modulating the use of shape.

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property-does not enter into judgments of object naming until fairly late in development, even when it is explicitly demonstrated. One might object that the functional information that we provided was simply not presented in a salient enough fashion, or that the designated functions were not those with which young childen are familiar. In subsequent experiments, we have addressed these problems and have found that young children’s lack of attention to object function in the context of object naming is remarkably robust. In one experiment, children were given emphatic instructions about object function: “This is a dax. Daxes are made by a special company just so that they can be used for holding stick pins like this.” With these instructions, 3-year-olds showed the same strong shape bias they had with the less emphatic instructions, 5-yearolds showed a small reduction in the shape bias, and adults a small increase in their material bias. In another experiment, children were shown objects whose designated functions were highly accessible: containment (the standard was used for carrying water) and retrieval (the standard was used for pulling a toy across a small table). Children under the age of 2 years have been shown to appreciate both of these functions in nonlabeling tasks. For example, Kolstad and Baillargeon (1991) found that infants were sensitive to containment functions, showing surprise if a bottomless cylinder appeared to be capable of containment. Similarly, A . Brown (1990) showed that 18- and 24-month-olds were sensitive to the length and rigidity requirements for tools that could be used to retrieve another object. Still, in the context of object labeling, young children extended the novel label to objects that were the same shape as the standard, rather than those that were of a different shape but which could still fulfill these functions. Finally, in a number of these experiments, children also were asked explicitly about the objects’ functions following the naming task. For example, “Could you use this to hold a stick pin/ mop up water/ carry water/ pull a toy?” Overall, the results of these tasks indicate that children did understand the designated object function and were able to determine accurately which of the test objects could also fulfill those functions, despite the fact that they had not generalized the novel object’s name on the basis of function. For example, children said that cork objects that were a different shape from the standard could be used to support stick pins, even though they had rejected these as members of the named category “dax. The overall pattern of results, then, suggests that although object function may play an important role in early cognitive development-supporting planful, intelligent problem-solving activities, for instance-it does not appear to enter into early object naming, at least in the contexts so far studied. This does not mean that object function never enters into ”

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naming and the extension of novel object names. Quite the contrary, adults weight object function much more heavily than object shape when they are given explicit information about the object’s function. This pattern of development-from object shape to object function-is reminiscent of the pattern suggested by Imai et at., a pattern of development from shape to taxonomic responding. Although the relationship between shape and function on the one hand and shape and taxonomies on the other may be quite different, the two developmental patterns do begin to suggest that shape may indeed have a priviledged role in the early development of object naming.

VI. The Role of Object Shape in Early Object Naming Let me first suggest three conclusions based on the findings already reviewed, and then use these conclusions to propose an account of the shape bias in terms of its underlying representational system and its mechanisms of development and change. OF THE SHAPE BIAS A . CHARACTERISTICS

1.

Context Sensitivity

The shape bias is not pervasive across all contexts, as might be expected if it were due to an unbiased salience across all contexts. Even in the youngest children, a bias for object shape appears to emerge primarily in the context of rigid objects that are labeled by count nouns. With development, the bias appears even more selectively in the count noun context, leading to shape-based generalizations even for displays where nonlinguistic tasks reveal a greater salience of texture or material. As the count noun context exerts greater influence, other syntactic contexts come to serve increasingly as mental pointers to other properties such as surface texture (Landau, Smith, & Jones, 1992; Smith et al., 1992), substance (Subrahmanyam, 1993), or even location (Landau & Stecker, 1990). Thus the initial coupling of rigid object, count noun, and shape gives way with development to more articulated and diverse sets of generalization patterns guided increasingly by syntactic context. At all times, however, the shape bias is selectively elicited by certain contexts and not by others. 2. Structured Spatial Representations of Objects The shape-based representations underlying object naming are not rigid or inflexible. Young children make different kinds of shape-based generalizations depending on the character of the object they observe. For rigid, artifact-type objects, few transformations are accepted as legitimate in-

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stances of the labeled object category; for objects that are locally rigid but globally malleable (such as bendable tubes or worms), a different set of transformations is considered acceptable. For all of these examples, it seems clear that part and parcel of a “shape bias” in young children is a spatial-perceptual system whose character admits not only the representation of momentary shapes, but rather a representation of the set of shapes that is possible under different transformation conditions (see sections IV,A and IV,B for evidence). The system underlying the shape bias is, therefore, more like a system of knowledge-in permitting inferences based on existing information-than it is like a static system in which “lists” of existing momentary shapes are cataloged. 3. Relationships to Other Knowledge

The shape bias in children emerges even under conditions where it is pitted against other kinds of information that might be more important to adult decisions on object naming. Although children from the age of about 4 or 5 appear to be strongly affected by taxonomic o r functional information, children of 2 or 3 years of age seem to base their generalizations on shape in contexts where same shape is pitted against these other kinds of information. However, though the pattern of development is superficially similar for taxonomic and functional information (vs. shape), the underlying causes may be different. For functional knowledge, the evidence begins to suggest an early separation between the object representations underlying general knowledge of object and those underlying object naming. With development, these two representations appear to interact more closely, with adult knowledge of object function appearing to override similarity in shape. What is interesting about this possible dissociation early in development is that shapebased generalizations may provide better predictions for object naming than function-based ones. Although many adults share the intuition that function is critical to categorization of artifacts, an object’s function may be only tangentially related to what we call it: We can insert smaller objects into larger ones whether the smaller ones are called clothespins, pegs, or coins, and whether the larger ones are called milk bottles, pegboards, or slot banks; we can sit on things whether they are called chairs, benches, or garbage cans; and we can cut with things whether they are called knives, scissors, or ruler^.^ There is a direct consequence of this

’

The range of an object’s functions should not be equivalent to any action we can take with it: Although knives are typically used for cutting. they can undergo a range of uses from serving as a paperweight to serving as a screwdriver (if the blade is small enough). By suggesting a wide range of uses, I do not mean that absolutely any action taken on an object should be considered a plausible use.

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name-function mapping for learning object names: The kinds of manipulation that can be performed on an object may be overly general relative to its name. Although young children may possess knowledge of many object functions, they may also learn quite early in development that those functions do not map unambiguously into the categories of things that share the same name. The case with taxonomic knowledge may be somewhat different. Although lmai et al. (1994) found a preference for same-shape over same taxonomic kind, this finding need not imply that young children do not also use (nonshape) taxonomic information in extending object labels. There are two other possible interpretations. One possibility is that the children were willing to assign the same label to two objects in different ontological categories (e.g., hat and birthday cake) because, in general, similarity in shape licenses such crossing. As pointed out by Landau et al. (1988, Landau Jones, & Smith, 19921, naming practices are sometimes at odds with an object’s ontological category. Consider one of the deepest ontological distinctions-that between animates and inanimates. Even the youngest child presumably can detect the difference between a real bear and a toy bear; preschoolers make qualitatively different inferences about such cases, reflecting, for example, their knowledge of the different causes of motion in the two cases (Massey & Gelman, 1991). Yet young children are perfectly willing to call toy bears “bear,” presumably in virtue of the similarity in (shape-based) appearance. Perhaps even more dramatically, the child who has never before heard object names applied to pictured representations of those objects can easily supply the correct-real object-names for line drawings of familiar common objects (Hochberg & Brooks, 1962). The significance of these observations is that, although the real object and its (shape-based)representation clearly are members of different ontological categories, it still is quite natural for them to be called by the same name (and these would not be considered to be homonyms, as are other cross-category violations such as the winged “bat” vs. the wooden “bat”; see Keil, 1979). It is not just that the toy bear is a representation of a bear, as suggested by Soja et al. (1992). There are many other representations of bear for which one could nut naturally answer “yes” to the question “Is this a bear?,” for example, footprints of a bear, a piece of the bear’s fur, or the words a bear (examples from Soja et al., 1992). Similarity of appearance-and in particular, I would conjecture, shape-based similarity-appears to license the use of an object’s name to its (nonreal) lookalikes (see Jackendoff, 1992, for interesting relevant evidence from cases of anaphora). The second possible explanation for children’s preference for sameshaped objects is that this reflects children’s strong belief that objects of

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the same shape tend to be objects of the same kind. Again, considering one of the stimulus sets in Imai et al.’s paper, the target object hat was paired with a birthday cake as its shape match; both were composed of the same principal volumes arranged in the same spatial relationship. It is possible that this shape identity led children to believe that the birthday cake was a hat that was decorated to look like a cake, or that the hat was a cake decorated to look like a hat! Under these conditions, it certainly would be acceptable to extend the novel label from one to the other. This interpretation does not in any way diminish the importance of Imai et al.’s findings, but it does suggest that their finding of a same-shape preference need not imply that children do not also use a taxonomic basis for extending novel labels. The findings could also be accounted for by a system of representation that includes both perceptual information and knowledge of like kind, and the mapping between the two. Let me now propose some specifics for such a representational system.

B. REPRESENTATIONAL SYSTEM A N D DEVELOPMENT: A PROPOSAL There are three key elements to any account of the early development of object naming that seeks to unify the evidence and arguments presented so far: These are the perceptual representations of objects, their conceptual representations (kinds), and their names. Arguments about the respective roles of perception and conception have often sought to pit these against each other, as if our experience of objects is either perceptual o r conceptual, as if children rely on either the perceptual or conceptual, and as if there is some “true” foundation for the development of naming which is either perceptual or conceptual. Yet, this seems quite wrong. It is more likely that perceptual and conceptual systems evolved to work in cooperation, both serving to allow the learner a wedge into the induction problem, fostering further learning.

I. Shape-Based Representations Are Natural for Object Naming

Consider first the possibility that infants approach language learning with a system of object representation that is rooted in perception. The visual system is sensitive to shape, texture, color, and so forth. However, evidence suggests that, for adults, object recognition is linked strongly to object shape. Adults can easily recognize and label familiar common objects (including artifacts and natural kinds) on the basis of line drawings or silhouettes (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). Although an object’s color may be highly salient for certain purposes,

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there is virtually no time advantage for recognition gained by the addition of color to line drawings of objects (Biederman & Ju, 1988). Thus, it appears that shape-based representations are special: They are meaningfully linked to the corresponding 3D objects immediately, automatically, and irresistably. In infants and young children, the evidence is more limited, but quite compelling. Of critical importance is the demonstration by Hochberg and Brooks (1962) of the child’s “instantaneous” application of object names to line drawing (shape-based) representations. In this case, a 2-year-old who had no previous experience hearing adults use object names for representations of objects (such as paintings), nevertheless correctly named a set of line-drawn objects on first exposure. Related evidence comes from studies of infants that indicate an early ability to recognize photographs of corresponding objects, whether novel or familiar (see, e.g., DeLoache, Strauss, & Maynard, 1979). Finally, the evidence reviewed in this chapter suggests that when young children observe rigid objects and hear them labeled, a potent basis for generalization is object shape. The link between object name and object shape seems to be readily accessible during early development. It is natural. The system responsible for the visual computation of object shape would seem to qualify, on a number of criteria, as part of a “modular” visual input system responsible for object recognition (Fodor, 1983).*First, the object recognition system is domain specific: Its representations can account for the perception of objects (principally on the basis of shape), but not, say, the perception of sentences. Second, its operation is mandatory: We have no choice but to see a given object as the visual system computes it; it is virtually impossible to see a dog as a contour filled with a textured pattern. Third, it is fast. Fourth, it is informationally encapsulated: There are strict limitations on what objects we actually see, regardless of what we wish to believe. (As Fodor points out, “organisms that don’t (see this way) become deceased,” p. 68). Fifth, its analyzers have shallow outputs; there is generally a level of description that captures what we see and this is different from what we know (“dogs but not protons count as

* These criteria are a subset of those proposed by Fodor (1983) for modularity. One might wonder why there are linguistic context effects (i.e., syntactic effects) on construal of a visual display, if the object recognition system is truly modular. The syntactic effects of count noun versus adjective would not seem incompatible: Subjects merely switch their attention from the object’s shape to its texture. both represented in its visual representation. More of a puzzle would be syntactic context effects such as those reported by Subrahrnanyam (1993) in which a count noun directs attention to object shape, but a mass noun directs attention to a nonobject reading (i.e., substance, not object).

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observed,” Fodor, 1983, p. 97). Finally, the ontogeny of the object recognition system shows a characteristic developmental sequence and it is intact prior to language learning. What role could such a system play in early object naming? Of special significance here is Fodor’s suggestion that basic level categorization (Rosch et al., 1976) may be delivered from such a modular visual input system-and if so, it would provide a ready-made representation waiting to be mapped to an object name. Following R. Brown (1958), Rosch et al. argued that there is a certain level of category hierarchy at which many properties converge to create special psychological salience. These properties include (a) its members tend to be lexicalized monomorphemically (e.g., “dog”), (b) they tend to be learned first (“dog” before “animal”), (c) they tend to lend themselves naturally to ostensive introduction (“This is a dog”; see Shipley, Kuhn, & Madden, 1983, for relevant evidence), and (d) people tend to behave as if the members share many properties. Of particular importance here is the fact that members of basic level categories also seem to be those that are shape-similar. According to Fodor (1983): Basic categorizations are typically the most abstract members of their inferential hierarchies that could be assigned by an informationally encapsulated visual-input analyzer; more abstract categorizations are not reliably predicted by visual properties of the distal stimulus . . . Suffice it that the notion that visual analyses are computed by an informationally encapsulated system leads to the prediction that there should be some set of representations which are (roughly) shape-assignable on the one hand. and which. on the other hand. play a specially central role in the mental life of the organism. The pregnancy of the basic category construct suggests that this prediction is true. (p. 97)

2. Shape-Based Representations Can Be Linked to Object Kind Suppose, then, that the visual system delivers up such shape-based descriptions, and that these are important in guiding young children’s object naming-particularly , objects whose names can be introduced by the ostensive definition “This is a (dax).” What of object kind? Suppose also that children come to learning not only with a rich perceptual system, but also with a rich conceptual one in which there exist representational elements dedicated to important ontological categories such as “kind of object” (cf. Soja et al., 1992). Suppose further that these same elements were designed to be put into correspondence with object names, such that object names were mapped onto object kinds. A problem still would remain: How is the learner to decide which of the many objects in the

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real world actually are of like kind, hence, which should be named by the same name? It is interesting to note that this problem is similar to that posed by Fodor (1966) regarding linguistic categories such as noun and verb. These categories cannot be reduced to “object” or “action” in the mature system (any more than “kinds” can be completely reduced to “shape”), and it is only under peril of the wrong conjectures or penalty of massive qualitative developmental change that the learner could begin with such a conjecture. However, as Grimshaw (1981) and Pinker (1984) suggest, the child might solve this problem by assuming some canonical mapping such that objects are encoded by nouns and actions by verbs. Note that assuming such a canonical mapping does not prevent the child from possessing the correct knowledge about noun categories, nor from using distributional evidence to identify nouns in the speech stream. What the canonical mapping provides is a “bootstrap” into the system, allowing identification of a stock of nouns independent of distributional considerations. Given knowledge of a few such nouns, children can then apply the other (syntactic and distributional) criteria needed to identify new nouns and verbs. In this view, bootstrapping from one set of assumptions can provide the child with the needed wedge into discovering which are the nouns and which are the verbs in the speech stream. After identifying a stock of nouns and verbs on this basis, the child, using distributional criteria, will be able to learn nouns that do not encode objects and verbs that do not encode actions. But even at this point, his o r her preliminary conjectures need not be discarded: It will always be true that objects can be encoded as nouns and actions as verbs. A similar solution can be applied to the problem of discovering which things are of like kind. The key is the existence of a system of object representation such as previously suggested. Such a system can provide reliable information to the learner about which objects will likely be grouped under the same name. If the object naming system is linked to object shape on the one hand and object kind on the other hand, then young learners might assume that objects of similar shape are also likely to be of similar kind. That is, the links among shape, name, and kind should allow learners to make a critical inference: Objects of similar shape are often also of like kind. This would be compatible with the suggestion that people believe in causal relationships between surface and “deeper” properties of category members (Medin & Ortony, 1989). The learner who is engaged in object labeling tasks should tend to extend labels to objects of like shape for two (linked) reasons: first, because the object representation system finds shape-based descriptions most natural, and second, because similarity of shape is meaningfully linked to similarity of kind.

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3. Shape-Based Representations are Advantageous to Learners

Such a system would have a number of benefits to the learner. First, the object representation system underlying links between shape, name, and kind is presumably a part of human innate endowment; learners are prepared t o link up object names with object shape well before the earliest points of language learning. Second, shape is valid as a symptom of object kind, not because of any necessary relationship between object shape and object kind, but rather, because of the way the world is and the way that we have evolved to talk about the world. To put it succinctly, there is a lawful covariation between object kind and object shape in the world. Languages capture object kinds with names, hence objects with the same shape are often grouped together under the same name. A third advantage is that shape-based judgments do not depend on the acquisition of specific knowledge about objects, some of which might be conveyed only through explicit instruction (e.g., that birds lay eggs or that sponges absorb water). Because such specific knowledge is not required for shape-based naming, learners will not suffer from the vicissitudes of experience: Whether the child’s parents are highly motivated to provide massive amounts of information about object kinds, or not at all so motivated, the child still will be able to make the initial connection between object shape and name, creating a critical wedge into object kinds. Finally, if the visual input system is indeed modular, learners will be capable of making determinate decisions about how to extend an object’s name, despite the in-principle indeterminacy pointed out by Quine. 4.

Shape-Based Representations Can Foster Further Development and Learning

Once shape is entered as a good diagnostic for like kind, this correspondence can serve as a vital force in organizing further learning. For example, as many have pointed out, objects of like kind often share properties that are not present to observation. How does the child learn these? Much of this will be conveyed to the child through explicit instruction (Shipley, 1993), in which the object’s category label plays a prominent role. If children believe that same shape is a good diagnostic, they might search for other similarities among objects of similar shape, just as they might search for similarities among objects of same name (see Gentner & Ratterman, 1991, for a similar view). Objects possessing both the same shape and the same name might form a critical pair that could serve to heighten attention in children, allowing further learning of what other important commonalities exist among members of named categories. This conjecture

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would account for the fact that the youngest children tested are more likely to draw inferences about novel object properties to objects that are both perceptually similar to a target object and bear the same name (Davidson & Gelman, 1990). In this scheme, most of the burden of development would be placed on learning about other diagnostics for category membership, and thereafter (with increasing attentional and memorial resources), organizing and revising their weighting both relative to shape and to each other. Unlike the shape-to-taxonomic change proposed by Imai et al., this scheme suggests that for both adults and children, object shape might be at the top of a preference hierarchy, where other properties also reside. Being at the top of such a hierarchy would place shape first as a conjecture for young learners, and also for adults under time-constrained conditions (such as those occurring in many object-recognition tasks (see, e.g., Biederman & Ju, 1988). Other diagnostics could include properties such as object function (e.g., is used for slicing) as well as sets of properties viewed as more critical for natural kind categories, for instance, behavior patterns (e.g., eats worms, bears its young alive), genetic composition, and so forth. As many of these are subject to revision along with progress in science, one would expect massive revisions over development and with expertise regarding exactly which of these properties will be viewed as most critical. Differences in specific knowledge could, therefore, cause massive differences between adults and children in the extent to which they generalize object names on the basis of shape. As children acquire more information about what makes a named object a member of a particular category, and as they can devote additional resources to thoughtful consideration, they may be more likely to move down the hierarchy, considering a variety of properties as more critical than appearance. For example, the child who is instructed that “whales look just like fish, but they’re really different because they’re mammals” will have learned that criteria other than appearance are considered by the speaking community to be more important to categorization. It is an empirical question whether such an adjusted weighting will then generalize to all new categories, or to just new categories of animals. But note that the instructional experience-an instance of explicit tutoring-will be a necessary condition for adjusting the weighting (see Shipley, 1993, on the significance of explicit instruction in promoting inductive inferences). Significantly, what is unlikely to change is how the object looks to the learner. Knowing that coral is really a collection of tiny organisms does not make it look any less like plant life; knowing that mushrooms really share as many characteristics with animals as with plants does not make them look more animate. In these cases, what makes the object an animal

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versus a plant is impossible to detect through perception alone. The relative impenetrability of this kind of object perception is both a strength and a weakness for the learner: Because appearance is impervious to knowledge, individual differences in learning may not have much impact on the early stages of word learning (either in development of a new word meaning o r in the development of an individual’s lexicon); but also because it is impervious to knowledge, reliance on what an object looks like will ultimately cause some incorrect conjectures for the learner, as when whales are called “fish” or the Pekinese is called a “cat.” Luckily, the learner who also recognizes that object names capture object kinds (of which shape is a preferred but not an exclusive diagnostic) will also be able to revise his or her description of the properties considered most critical to ideIltifying new instances of a named category.

VII.

Summary and Conclusions

In this chapter, I have argued that the existence of a shape bias can provide a vital link to mapping knowledge of kinds onto the objects that exist in the physical world. Because of the characteristics of the shape bias-its selectivity, its mapping to syntax, its guidance by a rich system of object representation-there is no need to conclude that the child who has a shape bias will possess impoverished or inaccurate representations. Rather, the shape bias can serve as the child’s bootstrap into what otherwise would be an impossible problem to solve. What is the ultimate fate of the shape bias as children learn more about other kinds of criteria for categorizing objects? Does it ever really go away? Our evidence from adults strongly suggests that it does not go away, but rather, continues to play a prominent role in guiding people’s inferences about the extension of novel named categories. Recall that adults, not children, show the strongest shape bias when novel rigid objects are introduced with no further information (Landau et al., 1988).However, the shape bias also admits of enrichment through the acquisition of specific knowledge, and this knowledge can, in some circumstances, come to override shape. Most important from a developmental perspective, however, is that the shape bias may be the key to getting the object naming syztem off the ground. The existence of the shape bias does not suggest that young children are “perception-bound,’’ nor that they must undergo massive qualitative change to consider factors other than shape. What it does suggest is that nature planned well in designing learners to be sensitive to properties that really do count.

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ACKNOWLEDGMENTS This work was supported by Social and Behavioral Sciences Research Grant #12-214 from the March of Dimes Birth Defects Foundation and Grant #l-ROI-HD28675-OI from the National Institutes of Health. The author thanks Paul Bloom, Doug Medin. Elizabeth Shipley, Manish Singh, Kaveri Subrahmanyam. and Sandy Waxman for very helpful comments on the chapter, and Diane Beck, Jamie Chosak. and Jennifer Nolan for assisting with the work described here. Correspondence should be addressed to B. Landau. Department of Cognitive Science, University of California. Irvine, CA 92717.

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meaning using artificial objects. Papers and Reports on Child Language Development, 15, 1-6. Gentner, D., & Ratterman. M. (1991). Language and the career of similarity. In S. Gelman & J. Byrnes (Eds.), Perspectives on thought and language: Interrelations in deuelopment. London: Cambridge University Press. Grimshaw, J. (1981). Form, function, and the language acquisition device. In C. Baker & J. McCarthy (Eds.), The logical problems of language acquisition. Cambridge, MA: MIT Press. Hochberg, J., & Brooks, V. (1962). Pictorial recognition as an unlearned ability: A study of one child's performance. American Journal of Psychology. 75, 624-628. Hoffman, D., & Richards, W. (1984). Parts of recognition. Cognition, 18, 65-96, Hoffman, D., & Singh, M. (in preparation). Parts of recognition: Part salience. Manuscript, University of California, Irvine. Hummel, J., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99(3), 480-517. Imai, M., Gentner, D., & Uchida, N. (1994). Children's theories of word meaning: The role of shape similarity in early acquisition. Cognirive Development, 9( l), 45-76. Inhelder, B., & Piaget, J. (1964). The early growth oflogic in the child. New York: Norton. Jackendoff, R. (1992). Mme. Tussaud meets the binding theory. Natural Language and Linguistic Theory, 10, 1-3 I . Jones, S.. & Smith, L. (1993). The place of perception in children's concepts. Cognitive Deuelopment, 8(2). 113-139. Jones, S . , Smith, L.. & Landau, B. (1991). Object properties and knowledge in early lexical learning. Child Development, 62, 499-5 16. Jones. S., Smith, L., Landau, B., & Gershkoff-Stowe. L. (1992). On the origins of the shape bias in young children's novel word extensions. Paper presented at the Boston University Language Development Conference, Boston, MA. Keil. Frank C. (1979). Semantic and conceptual development: An ontological perspective. Cambridge. MA: Harvard University Press. Keil, F. (1989). Concepts, kinds. and cognitive deuelopment. Cambridge, MA: Cambridge University Press. Keil, F. (1994). Explanation, association, and the acquisition of word meaning. In L. R. Gleitman & B. Landau (Eds.), Lexical Acquisition. Lingua, Special Volume, 92(1-4). Kolstad, V.. & Baillargeon, R. (1991). Appearance and knowledge-based responses to containers in infants. Unpublished manuscript. Landau, B., & Gleitman, t.R. (1985).Language and experience. Cambridge, MA: Harvard University Press. Landau, B., Jones, S., & Smith, L. (1992). Perception, ontology, and naming in young children: Commentary on Soja. Carey, and Spelke. Cognition, 43. 85-91, Landau, B., Leyton, M., Lynch. E.. & Moore, C. (1994). Perception, object kind, and object naming. Unpublished manuscript. Landau, B., Smith, L.. &Jones, S. (1988). The importance of shape in early lexical learning. Cognitive Development, 3, 299-32 I . Landau. B., Smith, L., &Jones, S. (1992). Syntactic context and the shape bias in children's and adults' lexical learning. Journal of Memory and Language, 31. 807-825. Landau, B., Smith, L., &Jones, S. (1994). Object shape, object function, and object name. Unpublished manuscript, University of California, Irvine. Landau, B., & Stecker. D. (1990). Objects and places: Syntactic and geometric representations in early lexical learning. Cognitive Devekopment, 5 , 287-3 12. Leyton, M. (1992). Symmetry. causality, mind. Cambridge, MA: MIT Press.

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Lowe, D. (1985). Prrceptitrrl orgcrni:rrtion trnd uisiral rc>cognition. Norwell MA: Kluwer Academic. Markman. E. (1989). Categorization iti c.liiltirc~ti: Prohlems qf’ induction. Cambridge, MA: BradfordiMIT Press. Markman, E . (1994). Constraints o n word meaning in early language acquisition. In L. R. Gleitman & B. Landau (Eds.), Lr.xic,a/ Acyrtisition. Lingua. Special Volume, 92( 1-4). Markman, E., & Hutchinson. J . ( 19x4). Children’s sensitivity to constraints o n word meaning: , 121-157. Taxonomic versus thematic relations. Cognitiue P s y h o l o g . ~ 20, Marr, D. (1982). Vision. New York: Freeman. Marr. D., & Vaina. L. (1982). Repre\entation and recognition of the movement of shapes. Proc~rc~ditigs of‘thr Roycrl Soc.irty 1 ! / London. 214. 501-524. Massey. C.. & Gelman. R . (1988). Preschooler’s ability to decide whether a photographed Psyc/iolog.y. 2 4 ( 3 ) . 307-3 17. unfamiliar object can move itself. I~c~ueIoptrirntrrl Medin. D., Goldstone, R., & Gentner. D. ( 1993). Respects for similarity. Psyc~hologic~ctl Rcwir~i~ 100(2). . 254-278. Medin. D.. & Ortony. A. (1989). Psychological essentialism. In S . Vosniadou & A. Ortony (Eda. 1. Similarity trnd crnnlogic~trlrctr.rotiiitg. New York: Cambridge University Press. Merriman. W . , Scott, P.. & Marazita. J . (1993). An appearance-function shift in children’s object naming. Joirrnrrl of’ Child Littigricrge. ?O( I ). 101- I 18. Miller. G.. & Johnson-Laird. P. (1976). Lotigrili,ye cindprrcrption. Cambridge. MA: Harvard University Press. Nelson. K . (1974). Concept. word. and sentence: lntercorrelations in acquisition and development. Psyc.hologic,u/ Rcuien,. R l , 267-35. Nosofsky. R. M. (1984). Choice, similarity. and the context theory of classification. Jortrriul qf’E.\-prrirnrntcrl Psychology: Lcirrtritig, Mrmcrry, & Cognition, /0(1 ), 104- I 14. Peters. A , . & Zaidel. E. (1980). The acquisition of homonomy. Cognition. 8 , 187-207. Pinker, S. ( 1984). Lotigrrtrge lecrrtitrhility otid Intigiruge druc4opmetit. Cambridge. MA: Harvard University Press. Poulin-Duboi5. D.. Klein. B.. Graham. S . . & Frank, I . (1993). Shape and the taxonomic assumption. Paper presented at the 25th Stanford Child Language Research Forum, Stanford University. Palo Alto. CA. Putnani. H. (1977). Is semantics possible’! In S . Schwartz (Ed.), Naming. necrssity, and tie1111rc11Iririds. Ithaca. NY: Cornell University Press. Quine. W. (1960). Word and ohjecf.C‘arnbridge. MA: MIT Press. Quine. W. (1973). The. roofs t ~ f r ~ f i ~ r e t i La c ~ eSalle, . IL: Open Court Press. Rosch. E.. Mervis. C . . Gray, W.. Johnson. D.. & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitiuc’ P.~yt~ho/ogy. 8, 382-439. Schwartz. S. (Ed.). (1977). Ntrmitig. tw(,c.s.sity.and riarural kinds. Ithaca. NY: Cornell University Press. Shepard. R . (1964). Attention and the metric structure of the stimulus space. Jorirnol of M~ithrmcrtic~d Psvcholtjgy. I . 54-87. Shipley. E. (1993). Categories, hierarchies. and induction. In G. Nakamura. R. Tarahan. & D. Medin (Eds.), The psyc,hology of Irirrning and motiuotion: Reprc~sentcitioti arid p r o c c J s s i q qf’cztegorirs trnd t ~ m ( ’ ~ p t(Vol. .s 30). Orlando. FL: Academic Press. Shipley. E.. Kuhn, I . . & Madden. E. C. (1983). Mothers’ use of superordinate terms. Journal qf Child Languagr. I O ( 3 ) . 571-5x8. Slater. A., & Morison, V. (1991). Visual attention and memory at birth. In M. J. Weiss & P. Zelazo (Eds.). Nrw,born Artention. Norwood. NJ: Ablex. Soja. N . (1992). Inferences about the meanings of nouns: The relationship between perception and syntax. Cognitiur. Drurlopni~tit,7. 29-46.

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Soja, N.. Carey, S., & Spelke, E. (1991). Ontological categories guide young children’s inductions of word meaning: Object terms and substance terms. Cognition, 38. 179-21 I . Soja. N.. Carey, S . . & Spelke, E. (1992). Perception, ontology, and word meaning. Cognition, 45, 101-107. Smith. L.. & Heise, D. (1992). Perceptual similarity and conceptual structure. In B. Burns (Ed.), Percepts, concepts, and categories: The representation and processing of information. New York: Elsevier. Smith. L., Jones, S . , & Landau, B. (1992).Count nouns, adjectives, and perceptual properties in children’s novel word interpretations. Developmental Psvchologv. 28. 273-286. Subrahmanyam, K. (1993). Perceptual processes and syntactic context in the learning of count and mass nouns. Doctoral dissertation, University of California, Los Angeles. Tomikawa. S. & Dodd, D. (1982). Early word meaning: Perceptually or functionally based? Child Development, 51, 1103-1 109. Waxman, S. (1994). The development of an appreciation of specific linkages between linguistic and conceptual organization. In L . R. Gleitrnan & B. Landau (Eds.), Lexical Acquisition, Lingua, Special Vofume,92(1-4).

THE ONTOGENY OF PART REPRESENTATION IN OBJECT CONCEPTS Philippe G . Schyns rind Gregory L. Murphy

I. A.

The Origins of Parts

FEATURES I N CONCEPT REPRESENTATIONS

In the traditional artificial category-learning experiment (e.g., Bourne, 1982; Bruner, Goodnow, & Austin, 19.561, subjects were often shown very simple stimuli, such as colored geometric stimuli, which varied on a few very obvious dimensions. Thus, the universe of possible concepts was effectively limited to those dimensions. For example, subjects could focus on the color, shape, and number of geometric figures to learn the concept, ignoring other possible features. Although more recent research and theories have gone beyond this early work in many respects, many experiments continue to use simple artificial stimuli for reasons of experimental control (e.g., Gluck & Bower, 1988; Murphy, 1991; Nosofsky, 1988; Wattenmaker, 1991; among many others). Unfortunately, when encountering real-world objects, the set of stimulus dimensions is not limited in the same way as in these experiments. For example, reading X-ray films, identifying bacteria in a microscope, or categorizing pre-Cambrian organisms may require the use of new stimulus dimensions for their classifications-stimulus dimensions that were not used prior to the experience with the classification system. For example, consider the case of evaluating chest X rays as discussed by Lesgold (1984; Lesgold et al., 1988). N o t surprisingly, complete novices who examine such X rays have little understanding of what the relevant features THE PSYCHOI.OGY OF LEARNING A N D MOTIVATION. VOL. II

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are. However, this is not simply a matter of not knowing what the medical categories are; medical students and beginning residents know what the signs are for different diagnoses, but they are not always able to see these features in the X rays. For example, Lesgold et al. (1988, p. 325) discuss an X ray that “depicts a patient with multiple densities in the lungs, which represent tumors and other local pathologies . . . in this film, the novices merged local features (e.g., tumors) and saw a general lung haziness that is probably not present to the extent they thought it was.” Although there were perceptual cues in the X ray that experts noticed, these are not perceptual features that we have preexisting representions of. Learning to interpret X rays involves learning to identify the features as much as learning which features go with which diagnosis. Lesgold et al. (1988, p. 329) put it succinctly: “Experts see things differently.” In short, we are arguing that it is simplistic to assume that the features that are the basis for concept learning are somehow clearly given in the environment. What seems to the uninitiated as “haziness,” could really be discrete densities. This problem arises with more familiar, nontechnical concepts as well. For example, if subjects are asked to list features of an animal category, one would get answers like “has legs.” “is brown.” “eats fish,” and so on (Rosch & Mervis, 1975). Why is the feature “eats fish” and not “movesjaw rhythmically,” o r “kills other animals,” or “eats pink salmon“? It may well be that “eats fish” is a more useful feature for identifying category members, but it is not clear how that particular encoding of a behavior was chosen over other possible encodings. The question we are addressing in this chapter is the nature of the components of conceptual representation. Do these components form an independent set of stimulus dimensions that exists before experience with the category, or are features of representation progressively learned and developed as the organism categorizes its world? If one takes a developmental perspective, it seems clear that infants and young children are not aware of all the stimulus dimensions that will become important in later category learning (and this has been used to explain deviations between children’s and adults’ word usage by Clark, 1983; Mervis, 1987; and others). Thus, we need an account of how the features themselves are acquired. Wisniewski and Medin (1991; in press) discuss one influence on the process of feature construction in concept formation. They argue that the features that learners find in a category are often based on their expectations and biases toward the category (i.e., knowledge), rather than on purely perceptual processes. They demonstrated that the same stimuli were interpreted as having quite different features depending on subjects’ information about their source. For example, subjects viewed a series of

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children’s drawings and were told either that they were drawn by creative children or urban children. In one case, a series of dots on the shirt of a figure was interpreted as “buttons,” revealing a “detailed drawing” of a creative child by one subject, but was interpreted by another subject as a “tie” revealing “city clothing” drawn by an urban child. Thus, subjects’ knowledge about the source of the drawing influenced their interpretation of the stimulus, leading to different categorization strategies (see Murphy, 1993, or Pazzani, 1991, for more examples.) The experiments reported here focus on some of the ways in which features are constructed as a consequence of category learning. However, unlike Wisniewski and Medin. we will not address the influence of knowledge, instead. we look at how the parsing of an object into features can be affected by the category it becomes a member of. That is, during the process of concept formation, certain properties of objects get encoded as features in the conceptual representation because they are useful in specifying and distinguishing categories. Other properties of objects that may be equally “real” may not be attended to and encoded in memory. In particular, we will focus on how an object may be divided up into components o r parts. Although there are often strong perceptual cues to what the parts of an object are, the parsing of the object may also be influenced by category learning, as we describe in the next two sections. Thus, this type of learning may combine with the knowledge-based aspects described by Wisniewski and Medin to influence the construction of features in features representations.

B. PERCEPTUAL CONSTRAINTS ON PARTEXTRACTION Theories of categorization assume that the parts identified in an object (as well as other features) are compared to memory representations in order to identify the object (e.g., Rosch & Mervis, 1975; Tversky & Hemenway, 1984). A particular stimulus may be logically decomposed into infinitely many parts: therefore part extraction has too many solutions and is essentially an ill-posed problem (Bertero, Poggio, & Torre, 1987; Hoffman & Richards, 1985; Koenderink & van Dorn, 1982). Research in vision has derived a number of perceptual cues through which part structure may be determined. Hoffman and Richards (1985) proposed one reliable property of part boundaries. They suggested that when two parts are combined, they always meet in a contour of concave discontinuity of their tangent plane (the frunsuersulity principle, Hoffman & Richards, 1985, p. 69). For example, in the intersection of an antenna and the body of a radio, the resulting composite object is discontinuous where the antenna and the body intersect. Perhaps more relevantly, Hoff-

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man and Richards generalized their algorithm to continuous surfaces without sharp edges, which do not produce concave discontinuities. When a discontinuous surface is smoothed out, a discontinuity becomes a local minimum of negative curvature. Transversality applied to continuous surfaces results in the minima rule algorithm: Divide a surface into its parts at loci of negative minimu of each principal curvature along its associated .family o f f i n e s ofcurvature (Hoffman & Richards, 1985, p. 74). For example, imagine a range of hills and valleys without any abrupt discontinuities. The minima rule would still cut the scene into different parts at the bottom of each valley (the “minima of Gaussian curvature”) thereby separating

Fig. 1. This figure shows the silhouette of a desk lamp obtained with a detection scheme. White arrows indicate the segmentations predicted by Richards’s (1985) transversality principle. For example, the pair of white bottom left indicate the boundary between the base and neck. Note that decomposes the lampshade part into two perceptual components.

standard edge Hoffman and arrows at the this principle

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the hills. Braunstein, Hoffman, and Saidpour (1989) provided empirical evidence for using the minima rule. Categorical Constraints on Part Extraction

C.

One might imagine from this literature that the sole determinant of part analysis is perceptual rules like the minima rule. However, it seems likely that the permanent representations formed in memory for categories of objects are somewhat different. Consider the desk lamp shown in Fig. 1. Most people would parse the desk lamp of Fig. 1 into four parts: a lampshade, switch, neck, and base. However, a decomposition of the object based solely on the properties of its silhouette gives rise to five parts, not four, as indicated in Fig. 1 by pairs of arrows. In particular, the minima rule’ divides the top lampshade part into two components. whereas most people would treat it as a single component. That is, people seem to disregard this extra segmentation and to represent the lampshade as an unsegmented whole. This example illustrates the distinction between two types of components: the ones extracted from perceptual constraints such as the minima rule, and the components actually represented in memory for the object. We think that the relationship between these two parts is a missing link in current componential theories of object recognition and categorization. It has not yet been determined how perceptually defined features develop into the features of conceptual representations. That is, there is no current account of how the two perceptual units of the lampshade in Fig. 1 turn into one part encoded in the memory representation. In the lampshade example, a set of potential parts were encoded into a different number of features. This phenomenon illustrates what we call the conceptual instantiation of a part. Conceptual instantiation is the process by which a mere fragment of an object becomes a relevant descriptor of objects. This process changes a part’s status from perceptual to conceptual-that is, used in categorizing objects (see also Schyns, 1991;

’

In this chapter. we take a rather liberal interpretation of the minima rule. Hoffman and Richards’ (1985) definition strictly concerns the finding of part boundaries at extrema of negative curvature on the surface. These extrema are generally (but not always) preserved in the 2D projection of the surface (its silhouette; though see Hoffman &Richards’ discussion of possible limitations). Hoffman and Richards f 1985, 1988)also discuss parsing methods for 2D curves. Although the 3D and 2D parsing methods are different and should be distinguished (Kurbatt. 1994).both rely on finding minima of curvature and are motivated by transversality regularity. In this chapter, the minima rule is meant to refer to 3D and 2D parsing methods.

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Schyns & Murphy, 1991). Examples of conceptual parts are head, nose, wheel, handle, and so forth. These are the parts that make up concept representations. We will argue that the change of a part’s status from perceptual to conceptual arises from the need to categorize and represent objects with an efficient coding scheme. The features that people construct are the ones that will be useful in categorization. This claim is expressed in the following jLncrionality principle: If a fragment of a stimulus categorizes objects (distinguishes members from nonmembers). the fragment is instantiated as B unit in the representational code of object concepts.

The functionality principle simply expresses that a representational unit is defined functionally: A unit distinguishes category members from members of contrast categories. To illustrate, consider a simple example of two objects that make up a category. Imagine that objects in this category are described by a few components from the set of primitives P = {u,b,c,d,e,f,g}.Further suppose that Object 1 = {a,b,c,d,e}(see Fig. 2, Object I ) , and Object 2 has four components, three of which are the same as those of Object 1: Object 2 = {a,b,c,f}(see Fig. 2, Object 2). The functionality principle suggests that the category as a whole is associated with a subset of the stimuli, namely the set of three components {u,b,c} that is found in both of its members; each individual object is distinguished by the remaining component, {d,e}or {f}. The functionality principle claims that exemplars of categories are recoded into units of representation that perform the relevant categorizations. Although the set of minimal descriptors P has seven distinct elements, only three units carry all the information required to categorize the exemplars: {a,b,c},{d,e}, and cf}. If the categorization task had been different-for example, if Object 1 = {a,b,c,f} and Object 3 = {a,d,e,g} had formed a category (see Fig. 2, Object 3)-three other units, {a,d,e}, {b,c}, and {g}, would have performed the relevant categorizations. As this example and that of the desk lamp illustrate, perceptual and conceptual components do not necessarily have a one-to-one relationship. That is, a unit of representation may be composed of one or many perceptual components. However, a further constraint is that a conceptual unit must be composed of a contiguous fragment of a stimulus. Not every subset of a stimulus can be considered a fragment: Clearly, disparate portions of an object such as the nose plus the feet can hardly be perceived as the same fragment. Spatial contiguity of primary elements seems to be one crucial requirement of fragments. Furthermore, usually there must

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P = la. b, c, d,e,f, g)

d object 1 = {a, b, c, d, e )

a

b

e

f

d object 2 = (a, b, c, f ] unit = (a, b, c ]

object 3 = (a. e, d, g) unit = (a, e, d )

Fig. 2. This figure illustrates the interaction of perceptual and categorical consti-aints in learning new units of object representation. P = { u , b, c ' , d , e , f , g } is the set of primitive elements. Object I is composed of the elements ( u . b, c , d. e } . The arrows indicate the five parse points found by the minima rule. Object I and Object 2 (or Object 3 ) constitute a category. The figure illustrates that if Object 2 is composed of { a , b. c..f). the unit extracted to describe the category is different from the unit extracted if the second object is Object 3 = { u , P , d. g } . The dotted lines indicate the segmentations into conceptual units of object representation. This illustrates that categorical constraints may influence the parsing of an object.

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be some perceptual break at the fragment’s border, such as, for example, the local minima described by Hoffman and Richards (1985). One implication of the functionality principle, then, could be that units are created by composition of clearly distinguishable perceptual elements. This, however, implies that segmentation using perceptual principles such as the minima rule is unambiguous. The following example illustrates that this is not necessarily the case. In Fig. 2, possible parses are indicated by arrows at loci of curvature minima. Although the minima rule finds possible parse points, it does not pair them-but it is pairs of points that determine part boundaries. Hoffman and Richards (1985) suggested a heuristic rule for segmentation that connects together consecutive parse points (in Fig. 2, this rule would connect 1 and 2, 2 and 3 , 3 and 4, and 4 and 5 on Object 1). This technique, however, does not work for many objects. Other pairings (for example connecting 1 and 3 or 2 and 5) are necessary to account for extracting some parts of real-world and artificial objects (Kimia, Tanenbaum, & Zucker, 1989; Kurbat, 1994). This highlights a problem of the minima rule acknowledged by Hoffman and Richards (1985): The minima rule does not give all-or-none part boundaries. Although the minima rule is widely held as a robust principle of part segmentation (see, e.g., Hummel & Biederman, 1992), it appears that it does not in itself fully determine perceptual parts. Part extraction needs further constraints. The functionality principle could be one of these constraints (although we do not imply it is the only possibility). It predicts that categorical structure may group together fragments that would otherwise not be grouped. If a group of adjacent perceptual components categorizes objects, this may disambiguate the possible perceptual parsings indicated by the minima rule. The parts of the objects are those that the functionality principle combines into a single unit of representation in the alphabet-the cues to subparts could be ignored in the combination process. Thus, the functionality principle implies that if a conjunction of contiguous potential components categorizes objects, it is recoded as one primitive in memory. If adjacent perceptual components play the same conceptual role, it is not necessary to instantiate many separate conceptual parts when one unified part performs the relevant categorization. As units arise from perceptual and categorical constraints, they provide an efficient segmentation of the external world for object categorization. By themselves, perceptual constraints only indicate potential part boundaries, resulting in parts which may or may not be useful for categorization. Contrast categories influence this part formation because a fragment plays a role in categorization if it distinguishes a category from contrast catego-

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ries. Presumably, this principle underlies our representation of the desk lamp in Fig. 1. Although the lampshade could be divided further on a perceptual basis, a single unit was instantiated because these parts play the same role in categorization. The studies reported in this chapter examine the implications of the functionality principle for the instantiation of features in memory. In the first and second experiments, subjects viewed objects as a part of a category learning task. Components that were homogeneous throughout the category were expected to be instantiated as single units, and other components were expected to be rejected over the course of learning. The first experiment tested whether parts instantiated in one context of categorization would play a conceptual role and constrain possible segmentations in a new context. The second experiment investigated how the development of simple “partonomies” (part-whole hierarchies) could be induced by category learning. In the third experiment, we provided additional evidence that the parts subjects identified were based on the subjects’ true parsing of the objects rather than an artifact of the instructions or the parsing methodology. The last two experiments tested a generalization of the functionality principle to a case in which parts do not have the exact same shape across the exemplars of the category. These experiments also investigated a perceptual constraint on feature formation. Studying the ontogeny of features presents certain methodological challenges. Using familiar objects is obviously inappropriate because the theory concerns the effect of category learning on part analysis. However, most artificial categories used in previous experiments of this sort could not be used here either because it was necessary to avoid familiar parts as well as familiar objects. So, it would not do to teach subjects a new kind of furniture if they could already identify familiar features such as legs, seats, cushions, drawers, and so on. One would not expect to counteract subjects’ extensive prior knowledge of these parts in a brief experiment. Therefore, the first two experiments used three-dimensional (3D) spherical objects with mountainlike projections. Each projection or group of prqjections could serve as a potential part. The experiments examined the degree to which subjects’ perception of parts of an individual object could be influenced by learning its category and how the units acquired in this context would influence the extraction of new units in another category. The other experiments used two-dimensional (2D) objects composed of irregularly shaded areas, each of which could serve as a potential part. In each case, we examined the acquisition of novel features of these objects.

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11.

A.

Empirical Tests of the Functionality Principle

PARTIDENTIFICATION I N LEARNING ABOUT SINGLE OBJECT

A

As we described earlier, the interpretation of the features of an object may change depending on experience with the object’s category. In a preliminary experiment, we began with a more modest proposal that the perceived features may change depending on previous experience with the same object. That is, memory of the object can influence the parts that are identified in a given view of it. Across views, some portions of an object retain a constant relation, whereas others change. The ones that change may indicate a part distinction. For example, imagine an arm in a long-sleeved shirt that is extended at a right angle from the body. Most viewers would identify the arm to have three main parts: the upper arm, forearm, and hand. However, the shirt may hide any perceptual cue to the break between the upper arm and forearm. Thus, perceptual cues alone cannot explain part analysis; the judgment depends on memory of that object or objects like it. The preliminary experiment demonstrated that memory for the configuration of an individual object influences the part parse independently of perceptual cues. The stimuli of this experiment were simple tube- or pipelike objects, which had joints in them to form irregular shapes, as shown in Fig. 3. A naive analysis of one of these objects would probably identify each segment of the pipe, defined by the joints, as being a separate part. However, by exposing subjects to multiple views of each object (as in the figure), we found that they would violate this naive parsing in two ways. (Subjects identified parts using the same technique described in the next section.) First, they grouped as one part a number of distinguishable segments. This was accomplished by creating a “common fate” for a set of adjacent segments (the three left-most segments in the items of Fig. 3) such that their relations remained constant across configurations. Subjects identified these three segments as one part. Second, subjects created parts where there was no perceptual marking (i.e., no joint). This was accomplished by varying a joint in the stimuli (thereby indicating separate

Fig. 3. This figure illustrates the stimuli used in the pilot experiment.

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parts), and then testing on a stimulus that had no joint in that location (the right-most item of Fig. 3). Thus, in the test item, there was no perceptual cue to the parts. Nonetheless, subjects claimed that this straight line segment contained two parts because it had previously been shown to be bendable (analogous to the elbow covered by a shirt). This preliminary experiment provided important evidence that parts are not identified solely through perceptual cues. However, it was limited to the situation in which the same object is viewed in different configurations. If a new object is viewed for the first time, such influences would not come into play. In that case, however, the object's category might well influence t h e part structure that is perceived. The first two experiments considered this situation.

B. PARTIDENTIFICATION EXPERIMENT

IN

CATEGORY LEARNING:

1

This experiment examined the functionality principle and its implications through a two-part experiment that used categories of novel objects. The first phase tested the hypothesis that part instantiation of an object depends on the category it belongs to. Perceptual components that were homogeneous throughout the category (i.e., that were found in each member) were expected to be instantiated as a single unit, and other components were expected to be rejected over the course of learning. The second phase introduced a new category, which overlapped somewhat with the previous one. This allowed a test of the conceptual role played by the units learned in the first phase. The units from the first phase were expected to bias the extraction of other components in the new category. A group with no prior experience of the category served as a control. The control group expected to identify a different part structure, as described next.

I.

Mrthodological Issires in Part tdrntijication

In our experiments it will be necessary to try to discover what parts are represented in subjects' representations of the concepts. Unfortunately, there is no accepted technique to accomplish this goal that is appropriate for our stimuli. We will briefly describe other methods that have been used to identify parts and then justify our own choice. However, every technique has some drawbacks, and the conclusions will have to be taken with whichever grain of salt is most relevant to that method. Within the concept literature, researchers have primarily identified parts by asking subjects to list the parts of kinds of objects. In some cases, researchers have simply asked subjects to list features or attributes of the concept, and then have identified the parts or other types of attributes

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(e.g., Rifkin, 1985; Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). However, asking subjects to list parts in particular generally results in more of them being listed (e.g., Morris & Murphy, 1990; Tversky & Hemenway, 1984). The problem with this technique is that it relies on verbal coding of the part. For familiar stimuli, it may be that the conceptually represented units have familiar names like seat, wheel, roe, or branch. But for novel stimuli (as used in the present studies), parts never corresponded to a familiar object component. Thus, asking subjects to name the parts would have been problematic. Furthermore, it seems likely that the availability of familiar names influences the listing of parts independently of the actual representation. That is, subjects would probably be more likely to list parts that had simple names than those that required awkward, lengthy descriptions. (Indeed, this is a problem for the previous studies using this technique with familiar concepts, because there is no guarantee that less codable parts were simply not mentioned or were not described consistently by subjects.) Within the computational vision literature, there has not been a concerted effort to determine empirically what parts are mentally encoded (see the following exception). To a large degree, the effort has been toward constructing algorithms that identify the major components that correspond to the investigators’ intuitions (e.g., Hoffman & Richards, 1985; Vaina & Zlateva, 1990). Biederman’s RBC model is a kind of bridge between the computational and psychological literatures, and so might be expected to include a technique for identifying parts. However, the basic theory (Biederman, 1987)defines parts by principled means-its geometric primitives (called “geons”) were based on geometric constraints, rather than by empirically determining what subjects believed to be parts. So far as we can tell, experiments in support of the theory have not determined what parts people perceive in objects. For example, Biederman and Ju ( 1988) manipulated nonpart information only. Although Biederman and Cooper (1991) eliminated visual information relevant to identifying parts, this study did not actually determine what parts subjects perceived in the objects. Note that we are not criticizing these studies; our only point is that this research program does not help us to find a technique by which subjects identify parts. The technique we have used is a delineation method, in which subjects draw outlines around the parts of each object (using a computer mouse or a pen). This technique has an advantage over the verbal listing that has been the predominant one in the concept literature because it does not require codability of the parts. Thus, subjects can indicate any fragment of a stimulus they wish, whether it has an easily expressible name or not. And, of course, it is an entirely empirical measure, rather than being

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derived by a given theory of part construction. Braunstein et al. (1989) used a very similar technique in their Experiment 3 . Their first two experiments used a forced-choice method in which they presented stimulus fragments and asked subjects to choose one that was a part of a previously seen object. This method would not be appropriate for our studies because we wanted subjects t o select whatever parts they wished, rather than offer them a small number of choices. Furthermore, Braunstein et al. found that the delineation method gave the strongest evidence for use of the perceptual minimal rule. Because we are looking for evidence that the minima rule can be overruled, it will be especially impressive if we can do so with the same kind of technique. The drawbacks of this technique are those associated with any direct questioning about a given conceptual structure. First, we are assuming that subjects have some access to the units that represent concepts-that they know (at least roughly) what the parts are that make up their conceptual representations. Second, we are assuming that the measure is not influenced by biases and preconceptions of what kinds of responses should be given. To be honest, we doubt that either of these assumptions is always true. N o doubt there are conceptual units that are not accessible to conscious access (though see Dulany, 1991), and probably every measure is subject to biases of various kinds. However, given the kinds of objects and parts used in these experiments. and given the instructions to subjects, we will argue that there is no obvious bias that accounts for the pattern of results. We shall further address the limitations of the method, and the consequent limitations on the results, in Section IV. So far as we know, however, these limitations are at least equally true for other measures of part identity in the literature.

2. Phuse I Subjects were randomly assigned to one of two conditions in which they were exposed to exemplars of a particular category, A or B. Category A was a collection of objects on which a specific part ( a )was always present. Category B was also a collection of objects, but they were characterized by a different consistent part (b). We recorded the parts subjects considered important before, during, and after category learning. Subjects delineated parts on objects that had either part a or b. The functionality principle predicts that the parts subjects select after exposure to a category should be the ones that become encoded into the category representation. Therefore, subjects should pick part a if they are in condition A, and part b if they are in condition B. Also, the number of nontarget parts subjects pick should decrease as they gain more and more experience

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with the category. A corollary of functionality is that subjects should not consider as relevant parts those that do not glue a category together. Thus, if subjects from group A were exposed to an object from group B, they would not pick part b as a relevant part. Twelve undergraduates were assigned randomly to condition A or B, with the constraint that the number of subjects be equal in each condition. a. Stimuli. “Martian rocks” were synthesized 3D gray-level shaded objects displayed on the screen of a graphic mini-supercomputer (Stellar GS 1000). Each stimulus was generated on a Lisp Machine using the SGeometry package. A Martian rock was created from a sphere by specifying eight equidistant regions along its equator. Each region covered a surface extending from the equator to the two poles of the sphere, and deformations were applied to each region. A deformation consisted of pulling out a facet of a region for a certain distance along the surface normal at that point. The deformations were deterministic for the target parts, whereas the seven remaining regions were randomly distorted. The top pictures of Fig. 4 show the deformations that created unit N and unit b, and the bottom pictures present the same parts in the context of actual stimuli. Random Martian rocks were also created by randomly distorting ufl the regions. We will call the objects in the category being learned the “MI” rocks.

6 . Initial Delineations. Before experiencing the objects of their category, subjects were asked to delineate the parts they thought were important on one MI rock. Subjects were instructed to delineate at least part per object, but no upper limit was specified. The purpose of this stage was to test for the initial salience of the target parts. Part delineation was done with a computer mouse. Using the left and right buttons of the mouse, subjects could rotate the object to the left or right (around the vertical axis) by 2-deg or 20-deg (the middle button of the mouse served as a switch between 2 deg and 20 deg of rotation) increments until the part they wanted to delineate was correctly centered with respect to a vertical bar in the middle of the screen. Then, subjects moved a cursor on the screen with the mouse to mark the outline of the part they had just centered. By clicking the mouse, subjects extended a red line from its last vertex to the current location. They could indicate any shape they wanted by this technique, regardless of the “true” outline of the part. The specific instructions for part delineation were as follows: “Your task is to pick the part(s) that you think are relevant to the MI rocks. You should pick at least one part per object. Some of the part(s) may be complex or irregular.” (The request for “relevant” parts was to ensure that the parts subjects identified were part of the concept representation,

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Fig. 4. This figure illustrates the stimuli used in Phase 1 of Experiment 1. The top pictures show part a (left) and h (right) in isolation. The bottom pictures show examples of the complete objects. During the experiment. subjects could rotate the object to center the part they wanted to delineate.

rather than parts of that specific object only. Because every object had numerous projections, there were many possible perceptual parts and part combinations that could have been circled. Braunstein et al., 1989, achieved a similar result by restricting the number of parts subjects were to select.) Subjects indicated that they were finished marking a part by pressing a specific key on the computer keyboard. Once one part was delineated, they could either center another part of the object or quit part delineation for that object.

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c . Learning Stage. The learning stage was divided into six trials. On each trial, subjects first saw an MI rock. This rock rotated once to the left and then to the right around the vertical axis. The total exposure time of each stimulus was 24 sec (12 sedrotation), with a rate of rotation of 5 framedsec. Subjects were told to study this rock to learn what M1 rocks were like in general. Then, this stimulus was replaced with another rock on which subjects were instructed to delineate parts as described earlier. For five out of six delineation rocks, this item was an MI, and the other delineation rock was from the category of the other condition (category B for subjects in group A, and vice versa).

d . Test Stage. To ensure that subjects learned the category, acategorization stage with six test rocks was conducted. Two of the test rocks were M1 rocks, and the other four were random rocks. One test rock at a time would rotate-once left and once right, as in the learning trials-on the screen and subjects had to decide, by pressing a key marked either yes or no on the keyboard, whether the rock was an MI. If subjects were correct on all six items, the first phase of the experiment was over. Otherwise, they repeated as many learning blocks as needed to perform the test stage without mistake. No feedback was given during this test. 3 . Results and Discussion of Phase 1

We constructed two grids of points in the exact configurations of part a and part b. The measure of subjects’ accuracy in part identification was the number of points of the target parts contained within their delineations. Fifty percent or more of the target’s surface had to be delineated to score the subject as having identified the target. To test the a priori salience of the target parts, we collected the total number of part delineations in the initial delineation stage (before learning). Across subjects, a total of 44 parts were initially delineated; only one of them met the criterion for being a target part. Thus, the targets were not salient units before learning. To test the first prediction of the functionality principle, we considered the last learning block for all subjects (the one preceding a successful test stage). We computed the average number of points included in the delineation of the last target part (the MI part), and compared it with the average number of points in the delineation of the part from the other condition (also from that block). Subjects delineated 93% of the target from the category they learned, but only 8% of the target from the other category on average, t ( l 1 ) = 11.88, p < .001. The corollary of functionality was tested by recording the number of nontarget delineations per object for all subjects. We expected the number of nontarget selections to decrease with learning. Thus, we divided each

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subject’s learning stage into five sets (because each subject saw five M1 rocks for delineations per learning block, the total number of M I objects seen in learning was always a multiple of 5) and counted the number of nontargets delineated in each set. The number of nontargets decreased significantly from the first to fifth set, F(4.44) = 18.63, p < .OOl (means of 2.78, 2.09, 1.68, 0.70, and 0.21). A contrast showed that this decrease was linear, F(1,44) = 73.59, p < .001, and there was little remaining variance, F(3,44) = .32. The results confirmed the two predictions of the functionality principle. The conjunction of perceptual constraints and category learning provide information to instantiate potential parts. Before the experiment, subjects virtually never selected the target part; but by the end of learning, subjects picked the part that was redundant throughout the category. The corollary of functionality is that as subjects gain more and more experience with the context of one category, they should progressively reject infrequent potential parts as plausible candidates for a conceptual unit status. This prediction was corroborated with a linear decrease of the nontarget delineations. The number of nontarget delineations helps to rule out an uninteresting explanation of the results. Perhaps subjects’ focus on the crucial part (predicted by the functionality principle) is due to the instructions, giving the impression that only one or a very small number of parts should be selected. However, in the first set of stimuli, subjects chose 2.78 nontarget parts, in addition to their choice of the target part. Thus, subjects clearly felt no compunction about selecting a number of parts for each stimulus. Because they received no feedback whatsoever about the part identification, there was no need for them to eliminate these nontarget parts or to select the target part. Yet, with category learning, this is just what they did, as predicted by the functionality principle. Thibaut (1991) independently reported that the segmentation of a stimulus was also influenced by the stimuli forming a category (see also Hock, Tromley, & Polmann, 1988). These results show applications of the functionality principle in a situation of categorization where no incremental learning of units is involved: All the stimuli of the category are simultaneously presented to subjects, who then segment the objects according to functionality. If the unit instantiated in one particular context has a conceptual role, it should be used to describe objects of a new category. Once instantiated, these units have a status similar to nose, wheel, handle, and so forth. That is, they can be used to characterize new objects. The purpose of the second phase of the experiment was to test this implication of the conceptual role by presenting the same subjects with a new category of objects

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in which a number of potential parts were distributed throughout the category, with either one or none of them having been already instantiated in Phase 1. 4.

Phase2

Subjects from both groups of the first phase were exposed to a new group of objects, category C. All objects of Phase 2 were characterized by part a and part b adjacent to one another. In addition to groups A and B, a new group of six subjects, group C, who did not participate in Phase 1 , served in Phase 2. New Martian rocks were created in which part a and part b were adjacent to one another and only six regions were random. Figure 5 shows different views of a Martian rock from this phase. Phase 2 took place immediately following Phase I for groups A and B. As in the previous phase, subjects went through three main stages: an initial delineation stage, a learning stage, and a testing stage. Subjects again went through six trials in the learning phase, and they had to delineate five rocks of category C (the remaining rock was a random rock). In the test phase, six rocks were presented (two of them with part a only, two with part b, and two with parts a and b ) , and subjects had to correctly identify the members of category C to end the experiment. No feedback was given during the testing phase. 5. Results and Discussion o j Phase 2 We recorded the number of times part a or part b was selected in the initial part delineation before learning. The subjects of group A always delineated part a , and never b. The subjects of group B always delineated 6 , and never a . On the final learning trial, the subjects from groups A and B all delineated parts a and b as two distinct units, whereas all the subjects from group C (the group with no prior experience with Martian rocks) delineated parts a and b as a single unit (see Fig. 5). This study investigated the role of previous knowledge in parsing new objects. The results showed that the units instantiated in the first phase were applied to categorize objects of category C. This shows that they played a conceptual role in the sense explained earlier. The effect of learned units on part extraction is also illustrated in the second phase. Specifically, subjects with prior knowledge ended up instantiating two units: the one they had learned in the previous phase, either a o r b , and the other part adjacent to the one they knew already. In contrast, subjects from condition C did not segment a and b into two instantiated units. Instead, they considered a and b to be one single unit c . This is interesting for two reasons. First, a and b adjacent to one another can clearly be

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Fig. 5. This figure illustrates the stimuli from Phase 2 of Experiment 1. Subjects in groups A and B delineated the two parts centered in the top pictures. Subjects in condition C selected the part centered in the bottom picture as one unit (the delineations are not shown on the picture). This part is a con.junction of the two parts shown above.

segmented into two units-as shown by the other subjects. This means that the two parts are separable on a perceptual basis (furthermore, it means that nothing in the instructions or procedure forced subjects to select a single part once they had learned the category). In fact, like the lampshade of Fig. 1, a and h could be segmented by applying Hoffman and Richards’ minima rule algorithm. Subjects in group C apparently encoded c as one part rather than the two units a and b, which were not known as such to the subjects of condition C. This leads to the second point. The context of a category of objects may influence what is considered a feature in an object representation.

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The context enables the selection of a perceptual interpretation that maximizes the homogeneity of a category. Because parts a and b were always present in the objects in category C , there was no reason to segment them into two separate parts, unless they had already been distinguished. This last result has far-reaching consequences for understanding the development of a conceptual system. Potential parts become conceptual features if the right categorical context offers them a functional or conceptual role. This provides, among other things, a theoretical framework within which the development of part-whole relationships can be understood, as well as the circumstances under which partonomies are constructed in conceptual representations. The next experiment investigates this implication. OF A HIERARCHY OF PARTS:EXPERIMENT 2 C . DEVELOPMENT

Objects do not have a single level of part analysis; rather, parts can be structured hierarchically such that the major parts can be broken down into smaller parts, which can be broken down into still smaller parts, and so on (Brown, 1976; Cruse, 1986; Tversky & Hemenway, 1984). In some cases, the existence of multiple levels of part structure may be apparent simply from the perceptual analysis of an object. But in other cases, the existence of subparts and their relations to the larger part may be determined by categorical experience. The second experiment tested whether learning a category that used a subpart could cause the reanalysis of a larger part that had already been identified. The experiment involved three phases. In the first phase, a category of stimuli was presented in which each object contained parts a and b adjacent to one another. We expected that subjects would select these as a single unit, as in the previous experiment. The second phase introduced a new category. In conditions A and B, a single fragment ( a and b, respectively) of the part learned in the first phase appeared in the objects of the new category. In condition C, a different fragment, which did not play a conceptual role in Phase 1, defined the category. This second phase, then, either revealed a subpart of the part formed from the first phase (for groups A and B), or did not (group C). The third phase investigated the effect of this new experience by testing subjects on an exemplar repeated from Phase 1. We expected that parses would reflect the learning that occurred in Phase 2-namely, that the coherent whole would be divided into subunits if its perceptual components had acquired a conceptual role in Phase 2. I . Phases I and 2

Phases 1 and 2 were intended to induce a specific state of knowledge in three different groups of subjects. (The effects of this knowledge were

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tested in Phase 3.) In each phase, the procedure was exactly the same as in the previous experiment: After an initial delineation phase, subjects went through as many learning and testing stages as needed to categorize the test stimuli correctly. Because these two phases are essentially replications of Experiment I , we will not dwell on them. In the first phase, 12 Brown University students viewed stimuli (also used in Experiment I , Phase 2) characterized by parts a and b adjacent to one another. Consistent with the results of Experiment 1, these subjects without any prior experience selected a and b as a single part. All subjects selected 50% or more of the part, and on average, they delineated 90.3% of the target part, indicating that they instantiated this part as one unit of representation. Phase 2 took place immediately after the first phase. Subjects were randomly assigned to group A, B, or C such that there were three, three, and six subjects per group, respectively (the data of Groups A and B were to be combined). Groups A, B. and C were exposed to a new category of objects in which parts a, h, and c defined the category, respectively. Parts a and b were subparts from the previous phase, whereas c was a new part of the same size. The results showed that in each group, the target part was on average delineated above the 50% criterion (A = 91%, B = 66.4%, and C = 89%). However, one subject in Group B did not pass the criterion, as she circled only 34% of the part. The subject was retained, however, because she had apparently learned the category (as shown by passing the categorization test) and therefore must have learned some aspect of the relevant part.?

2.

Phase 3

The third phase introduced a single exemplar from the Phase 1 category so that it contained parts a and b adjacent to each other. Here, subjects were simply asked to divide the object into its relevant parts, as they were in Phase 1. We recorded the number of times part a , part b, or parts a and b were delineated, grouping together the results of groups A and B and comparing them with those of group C.

'

Other subjects were eliminted from the experiment because of an inability to learn the categories. One subject never passed the learning criterion in Phase I and another could not learn the category in Phase 2. Also. two subjects parsed the part of Phase 1 into two units, and so their learning in Phase 2 could not have revealed any differences as a result of their experiences. They were also eliminated.

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3 . Results and Discussion of Phase 3 All six subjects in group C selected a and b as the only part in Phase 3 , repeating their parses from Phase 1. However, all six subjects in groups A and B now indicated a smaller part in their responses, as a result of their learning in Phase 2. Five of these subjects still included a and b as a part as well-that is, in one delineation they circled a and b together, and on another they circled one or more of its subparts. Thus, the set of parts they selected suggests the beginning of a partonomy developed through category learning. A chi-square test of association showed that there was a significant association between group (A and B vs. C) and the type of delineation(s) made ( u und b o r a smaller part), xz (1) = 5.71, p < .025. That is, only groups A and B chose subparts. These results show that successive exposure to different contexts of categorization can induce observers to divide a coherent whole into parts. The unit learned in a specific categorical context is later segmented if its potential parts start playing a conceptual role in another context. These results were observed for groups A and B, where the unit a and b initially extracted was later divided after the conceptual instantiation of one of its components in the second phase. In the third phase, subjects also indicated their perception of a part-whole relationship by circling u and b as a coherent whole while simultaneously delineating one or two of its components. Another interpretation of these results is possible. In the second phase, subjects could have realized that parsing may occur at another scale from their segmentations in the first phase (recall that part n and part b are one-half the size of the composite part u and b). Therefore, subjects’ division of part a and h in the third phase could indicate a change in scales of segmentation instead of an indication of part-whole relationships. The results of group C rule out this alternative interpretation. The part they learned in Phase 2 was the same size as part a and part b. Thus, if group A and B’s final parsings indicate a change of scale, group C should also have segmented a and b into two parts. However, none of these subjects selected a subpart. This experiment indicates how simple hierarchical representations of objects and different degrees of conceptual refinement may be derived. This is important because it generalizes the principles of part instantiation to more complex representations. Rosch et al. (1976) claimed that experts form many more specific categories than novices do. They argued that experts are able to make finer segmentations because they have acquired more specific features than novices (see also Schyns, 1991; Tanaka &

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Taylor, 1991). In classical accounts of object recognition and categorization, the problem of how these relevant units could be arrived at, starting from perceptual data, is not addressed. The functionality principle can account, in part, for the development of hierarchical relationships and conceptual refinement: The necessity to categorize objects causes the instantiation of a unit that breaks an unsegmented whole into parts. This principle can be applied to every level of part analysis, from the largest, most basic structure to the fine details.

D.

CONVERGING OPERATIONS ON PARTSTRUCTURE: 3 EXPERIMENT

One possible concern about t h e previous experiments is whether the part delineation task gives an accurate estimate of subjects’ perceptions of the part structure. For example. one might think that even though subjects see a component of an object as two units, some other consideration causes them to delineate it as a single unit. Perhaps this delineation is an artifact of the instructions or of subjects’ interpretations of the circling task, rather than a reflection of their true part analysis of the items. Because this technique is relatively novel, it seems important to validate it. The goal of Experiment 3 was to provide converging evidence for the conclusions based on the delineation method. In particular, we used categorization and typicality ratings in addition to the part delineation to discover whether previous experience influences part identification. This experiment used two-dimensional “Martian rocks” that had six subparts. Two of the subparts recurred throughout the exemplars. As shown in Fig. 6, these parts could appear adjacent or nonadjacently. The Joined group viewed category exemplars in which the two subparts were always adjacent; the Separated group viewed exemplars in which the crucial subparts were usually nonadjacent. In both groups, the position of the subparts within the object varied across exemplars. After learning, both groups categorized new exemplars that contained the joined subparts, the separated subparts, and each subpart alone. Subjects also rated the typicality of each figure. The primary question was whether subjects who always saw the subparts joined truly encoded them as a single part. Our prediction was that subjects in the Joined group would be much less accepting of the figures with a single subpart or with separated parts than subjects in t h e Separated group would be. That is, we expected that the part configuration would influence not only delineation of the parts, as in past studies, but also standard dependent variables of categorization studies: categorization decisions and typicality ratings.

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Fig. 6. This figure illustrates the stimuli used in the joined and separated conditions of Experiment 3.

1.

Method

The subjects were 24 undergraduates who were randomly assigned in equal numbers to the two stimulus types. The learning stimuli consisted of two sets of 20 exemplars like those shown in Fig. 6. Each exemplar consisted of six irregularly shaped black areas extending from a center. Two of the irregular shapes (subparts A and B ) were common to all members of the set. In the Joined set, these two subparts were always adjacent. However, their particular location varied across exemplars. In the Separated set, the two subparts were adjacent in only 4 of the 20 items, and two of these items had the subparts in the opposite relation as in the Joined condition (i.e., instead of A to the left of B , A was to the right). Thus, the two subparts were essentially independently positioned in the Separated set. The other subparts in each items were arbitrarily constructed areas that did not repeat across the items.

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The test stimuli consisted of eight new figures, each printed on a separate page. In one figure, the subparts occurred adjacent to one another (in the correct configuration for the Joined group-A to the left of B ) . In another figure, the two subparts occurred separated. In two more figures, each subpart occurred without the other subpart. For each of these test figures, the other subparts were again arbitrarily constructed areas that were not repeated. The other four test items were foils that had none of the crucial subparts. The figure with the two subparts adjacent was presented first, so that seeing the subparts separately in the other test items would not influence the results on this crucial item. The order of test items was the same for all subjects. Subjects read instructions describing the same Martian rock cover story as in the previous experiments. The instructions told subjects “Your task is to look for the part or parts that you think is/are relevant to the MI rocks. After the learning session, y o u will see some test rocks and you will have to decide whether each one is an M I , and how good an M 1 it is.” Subjects were required to look at each picture, but otherwise were not constrained in studying the pictures, until they felt they had learned what the MI category was. Immediately after this, the subjects read instructions for the test phase. In the test, subjects went through the items one at a time and were not allowed to return to previous items. At the bottom of each page containing a test figure was the sentence “Is this object an example of an M I rock‘?’’ and subjects had to check yes or no underneath it. If they responded that the example was an MI rock, then they rated the typicality to the MI category underneath. The scale (which was also printed on each page) was from 0 to 10, where 0 meant “terrible example” and 10 meant “best possible example.” Subjects were instructed that after making this rating, “You should indicate what part or parts of the object were relevant to your decision. To d o this, simply circle with a pen the relevant part. Make a separate circle for each part, if there are more than one.” Subjects carried out this delineation only if they responded that the rock was in the MI category. Also, at the beginning of the test phase, the experimenter urged subjects to make a separate, accurate circle for each part. 2.

Results

The foils were included primarily to allow subjects to make negative responses, and there were no predictions involving differences between the two groups. In fact, subjects in both groups responded yes to the foils only 12% of the time, indicating fairly good learning.

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The first question to be asked is whether the part delineation results replicated those of the earlier studies. That is, when subparts A and B occurred next to one another, how did the two groups of subjects parse them? In the Joined group, 11 of the 12 subjects identified the figure as an M1 rock, and all I 1 selected the conjunction of A and B as a single part. (In fact, the 12th subject, who did not identify the figure as an M1 rock, also circled the conjunction as a single part even though he was not required to circle anything.) In the Separated group, all 12 identified the figure as an M1 rock, but only 1 of the 12 subjects combined the two parts in one unit. Of the I 1 remaining subjects in this group, 10 circled both A and B as separate parts. Thus, these results clearly replicate those of the previous studies: Viewing two components adjacent to each other causes subjects to identify them as a single part. To analyze the typicality and categorization decisions of the figures, we derived a combined “goodness” score for each subject’s rating of each object. If the subject categorized the object as an MI rock, then his or her rating for that object was the score. If the subject rejected it as a member, then the response was scored as a 0. This score ranged from a maximum of 10 to a minimum of 0. (Because the level of categorization was low for some figures, it was not possible to analyze only the typicality of items that were accepted as category members. However, tests of categorization choices alone had the exact same pattern of significance as tests of the score reported here.) For the first test item (whose delineation results were just described), with A and B adjacent, the mean scores were 7.6 and 7.0 for the Joined and Separated groups, which were not significantly different, t(22) = -.63. Thus, for an item that should be perfectly acceptable for both groups, there is no difference between the groups. In contrast, the item with A and 5 both present but not adjacent should be acceptable for the Separated group but not the Joined group. And in fact, the mean rating was high for the Separated group, 6.25, but low for the Joined group, 1.75, t(22) = 4.71, p < .001. All but one subject in the Separated group accepted the items as category members, but only 5 of the 12 Joined group did. Thus, the Joined group apparently did not encode A and B as separate units. The two subparts A and 5 occurred separately in the final two test items. However, there was apparently a difference in how salient these two subparts were. Subpart A received a low score in both groups, but the Joined group was still reliably lower, as predicted (means of .33 and 2.0), t(22) = 2.1 I , p < .05. Subpart B was the more salient one, and the figure with it alone was recognized to a moderate degree by members of

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both groups, 3.3 and 2.8 for the Joined and Separated groups. This difference was not reliable, r(22) = -.46.

3 . Discussion Subjects in the twogroups viewed t h e same two subparts the same number of times. The only difference was that subjects in the Joined group saw these components in the same adjacent configuration in each item. As a result, they delineated these components as a single unit. in contrast to the Separated group, which separated them into two different units. But this delineation was now accompanied by additional measures of how subjects perceived figures in which the subparts were separated. When both subparts were present but were not adjacent, the Joined group gave a much lower categorization score than the Separated group. This demonstrates that the delineations by this group were not simply an artifact of the circling task-when the two subparts were divided, subjects no longer found the part that had been common to the M1 category they had learned. Of course, there was some similarity between this item and the learning set, because the separated parts were somewhat similar to the united components that were in the learning items. However, fewer than half of the subjects in the Joined group admitted this as acategory member. Similar results were found when the test item included only one of the subparts. In one case, the Joined group had a reliably lower score than the Separated group. In the other case, both groups had moderately low scores that did not differ significantly. An interesting comparison one could make between the two groups is whether the test item with adjacent subparts is different from the item in which the two subparts were separated. These items differ only in the potential for unitizing the two subparts into one whole part. For the Joined group, the two means, 7.6 and 1.75, were very different, t(l1) = 6.13, p < .001. For the Separated group, the two means, 7 and 6.25, were not significantly different, r ( l 1 ) = .8S. In short, subjects’ categorization decisions reveal that the two subparts are apparently not perceived as separate parts by the Joined group, whereas they are by the Separated group. These results illustrate that what constitutes an encoded part may be subject to interpretation. We saw that the context of a category of objects may force the interpretation of the same perceptual break as a part descriptor (in the Joined group) or as a part boundary (in the Separated group). The results demonstrate that the part delineation task we used in Experiments I and 2 predicts subjects’ categorization decisions. That is, rather than being an artifact of the instructions or subjects’ interpretation of the circling

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task, our finding that experience with category exemplars can influence part identification seems to have strong consequences for categorization and typicality ratings as well.

111. Shape Variation and Part Extraction

A.

USE OF THE FUNCTIONALITY PRINCIPLE WHENSHAPE VARIES:EXPERIMENT 4

In the experiments just described, subjects were exposed to a category of synthesized objects. On each object, a fragment of the surface was always exactly the same (e.g., part b was identical across exemplars). However, parts in object categories are rarely, if ever, exactly the same throughout the category. Consider for example the part leg. Even in the restricted domain of the animal kingdom, legs vary widely along a number of dimensions, including length, width, and relative proportions of thigh and calf. Within the same category of objects, a part can vary dramatically in shape, but people can identify the part in spite of this variation. This experiment will test the functionality principle in the context of a part that varies across exemplars. The question of part variation is quite complex. Some parts (such as w h e e l ) may change shape and still be called a wheel to the extent that their function is preserved by the transformation; a square wheel could not roll and so would lose its wheel identity. Similarly, if a handle were transformed to something difficult or impossible to grasp, it would also lose its functional identity. Here, we are not interested with the issue of functionality preserving transformations, which presumably require detailed world knowledge (Murphy & Medin, 1985). Instead we address part transformation when parts have not yet been assigned any functional role that would constrain their shape. Variation in part shape makes identification of a single part across examples difficult. Are certain variations easier to recognize than others? Would our perceptual system see invariants through certain variations of some parts but not others? Richards, Dawson, and Whittington (1988) suggested that extrema of curvature of an object’s 2-D outline convey relevant information about the object’s shape. Extrema of curvature are of three types: positive, negative, and zero. Formally, extrema of curvature occur where the tangent to a curve rotates at the greatest rate. More concretely, a local minimum of curvature is typically associated with a concavity-a valley-and a local maximum with a convexity-a bump. When following the curve from a maximum to a minimum, the rate of

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change of its tangent goes from a high positive value to a high negative value. The point at which the rate of change equals zero-the inflection point-is a zero of curvature. We saw earlier that minima of curvature could be useful for part segmentation. Maxima and zeros are also interesting for describing the silhouette of objects, because such extrema of curvature of 2D outlines are invariant under rotation, translation, and uniform scaling (see Richards, Dawson, & Whittington, 1988, for a more thorough presentation). Because these extrema of curvature are invariant over such quantitative changes in scale, they form a qualitative description of an object’s outline. If people use such a qualitative description to make perceptual judgments, parts that could be subsumed into a single unit of representation should be those with a similar sign of curvature description. To illustrate the point, consider the silhouette of a sedan seen from the side. Bumps in the outline would correspond to the hood, roof, trunk, and wheels. There would be minima separating the trunk from the roof, each wheel from the body, and so on. Even though the trunk and hood can vary in size in different cars (a quantitative change), the extrema of curvature of its outline (the qualitative description) is constant across most sedans. However, changing the curvature (e.g., making a bump into a valley or a straight line) would change the qualitative description by changing the sign of curvature. The following experiment (Experiment 4) had two main goals. The first was to demonstrate that subjects can use the functionality principle to abstract features from category exemplars even when their shapes vary. The second was to test the sign of curvature as a perceptual constraint on part learning. The stimuli of the experiment were 2D outlines of “Martian rocks” composed of a target part and a random part separated by a neck (see Fig. 7, top panel). There were two phases in the experiment. In the first, subjects viewed different exemplars in which the target part varied. In the second phase, we tested subjects on previously seen and unseen transformations of the test part. We expected that a transformation preserving the sign of curvature (Fig. 7, middle row, left two pictures) would facilitate part extraction, whereas a transformation changing the sign (Fig. 7, middle row, right picture) would complicate part extraction. This was accomplished by creating three categories of objects named concave, convex, and mixed. We expected subjects who learned the concave and the convex categories (the ones without a change of sign of curvature in the part) to categorize accurately new and old exemplars, and to demonstrate a prototype effect in the testing phase. Conversely, subjects in the mixed condition should have more difficulty in forming a representation of the target part, and therefore should perform poorly in categorization. We did not ask subjects to delineate parts here because the question was not

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Fig. 7. This figure illustrates the stimuli used in Experiments 4 and 5 . The top picture shows the cubic B-Spline from which the categories were generated. The control points numbered 16, 17, 0, I , 2 . 3. and 4 were used to create the random part, and control point number 10 was translated to generate t h e target part. The middle pictures illustrate the set of transformation experienced during the learning phase of Experiments 4 and 5 . The middle picture represents, from left to right, the training exemplars of conditions convex, concave, and mixed. The set was obtained by superimposing the learning exemplars. The bottom pictures show the corresponding test items used in Experiments 4 and 5.

where the parts were, but whether they were the same part across shape variation. 1.

Method

Eighteen subjects were randomly assigned to the three category types, with the constraint that the number of subjects be equal in each condition.

a . Stimuli. Two-dimensional Martian rocks (M2) were generated using Uniform non-Rational B-Splines (URBS) on a Silicon Graphics cornputer. A B-Spline is a form of parametric cubic curve that approximates

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a set of control points. The shape of a B-Spline is transformed by moving its control points. Each Martian rock was generated with a total of 21 control points; 18 of the points are shown in the top section of Fig. 7. (To obtain a closed cubic curve, the remaining points, not visible on Fig. 7, were always duplicates of the three first points.) Each object consisted of a random and a target part separated by a “neck.” The points controlling the shape of the random part (labeled 16, 17, 0, 1, 2 , 3, and 4 on Fig. 7, top panel) could vary within a certain region, with the constraint that they not cross regions. To illustrate, imagine that the part was divided into eight pie slices, with each control point at the center of one slice. In learning exemplars, each control point was located according to a random selection of length and angle, with the constraint that it not leave its slice. The point controlling the shape of the target part (labeled 10 in the figure) was slid horizontally inward in the concave condition, whereas it was moved symmetrically outward in exemplars of the convex condition. This point was moved by successive constant increments in the two categories, augmenting the slope of the curves, while keeping their sign of curvature constant. The middle panel of Fig. 7 shows the transformations of all exemplars simultaneously by superimposing them. Each real exemplar consisted of only one such transformation. As can be seen, the random (left) part has little apparent regularity across exemplars, whereas the target (right) part varies according to the transformations just described. A similar procedure was applied to generate exemplars in the mixed condition, but the control point was moved both inward and outward, resulting in a change of sign of curvature. In all three categories, the total amount of displacement of the control point was identical. Note that the center of the part transformations (the prototype of the part) was never presented during training. Test items were constructed from items seen during learning and new part transformations. One type of transformation, called compatible, had the same sign of curvature as the transformations in the learning set (i.e., convex transformations in the convex category, concave in the concave category). The range of compatible transformations was greater than that of the transformations experienced during learning: The relevant control point was slid inward or outward by one increment from the innermost or outermost learning transformations so that it would be outside the range of the learning transformations (i.e., one transformation was smaller and one was larger than in learning). Another type of transformation was incompatible with the transformations that generated the parts in the learning set (i.e., convex parts in the concave category, and vice versa). The mixed category only had consistent transformations, as it contained both convex and concave parts. The final test item type was the category

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prototype, consisting of the center of all the transformations used to create the learning items. The prototypes were not shown during learning. To illustrate the test stimuli, consider the convex condition (the leftmost picture of the bottom panel of Fig. 7). Five test exemplars are shown superimposed, each corresponds to a different location of the control point. The left-most and right-most outlines are extrapolations compatible with the training set. Next to them are old exemplars, and, finally, the center outline is the interpolation of the training set, the prototypical part never shown during learning. There were 40 test items, 20 true M2 rocks and 20 false items. The 20 true items consisted of 8 old, 8 consistent, and 4 prototype items. For the concave and convex categories, the false items consisted of 4 incompatible transformations of the part, plus 16 other items in which both the target part and the rest of the rock was randomized. For the mixed items, there were no incompatible transformations, and so all 20 false items consisted of the latter type. b. Learning Phase. Subjects were instructed that they would see 48 members of the same category of Martian rocks called M2 and that they should study the rocks to learn what M2 rocks were like in general. Each of the six possible training variations appeared eight times in a random order. Each of these rocks appeared one at a time for 2 sec on the computer monitor. c . Testing Phase. To measure what subjects had learned about the category, a categorization task with 40 exemplars was conducted. One test rock at a time was randomly selected and presented on the computer screen. Subjects had to decide, by pressing either the yes or no key on the computer keyboard, whether the rock was an M2. No feedback was given during the testing phase and subjects were instructed to respond as quickly and as accurately as possible.

2 . Results and Discussion The quality of part representation should be reflected in categorization performance in the test phase, because the categories were defined by the presence of the target part and the true test items differed in variations of that part. On average, subjects from the concave, convex, and mixed conditions categorized their stimuli with 97%, 99%, and 79% accuracy, respectively. A one-way analysis of variance showed a significant effect of condition on the mean percentage of correct responses, F(2,15) = 11.65, p < .01. A post hoc, Fisher-protected t test (LSD = 13.0) revealed significant differences between the mean percentage correct of the mixed

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and the other two conditions ( p ’ s < . O l ) , but no significant difference between the convex and concave conditions. No subject categorized the extrapolations with change of sign of curvature as M2 rocks. To test for a prototype effect, we compared reaction times (RT) for each type of true test item. Mean correct RTs to old, new, and prototypical exemplars are summarized in Table I . A two-way analysis of variance revealed a main effect of condition, F(2,15) = 8.0, p < .01, a main effect of exemplar type, F(2,30) = 8.18, p < .01, and a significant interaction of condition and exemplar type, F(4,30) = 4.19, p < . O l . As shown in Table I, the mixed condition is slowest overall. But, more importantly, the prototype was the fastest item type for the convex and concave conditions, but was the slowest type for the mixed condition. The most important result from this study is the finding that people could identify outlines that varied in shape as being the same part. Subjects had no difficulty identifying what was common to the category when the part varied “in the same direction,” either concave or convex, but when the part crossed the perceptual boundary (i.e., when the sign of curvature changed), subjects were much less accurate and were slower at identifying the object as being in the same category. Because category membership depended only on the presence of this part, these measures indicate how easy subjects found it was to identify the part. This difficulty of learning the part can be indirectly observed in subjects’ responses to the prototype. In the convex and concave conditions, prototypical exemplars never experienced before were recognized as quickly as old exemplars. This result is compatible with a prototype representation of the part. Subjects in the mixed condition responded more slowly to their prototype, suggesting that they might have formed not one, but two

TABLE 1 MEANREACTION TIMESI N MILLISECONDS FOR THE CORRECT CATEGORIZATION OF EXEMPLARS I N T H E THREECONDITIONS OF EXPERIMENT 4

Old New Prototype Mean

Convex

Concave

Mixed

Mean

593 760 592 648

664 803 603 690

818 967 1054 946

692 843 750

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distinct representations of the part: one with a negative sign of curvature, one with a positive sign. Because the prototype in the mixed condition is neither concave nor convex, it is difficult to categorize, even though it is geometrically the middle item of the presented parts. In the concave and convex conditions, subjects never categorized test exemplars with different signs of curvature as M2 rocks. In contrast, extrapolations that preserved the sign of curvature were accepted as category members. Thus, although variation in the part did not prevent subjects from noticing the constant part, not all variation was perceived as the same part. It appears that the category context created one set of constraints, and the perceptual restrictions on changing sign of curvature created another.

B.

LIMITSON THE VARIABILITY OF EXPERIMENT 5

A

SINGLE PART:

The previous experiment suggested that when the common part of a category did not follow a perceptual constraint (keeping the same sign of curvature), subjects found it difficult to learn the category. However, it is not entirely clear how this was related to the extraction of a part or parts in the stimuli. One possibility is that when the sign of curvature was respected (in the convex or concave conditions), subjects simply learned that there was a single part, which varied in the amount of its curvature. In the mixed conditions, subjects may have learned that the category had two different parts (the convex and concave versions), each of which also varied in the amount of its curvature. If this was the case, then after category learning, subjects should differ in whether different versions of the part are perceived as the same or different, and this should influence the learning of new categories with these components. In particular, if the subjects in the mixed condition encoded two different parts, they could have easily learned two new categories that differed in the presence of these parts. In contrast, subjects in the concave or convex conditions would not have formed such a distinction and so would have difficulty learning new categories based on differences in part variation. In Experiment 5 , subjects first learned a single category of concave, convex, or mixed stimuli, as in the previous experiment. In a second phase, they learned to divide these stimuli into two subcategories: the three “left-most’’ transformations of the part versus the three “rightmost” transformations of the part (see Fig. 7). In the case of the mixed categories, this corresponded to parts with different signs of curvature (i.e., one subcategory had the convex and the other had the concave transformations). If subjects had already discriminated these two transfor-

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mations into two conceptual parts, then it would be very easy to learn the new categories because the categories could be distinguished on the basis of these two features. If they had not distinguished the transformations, then learning new categories should be difficult. We expected that subjects in the mixed category would have formed such parts, whereas subjects who had learned concave or convex transformations would not have already formed such parts, and so should find learning the subcategories more difficult. 1. Method

Subjects were 18 students who were randomly assigned to the concave, convex, and mixed conditions. The stimuli were the same as in Experiment 4. Uniform nonrational B-Splines composed of a target and a random part were transformed as explained earlier to generate the concave, convex, and mixed categories. Stimuli were displayed on the screen of a Silicon Graphics computer.

a. First Learning Phase. Subjects were exposed to exemplars of Martian rocks, some of them forming a category called M2. Subjects were told to study the rocks to learn what M2 rocks were like in general in order to categorize them correctly. The learning phase was divided into blocks, each consisting of 24 rocks, divided into 12 M2 and 12 random rocks. The M2 rocks were two repetitions of the six possible transformations of the first Phase of Experiment 4 (see Fig. 7, middle pictures). The random rocks were generated as explained earlier by randomizing the control points of the target part. By pressing either the yes or no key on the computer keyboard, subjects had to indicate the category membership of each rock. To reach criterion, they had to be correct on all 24 exemplars of a learning block. If they made a mistake, they continued the learning phase until reaching criterion. b. Second Learning Phase. The second learning phase started upon completion of the first learning phase. Subjects were instructed that in fact, M2 rocks could be subdivided into two subcategories M2-a and M26, and that they would learn to categorize M2-a and M2-b rocks correctly. The learning procedure was similar to the one of the first learning phase, but subjects saw only M2 rocks. A learning block was composed of 12 exemplars of M2 rocks, divided into six M2-a and six M2-b. In the mixed condition, M2-u rocks were two repetitions of the three concave transformations, and M2-b rocks were two repetitions of the three convex transformations. In the concave and convex conditions, the three left-most transformations were M2-a, and the three right-most transformations were

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M2-b. By pressing keys labeled M2-a or M2-b on the computer keyboard, subjects indicated their subcategorizations of M2 stimuli. As in the first learning phase, subjects had to go through as many learning blocks as needed to categorize the exemplars perfectly. Each block took about 5 min to complete.

2. Results and Discussion The mean numbers of learning blocks to reach criterion in the first and second learning phases are reported in Table 11. A two-way analysis of variance revealed a main effect of condition, F(2,15) = 11.46, p < .01, a main effect of learning phase, F(1,15) = 9.84, p < .01, and a significant interaction of condition and learning phase, F(4,15) = 25.18, p < .01. All three effects reflect the finding that subjects in the mixed condition required very many blocks in the first learning phase, which decreased dramatically in the second learning phase. In contrast, the other two conditions increased in the second phase. In Experiment 5, the difficulty of forming a part representation in the mixed condition was assessed by the accuracy of category judgments. The present experiment showed a similar effect with a different procedure. As expected, the average number of learning blocks required to learn the category was much higher in the mixed condition than in the concave or the convex conditions. It is worth reemphasizing that the total size of transformations in the three conditions was identical, so the difficulty of learning the mixed categories must result from the change in the sign of curvature of its transformations. But the main goal of this experiment was to test whether a change of sign of curvature would give rise to two distinct units of representation for the category. If this were true, subjects in the mixed group would enter the second learning phase with a category already segmented into two subcategories: one with the convex part, and one with the concave TABLE I1 MEANNUMBER OF LEARNING BLOCKS NECESSARY TO REACHCRITERION I N PHASE 1 A N D PHASE2 OF EXPERIMENT 5

Phase 1 Phase 2

Convex

Concave

Mixed

3.67 5.83

3.17 4.17

13 1.83

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part. Therefore, reaching criterion in this phase would be faster for the mixed group than for the other groups. This prediction was confirmed by the data. Although the mixed condition was by far the slowest in the first phase, this group learned the category in 3.1 fewer blocks on average than the other groups in the second phase, which was a reliable difference, t(16) = 2.15, p < .05. To summarize, the results give evidence of one kind of perceptual constraint on part construction when the parts are not constant across exemplars. Although the functionality principle predicts that potential parts present in all objects of a category should form one unit of representation, these results show that not all variations of a component result in a single potential part. People seem to expect that some aspects of the part will remain constant in order for it to be the “same” part encoded into memory; in particular, the extrema of curvature of 2D outlines of objects are expected to remain constant. If there is a change in the sign of curvature, subjects will instantiate two units, though with more difficulty than they would instantiate a single part. However, those two units then become available for learning other categories.

IV. General Discussion A.

CATEGORY LEARNING A N D PARTEXTRACTION

In these studies, we investigated how features of objects develop as a function of category learning and perceptual constraints. The functionality principle was proposed as a determinant of parts in object concepts: A part is a connected fragment that serves to categorize objects. The functionality principle guides the evolution of perceptually defined parts into a concise code of functional units that can represent object concepts-in general, this process is a recoding of stimuli into conceptual space. According to this principle, adjacent perceptual parts that are common to a category are unified into a single unit in the object representation. In a pilot experiment, different discrete views of the same object formed the simplest context in which such a process could occur. This experiment showed how separable perceptual parts can be unified into conceptual units and how the memory of an object can result in different parts being perceived even when no perceptual cue defines the part. Of course, in most cases, units will be determined by both perceptual and other kinds of cues, but the finding that parts are identified even in the absence of any perceptual distinction is strong evidence for the importance of experience in determining part structure. The first experiment (Section II,B)

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generalized these findings to categories of objects. Learning a category enabled the instantiation of a conceptual part needed to represent the category-the “mountain” that was common to the category was identified as a relevant part, and other parts were chosen less often with experience. Once instantiated, this unit played a conceptual role in categorizing novel objects by influencing the extraction of new parts (in Phase 2). The second experiment illustrated the development of a simple hierarchy of representation. A part initially instantiated as a single unit was broken down into two subunits, if its perceptual subparts were later discovered to categorize objects in a new context. Thus, experience in categorizing new objects led to a reanalysis of units that had previously been formed. Experiment 3 (Section II,D) validated the delineation measure of part structure by confirming the results with categorization judgments. These results raise the question of the relative roles of perception and higher-level cognition in the identification of parts. In what ways do the two affect part extraction, and how do they interact? It is important to emphasize that perceptual properties alone cannot explain subjects’ behavior in our experiments. In Experiments I and 2 , subjects grouped together two potential parts that were separated by the perceptual breaks as described by Hoffman and Richards (1985). This unitary representation could have been separated into two distinct units, as indeed it was by subjects in other conditions. However, the minimum of Gaussian curvature between the two parts was not sufficient to induce subjects to encode the parts into two units: When they had identical distributions across the category, they were treated as a single unit. Thus, perceptual cues cannot fully explain part encoding (though see the next section for possible counterarguments). At the same time, it is important not to underestimate the importance of perceptual information. Experiments 4 and 5 (see Section Ill) revealed that some perceptual constraints (suggested by Richards et al., 1988) overrode the determination of parts by the functionality principle. When the potential part varied, it was perceived as a single part only if the variations did not change the sign of curvature. When the variations resulted in different signs of curvature, subjects apparently perceived them as two different parts, making the category harder to learn initially, but easier to subdivide later (Experiment 5). In each of our experiments, the encoded part did have reliable perceptual cues that distinguished it from its neighbors. In addition to the nonaccidental properties already mentioned, the basic gestalt laws of similarity, proximity, common fate, good continuation, and so on (see Kaufman, 1974) probably exercise a strong influence on many stimuli. Obviously, if there

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were no perceptual cues, no amount of category learning could make part perception possible. In short, perceptual information is probably necessary but not sufficient to explain which potential parts are actually encoded in memory. Our studies have shown that categorical context can influence part analysis. However, it does not seem likely that this variable is always necessary for part identification. For example, a potential part that is highly salient (distinctive shape, brightly colored, with a sharp break between it and the rest of the object) will probably be represented as a conceptual unit even if there is no supporting category information. But in less extreme cases of perceptual breaks, the categorical context can essentially determine the shape of the part. For example, in Phase 2 of Experiment 1, naive subjects identified a single part of the Martian rock as being of a certain size and having a certain shape; But subjects who had prior experience with the components perceived the same rock fragment as consisting of two different units that were smaller and shaped differently from the whole. In Experiment 3, the same fragment of a stimulus was perceived as an undivided chunk or as the composition of two units, depending on which category had been learned. Thus, categorical experience resulted in different numbers and shapes of parts being extracted. Although one might expect the shape of a unit to be solely determined by perceptual criteria, if category learning can affect how subparts are collected into a conceptual unit, then it can influence the ultimate shape of parts in object representations. There is an analogy between this work and recent research on similarity comparisons. Medin, Goldstone, and Gentner (1993) and Markman and Gentner (1992) have discussed how similarity comparisons can alter the perception and analysis of the component objects. When object X is compared to object Y , certain of its features become highlighted because they are the same as or directly contrast with those of object Y . But if object X were to be compared with object Z , different features might well become more salient instead. In fact, Medin et al. showed that a given element of an artificial object can be perceived in two incompatible ways on different occasions, depending on what the object is compared to. In general, the features of an object that enter into a similarity comparison change depending on the other object that it is compared to. Our experiments reveal a similar effect, in the case of category formation. For example, in Experiment 2 (Section I1 , C ) , subjects identifying the important parts of an object selected different components depending on their prior experience with a category. The interaction of the object’s perceptual attributes and category learning determined these components-not the object’s attributes alone.

344

B.

Philippe G. Schyns and Gregory L. Murphy OF POSSIBLE LIMITATIONS

THE

EXPERIMENTS

As we discussed in Section 11, the choice of techniques places limitations on the interpretation of our results. In Experiments 4 and 5 , we used categorization decisions to infer subjects’ part analyses of the stimuli. Because the categories were defined by the presence of the crucial part, categorization seems to be an appropriate, though indirect, way of measuring part structure. In Experiments I and 2 , subjects were directly asked to indicate their part analysis of each object. Experiment 3 used both techniques and found considerable agreement between them. That is, when subjects identified a combined fragment as being a single part, their categorization decisions were strongest for the objects containing that entire fragment, and were reliably weaker for objects that split up the fragment. Subjects who identified the same fragment as being two parts did not object to categorizing objects with the split fragments as being in the category. Thus, this experiment helps to cross-validate the two methods. Nonetheless, the delineation method has some drawbacks that must be taken into account when making conclusions. First, the method requires subjects to make a conscious decision about part structure, which might be susceptible to demand characteristics o r response biases of some kind. Although true in principle, we would argue that the entire pattern of results does not support such a hypothesis. Subjects selected many parts at the beginning of the learning process (Phase 1 , Experiment I ) , so they apparently did not feel constrained to choose only one part. Furthermore, whether subjects selected one or two parts was highly predictable from the structure of the learning stimuli, as the functionality principle predicted. So, subjects identified the test fragment as a single part in Phase 1 of Experiment 1, but as two parts in Phase 2 . In Experiment 2 , subjects in group C delineated a single part, but subjects in groups A and B delineated two of them; a similar difference was found in Experiment 3. These differences are difficult to explain by biases or demand characteristics in the instructions o r task, inasmuch as the instructions and task were constant across groups. Another concern about the measure involves the distinction between conceptual and perceptual representations. It is important to understand that we are making claims about the representation of conceptual units in memory. Thus, in addition to parts that are represented in the concept, there may be a lower, perceptual level of representation, which more finely divides up the stimulus. Presumably, this level is determined by purely perceptual variables, such as the minima rule (Braunstein et al., 1989;Hoffman & Richards, 1985). The fact that subjects seemed to conjoin separable parts in their part delineation does not indicate the absence of

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a lower-level analysis in which these parts are separated. But because not all minima of curvature indicate a part break, it is possible that categorical information alters the perceptual interpretation of stimuli. This is a complex question that deserves extensive studies in future work. Finally, there is a weaker interpretation of our results that deserves attention. Consider Experiment 3 , in which two subparts either occurred adjacent in all learning examples, or were independent. Subjects in the former group identified objects as category members only when the subparts were adjacent, whereas subjects in the latter group ignored the matter of adjacency. However, perhaps subjects who viewed the two adjacent subparts did not truly represent them as a single unit in their mental representations-perhaps they identified them as two (or more) parts, with the constraint that “Part A must be immediately to the left of Part B.” Thus, this alternative argument says that in this experiment, and perhaps others, subjects identified subparts as separate units but then also viewed their spatial relations as relevant to category membership. One problem with this counterargument is that it does not explain subjects’ delineations. If both groups in Experiment 3 saw the crucial part as two separate units, why did one of them delineate one unit and the other group delineate two units when they were adjacent? In real-world objects, many parts must co-occur adjacently (e.g., head and neck), but that does not mean that people don’t perceive them as two separate parts. So, the part identification data are not fully explained by this view. However, in a broader sense, it may be that this counterargument is not really an alternative view after all. That is, if one’s conceptual representation includes the information that subunit A is always adjacent to subunit B, then the conjunction of these two may serve as a part, and may act as a unit in conceptual representations. As just discussed, there are other levels of object perception, and it may be that our units can be subdivided at these lower levels. However, if subjects’ reports and categorization seem to treat a conjunction of possible parts as a functional unit (as measured by part identification or categorization), then the most parsimonious conclusion at this point is that they form a single unit in conceptual representations. That is, the conjunction may serve in categorizing objects while the subparts do not-unless the subparts are conceptual components themselves. This last point could be investigated further with a simple experiment showing that the order of learning categories could determine the nature of the parts extracted. For example, two groups of subjects could learn in a different order the same two categories (one defined by a chunk of two adjacent subcomponents, and the other one by only one of the subcomponents). The “chunk-before-subcomponent” group would first learn the chunk category, then the subcomponent category. The

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“subcomponent-before-component” group would learn the same categories, but in the reverse order. The functionality principle predicts that the “subcomponent-before-chunk” subjects should learn the chunk as a conjunction; the conjunction of the subcomponent learned in the first category, plus the contrastive component that characterizes the second category. The functionality principle also claims that the chunk-beforecomponent subjects should not identify the contrastive component as a separate unit, because it never served to distinguish a category. A categorization test on the contrastive component should show that this component is a unit in the subcomponent-before-chunk group, but it is not a unit in the chunk-before-subcomponent group. This experiment would show that a chunk learned according to the functionality principle is perceived as undivided unless its components are themselves conceptual units. Experiments are underway to explore this and other predictions about how the order of learning categories results in different units being identified.

C. CONCLUSION In summary, our work suggests that the features that are encoded into conceptual representations are not always “out there” in the world, simply waiting to be perceived and encoded. Instead, these features are encoded in part as a function of the categories that people learn. Wisniewski and Medin (1991, in press) argued that world knowledge could influence the perception of such features. Our experiments have focused on a different influence, the relation of the potential part to the category structure. The interaction of perceptual information, which may not completely specify part structure, with category identification can result in the perception of a single unitized part, even if a perceptual analysis into smaller parts is possible. Of course, none of this work should be interpreted as saying that perceptual information is unimportant or ineffectual. However, it is simplistic to assert that either all feature identification is purely perceptual or that it is all totally conditioned by learning. Rather than either of these extreme views being correct, it is more likely that the interaction of perceptual information and experience will explain feature construction. And it is one specific kind of interaction resulting from concept formation that we have attempted to explain and provide evidence for in this chapter. ACKNOWLEDGMENTS This work was supported by National Institute of Mental Health Grant MH41704. The authors thank Heinrich Bulthoff and Shimon Edelrnan for their help in carrying out this research. and Barbara Malt and Brian Ross for helpful comments on a draft. Partial reports

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of this research were given at the Thirteenth and the Fifteenth Annual Conferences of the Cognitive Science Society.

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INDEX

A Acquisition, see also Reacquisition and conflicting outcomes. 33-35 discriminative responding, 80 and extinction, affecting factors, 47-50 object naming, 253 and representation of social categories. 139 speed, and category size, 98-99 Adults, see also Learners acceptance of shape transformations, 274-276 generalizations and syntactic context, 266 identification of new category members, 285-291 lexicon, 277-278 and novel objects, 257-260 reliance on object shape, 255 and shape bias, 300 Anomalies, and development of expertise, 24 1 Appearance, see aiso Body build external, and social categorization by children, 174-176 and function in object naming, 286-289 impervious to knowledge, 300 physical, importance to young children, 152-157 Artificial intelligence, medical, 188, 190 Associations binary, 2-45 and hierarchical, 50-64 formation, and selective attention, 123

formed in Pavlovian conditioning, 33 identical, 32 learned, resistance to passage of time, 4 R - 0 . see Response-outcome associations S-0, see Stimulus-outcome associations S-R, see Stimulus-response associations Associative codes, multiple, 50-63 Associative loss, promotion, 9 Attention children’s, 139 selective, long-term changes, 123 Attitude. and behavior, correlation, 176-177 Awareness, in preschool children, 143

B Behavior and attitude, correlation, 176-177 conceptual, nonsimilarity-based, 11 1 cultural, skills development in children, 133 experts, 205 instrumental and binary connections between S, R, and 0, 2-4 and simple nonreinforcement, 7-9 persistence in S + -R associations, 29-30 and redundant stimuli, 55 stimulus-controlled, 75-76 35 I

352 Behavioral analysis, concepts, 75-77 and consciousness, 123-125 Biology a5 context for understanding race, I5 1- 163 folk, and racial thinking, 138, 163-173 naive, 178 Birth, and reproduction of race, 157-160 Blood, and race, 164 Body build and identity, importance to young children, 152-157 relevance to identity, 160-161 Brain, and concepts, 126-127

C Categories conceptual, kinds, 120-121 defined in presence of CrUCial part, 344 disorders. in diagnostic situation, 192 ontological, 277-285 and pseudocategories, 85-86, 101-102 racial, and perceptual information. 145. 148- 150 size in basic-level conceptualization by pigeons, 80-81 and speed of acquisition by pigeons, 98-99 social acquisition and representation, 139-140 and children's perceptual processing, 134-137 conceptual saliency, 141-144 nonracial, 160- 163 split, 86-88, 102-105 Categorization accuracy, and generalization decrement, 80 artifacts, role of function, 292 basic-level, Spencian model, 89-105 decisions, and typicality ratings, 327, 330 extrapolation to, 96-105 models, and perceptual similarity, 122-123 object, importance of like kind, 278-279

Index and object recognition, 309 role of fragment, 312-313 Category learning joint by pigeons, 107- I1 1 by preschool children, 1 11-1 16 and partonomy, 326 and parts extraction, 34 1-346 identification, 3 15-324 perception, 343 and stimulus dimensions, 306-307 Category members and functionality principle, 310 identifying features, 306-307 new and old, perceptual similarity differences, 253-255 and shape bias, 284 Children, see also Learners acceptance of shape transformations, 274-276 beliefs about innate potential of human race, 165- 167 innate racial potential, 170-173 generalization from object name, 257, 267-269 generalizations and syntactic effects, 264-267 preference for same-shape objects, 293-294 preschool, joint category learning, 111-116 representation of human groups, 133-179 and shape bias, 285 and theory of race, 151-163 and use of perceptual similarity, 254-256 Choice, procedures, and extrapolation to categorization, 96-105 Classification, heuristic, and diagnostic reasoning, 189-190 Codes, associative, multiple, 50-63 Cognition medical, 187 social and levels of conceptual analysis, 178-179 models, 177-178

Index

Coherence in adequacy of schema, 224-225 diminishment during knowledge acquisition, 240 global and local. 197-198 Color hair and skin, and innate racial potential, 165-169, 171-173 nonhuman surface, and children’s beliefs, 167-170 Competency, cultural, achievement in childhood, 133-134 Comprehension, as cyclical process, 235-236 Computer simulations in Spencian model of basic-level categorization, 90-96 trial-by-trial changes in response tendencies, 127-128 Concept formation, racial, 137-139 Concepts behavioral analysis, 75-77 and brain, 126-127 and consciousness, 123-126 and language, 121 racial, 149-150 representations, features in, 305-307 Conceptual analysis, levels, and social cognition, 178-179 Conceptualization basic-level, by pigeons, 77-84 behavioral signposts, 74 differences. and types of social groups, I36 nonsimilarity-based. 106-1 16 mediational account, 118-1 19 race, among children, 151 and stimulus generalization, 84-89 primary and secondary, 105-106 Conditioned stimulus Pavlovian, and instrumental responses, 32 relation to S + , 19-21 transfer to instrumental response, 18-20 Configuration malleability-induced changes, 269-273 rigid mechanical motion induced changes, 273-277 Consciousness, and concepts, 123-126

353

Constraints effects on learning, 254-255 on object shape, 268 on part extraction categorical, 309-313 perceptual, 307-309 perceptual, 333, 338, 341 weak and strong, 203 Constructive-integration model, 235-231 Cues biconditional, 56-60 and categorization, 123 characteristic, 155 clinical, 230 in diagnosis, 207-213 Pavlovian configural, 63-64 perceptual, 306-307 retrieval, 193 and intact memories, 7 visual, 144-150 Curvature Gaussian, between two parts, 342 as perceptual constraint, 332-333

D Delineation, for object parts. 316-331 drawbacks, 344-345 Devaluation magnitude for extinguished and nonextingujshed responses, 8-9 outcome, 45-46, 53-55 and passage of time. 5, 12-13 R-0 relation, 49-50 standard procedure in S-R associations, 29-30 Development and children’s generalizations, 264-267 object shape and object name, patterns, 256-262 piecemeal process, 284 and representational system, 294-300 and shape-based representations, 298-300 social alternative model, 139-141 and children’s conceptual limitations, 174-176

354

index

Diagnosis decomposed into diagnostic components, 207-213 medical, 189-194 Diagnostic accuracy in analysis of protocols, 213-214 by experts and subexperts, 224-225 in uncomplicated problems, 234 Diagnostic reasoning and development of expertise, 233-234 and disease classification, 238-239 expert, 20 1-226 as interactive task, 195 novice, 226-233 process, 188-190 Differential outcomes effect, implications, 60-62 Discrimination in basic-level conceptualization by pigeons, 79-80 in extrapolation to categorization, 97-98 and generalization, in behavioral analysis of concepts, 75-76 go-no go, 88-89, 92-96 Discrimination learning obtained speed, 81 Pavlovian, 63-64 Domain-independent computational model, 243-244 Domain knowledge experts’, relevance, 206 effect on clinical reasoning, 223-226 hierarchical structure. 226

E Environment aspects relevant to social categories, 163 perceptual regularities in, 140 social and children’s beliefs, 170-173 and children’s understanding, 133 Essence, in modeling expectations about category identity, 164 Experience, perceptual, in childhood, 34, 137 Expertise, see also Medical expertise and deep reasoning, 238-240 development, and nonmonotonicity. 226-228

as extended continuum, 234 generic, 206 and specific, differentiation, 224-225 Expert reasoning and domain specificity, 204-223 and forward reasoning, 242 and task difficulty, 223-226 Experts, see also Subexperts behavior, 204 definition, 200-201 and domain knowledge, relevance, 206 and formation of specific categories, 326 generated protocols, 194 generation of accurate diagnostic hypotheses, 2 10-2 12 and perceptual cues, 306 and subexperts, differences, 214-226 use of retrieval structures, 193 Extinction and acquisition, affecting factors, 47-50 instrumental response and Pavlovian response, 7-9 and reinstatement, 37-43 instrumental S-0 association, 19 and response decline after discontinued outcome, 13 S - 0 , effect on switching performance, 51 testing in S - 0 associations, 24-25 transfer responses tested in, 16-17

F Faces, simulated human, and joint category learning by children, 113-1 16 Facets high- and low-level, 192-194, 218-222 identification leading to accurate diagnosis, 213 Family background, and race and mutability, 152-157 Family resemblances, role of birth, 158-160 Features basic, as environmental given, 306 in concept representations, 305-307 development, 341 encoded into conceptual representations, 346 Function in categorization of artifacts, 292

Index

evocative, S-R associations, 29 role in determination of category membership, 285-291 Functionality principle, 310-338, 341, 346

G Generalization in basic-level conceptualization by pigeons, 79-80 decrement and categorization accuracy, 80 inhibitory, 94 and discrimination. in behavioral analysis of concepts. 75-76 in extrapolation to categorization, 97-98 about object names, role of perception, 254 shape-based, 291 and syntactic effects, 264-267 Groups differences, quantitative analysis. 204 human, representation by children, 133-179 psychological, 135- 139 Growth, and inheritance, children’s judgments, 155-157

H Human race, innate potential, and children’s beliefs, 165-167 Hypothesis backward-directed, 222 and evidence, coordination, 228-233 formation, and task difficulty, 206-21 3 generation, time course, 2 14-21 7. 223-224 generation and evaluation, time course, I94 Hypothetico-deductive model, 202, 230. 234

I Identity biological, 152-157 relevance of race and body build, 160- 161

355

Information perceptual, see Perceptual information about R - 0 relation, role, 47-48 about shape and shape bias, 267-277 social, memory for, 144-145 textual, 208-210 Inheritance biological, color-related physical properties, 169 and growth, children’s judgments about, 155- 158 Inhibition conditioned, Pavlovian, 21 and suppressive effects of S-. 26-28 trial-by-trial changes, 91 Innate potential hair and skin color, 165-169 human race, 165-167 racial, and children’s beliefs, 170-173 Instantiation conceptual, 309 conceptual part, 342 single unit, 313, 321-322 Instrumental learning hierarchical model, 61 hierarchical structure, 45-50 and significant binary connections, 2 Instrumental response extinction, 7-9 and reinstatement, 37-43 and Pavlovian conditioned stimuli, 32 training, 5-7 transfer of conditioned stimuli to, 18-20 Interference, in R - 0 relation, 48-49 Intermediate effect, in progression from novice to expert, 227-228, 232-233

K Knowledge basic science, 188-192 domain, see Domain knowledge flat structure, 227-228 inert, 191, 233, 235 irrelevant prior, 232, 235 previous, role in parsing new objects, 322-324 and role of known taxonomies and functions, 277-291 transfer, 141, 178-179

356

Index

L

M

Labeling effect on memory for social information, 144-145 object, and shape bias, 260-261 Labels novel for familiar objects, 282-283 for same-shape objects, 289 verbal, 148-150 Language and concepts, 121 labeling of phenotypic gaps, 138 mastery by children, 134-135 Language learning by children, and perceptual similarity, 256 by infants, and perception, 256 Learners, see also Adults; Children and category biases, 306-307 generalization from object name, 253-255 generalizations and syntactic context, 265 and object perception, 300 and shape-based representations, 298 and shape bias, 276 Learning biconditional discrimination, 52-57 category, see Category learning discrimination, obtained speed, 81 Pavlovian, 63-64 effects of constraints, 254-255 instrumental, see Instrumental learning object names, name-function mapping, 293 paired-associate, 14 and Pavlovian conditioning process, 28 and problem solving, 242-243 R-0, permanence, 4-14 and shape-based representations, 298-300 similarity-based, 134- 135, 138- 139 about single object, and part identification, 314-315 S-0, 14-29 S-R, 30-31

Mediational account, nonsimilarity-based conceptualization, 118-1 19 Medical expertise cognitive research, 188-189 and conceptual issues, 194-201 in verbal tasks, 195 Memory children’s, 143- 144 encoding and retrieval ability, 205 long-term, 193 long-term associative, robustness, 6-7 working, unreasonable demands on, 202-203 Minima rule, 308-312, 323 Models alternative, for social development, 139- 141 categorization, and perceptual similarity, 122-123 constructive-integration, 235-237 domain-independent computational, 243-244 hypothetico-deductive, 202, 230, 234 social cognition, 177-178 Spencian, basic-level categorization, 89- 105 Motion, rigid mechanical, 273-277

N Name, object, see Object name; Object naming Name game, in behavioral analysis of concepts, 79 Nonmonotonicity, in development of expertise, 226-228 Nonreinforcement, simple, and instrumental behavior, 7-9 Novices definition, 201 diagnostic reasoning, 226-233 and coordination of hypotheses and evidence, 228-233 formation of specific categories, 326 understanding relevant features, 305-306

Index

0 Object kind ontological, and shape, 277-285 and same-shape objects, 293-294 and shape-based representations, 296-297 Object name generalization about, role of perception, 254 learning, name-function mapping, 293 and object shape, development patterns, 256-262 Object naming acquisition by learners, 253 appearance and function in, 268-288 development, constraints, 255 importance of texture, 257-264 role of same shape, 267-269 and shape-based representations, 294-296 and shape bias, 277-279, 300 Objects feature development, 34 1 new, role of previous knowledge, 322-324 single, and part identification, 314-315 three-dimensional, 273-277 Object shape, see also Shape bias in determination of category membership, 285-291 in early object naming, 291-300 and object name, development patterns, 256-262 role in object naming, 255 for same shape, 267-269 variation, and part extraction, 332-341 Occupation physical depictions, in pairing task. 146-149 related apparel, contribution to identity, 160-162 as salient social dimension among preschoolers, 143-144 Omitted outcome, 13, 16, 21-23, 34. 52, 56-57, 63 Organization, conceptual, and nature of prejudice, 176-177 Outcome conflicting, in acquisition, 33-35

357

devaluation, 45-46, 53-55 and passage of time, 5, 12-13 differential, effect, see Differential outcomes effect direct administration, 30 discontinued, associated response decline, 13 encoding, precision, 23-26 expectancy, 43-44 identical associations with conditioned stimuli and instrumental responses, 32 multiple, ambiguity of response with, 60 negative correlation of response, 9-12 omitted, see Omitted outcome presentation, 38-43 operation of S+ on, 42-43 unique association with discriminative stimulus, 48-49 Outcome-response associations, 37-44

P Parts, see also Subparts extraction and categorical constraints, 309-313 and category learning, 341-346 and perceptual constraints, 307-309 and shape variation, 332-341 hierarchy, development, 324-327 identification in category learning, 315-324 learning about single object, 3 14-3 15 origins, 305-313 single, variability limits, 338-341 structure, converging operations, 327-332 Part-whole relationship, 324, 326 Perceptual experience, in childhood, 134, 137 Perceptual information and encoding of parts, 342-343 and race. 141-150 and racial categories, 145, 148-150 in representation of social dimensions, 174 Performance decline with increase in retention interval, 4

358

Index

instrumental S-R association-mediated, 15 suppression, 6 Physicians, and diagnostic process, 226 Pigeons and basic-level conceptualization, 77-84 joint category learning, 107-1 11 speed of acquisition, and category size, 98-99 Potential, innate, see Innate potential Prejudice in American society, 172 nature, and conceptual organization, 176- 177 social, 136 Presentation, immediate and sequential, 224-226, 234 Pretraining, in joint category learning by children, 113-1 16 Priming, effect on recall, 148-150 Principled relationship, image properties to real object properties, 269-270 Problem solving and learning, 242-243 medical, I9 1- 195, 201-205 sequential, 217-220

R Race child's notion, 137-139, 176 human, see Human race and perceptual information, 141- 150 related thinking, and folk biology. 163-1 73 theory, and children, 140-141, I5 I - 163 Reacquisition. extinguished responses, 10 Reasoning and construction-integration model, 235-237 deep and expertise, 238-240 and shallow reasoning, 190 diagnostic, see Diagnostic reasoning directionality , 194- 195, 201-204, 2 10, 240-24 1 expert, see Expert reasoning forward and backward, 197-200, 203, 209-212, 222-224, 237

forward-directed, 189 patterns, 217-219 Reassignment training, in joint category learning by children, 112-115 by pigeons, 108-109 Recall ceiling effect clinical cases, 206 relevant material, 225 detailed information, by intermediate novices, 227 enhanced, for domain information, 189 free, 142-143, 195 narrative, 144- 146 Reinforcement differential, 84 relative frequency, reduction, 24-25, 27 response, and biconditional cues, 60 social, 79 Reinstatement, extinguished instrumental response, 37-43 Repetition, stimulus, in basic-level conceptualization by pigeons, 82-84 Representations and acquisition of social categories, 139 concept, features in, 305-307 conceptual, and encoded features, 346 object, two-dimensional, 269-273 psychological, human groups, 135-139 semantic network, 204 and propositional representation, 195-200 shape-based and development and learning, 298-300 importance to learners, 298 and object kind, 296-297 and object naming, 294-296 structured spatial, 291-292 Responding discriminative, 80, 83 taxonomic, 260 Response correct, association with discriminative stimulus, 53-55 decline with discontinued outcome, 13 differential effect of stimuli. 30-31 encoding form, 2 extinguished, reacquisition, 10

Index instrumental, see Instrumental response negative correlation with outcome. 9-12 nonreinforced. 35-37 stimulus-controlled, 75-76 tendencies, 93-95, 97-104, 127-128 transfer, see Transfer response Response-outcome associations, 3-14. 47-50 ambiguity, 57-63 background, 3-4 permanence of R-0 learning, 4-14 Retardation, and summation, in S-R, 35-36 Retention interval, increase associated with decline in performance, 4-7 Retraining, and subsequent response suppression by S - , 36-37 Retrieval structure, facets construed as, 193-194

S Saliency, conceptual, common social categories, 14 I -1 50 S+ in go-no go discrimination, 88-89. 92-96 operation on outcome presentation. 42-43 relation to conditioned stimulus, 19-21 S+ procedures, in S-0 learning, 15-21 Sin go-no go discrimination, 88-89, 92-96 structure, implication, 62-63 summation experiment, 35 training, consequences, 30 training as, 16 training biconditional cue as, 56-58 S- procedures, in S-0 learning, 21-28 Schema development for testing hypotheses, 233 driven reasoning, 224 hierarchical, 19 I in medical problem solving, 204-205 and well-structured problem, 223 Scholarship, historical and anthropological, 177-178 Search procedures, breadth-first and depth-first, 228-232, 234

359

Shape bias, see also Object shape and adults, 300 characteristics, 291-294 in context of object labeling, 260-261 developmental profile, 259 effects of mechanical motion, 275 in object naming, 277-279 and perceptual effects, 267-277 syntactic effects, 262-267 to taxonomic bias, 284 Similarity, perceptual and models of categorization, 122-123 between new and old category members, 253-254 utilization by children, 254-256 Social cognition model, 177-178 Social development alternative model, 139-141 and children’s conceptual limitations, 174-176 Social information, memory for, 144-145 Spencian model, basic-level categorization, 89- 105 Stimulus in basic-level conceptualization by pigeons repetition, 82-84 trial-unique, 81-82 categorization by experts, 189 conditioned, see Conditioned stimulus contiguous fragment, 310-312 control, taxonomy, 121-122 control of behavior and responses, 75-76 differential effect on responding, 3 1-32 discriminative and manipulations of S-0 association, 50-52 relation to response, 45 rewarding outcome in absence of, see Srewarding outcome in presence of, see S+ role in outcome representation, 15 uniquely associated with outcome, 48-49 verisimilitude and controllability, 119-120

360

lndex

in extrapolation to categorization repetition, 100-101 trial-unique, 99-100 negative correlation with outcome, 20-2 1 trained as signal, 25-27 transfer properties, 24-25 Stimulus dimension, 305-306 Stimulus generalization and conceptualization, 84-89 primary and secondary, conceptualization via, 105-106 Stimulus-outcome associations inhibitory, characteristics, 23-28 manipulation, 50-52, 55-57 resistance, 36-37 nature of, 17-21 Stimulus-response associations association between S + and R, 29-32 inhibitory, 33-37 Stimulus-response-outcome conjunctions, encoding, 45-47 Subexperts definition, 201 and experts, differences, 214-226 generation of accurate diagnosis, 2 10-212 Subparts, categorization, 327-331 Summation and retardation, in S-R, 35-36 and S-R-0 conjunctions, 46-47 Suppression instrumental performance, 6 related effects of repeated extinction, 9

T Task diagnostic explanation, 195 difficulty and expert reasoning, 223-226 and hypothesis formation, 206-213 domain-specific, 189 match-to-sample, 146- 147 no-label, 260-261 pairing, label-to-picture, 146-148 well-structured to ill-structured, 192 Testing, in joint category learning by children, 112-1 15 by pigeons, 108-109

Textual information, 208-210 Texture importance for animate objects, 273 in object naming, 257-264 and shape bias in context of novel objects, 285 Thinking, racial, and folk biology, 138, 163-1 73 Time passage and outcome devaluation, 12-13 in permanence of R-0 learning, 4-7 variable, in determination of outcome availability, 10 Training, see also Retraining; Reassignment training; Retraining biconditional cue as S-, 56-58 discriminability, 87 discrimination, 96 increase, and corresponding increase in diagnostic accuracy, 232, 235 original in joint category learning by children, 112-1 15 in joint category learning by pigeons, 108-109 and reacquisition outcome, 10-12 in presence of S + , 48 S + , in S-0 learning, 15-16 S - , consequences, 30 split-category, 103 stimuli, accuracy to, 99-100 Transfer biological principles of inference, 140-141 conceptual, 110-1 I 1 discriminative responding from familiar to novel stimuli, 76 outcome-specific component, 25 in S-R associations, mediation, 30-32 Transfer response overall levels, reduction during conditioned stimuli, 19-21 tested in extinction, 16-17 Transfer test and outcome presentations, 42-43 and response suppressed by stimulus, 32 in S - 0 associations, 19, 21-22, 28, 37

Index

and S + procedures in S-0 learning, 15-16 Transformation familiar, in tasks for young children. 155-156

mechanical, 274-277 object shape, 268-273 part, 332-336, 338-340 Two-process theory, 15, 17, 21, 55

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CONTENTS OF RECENT VOLUMES

Volume 21 An Integrated Computation Model of Stimulus-Response Compatibility and Practice Paul S. Rosenbloom and Allen Newell A ConnectionistlControI Architecture for Working Memory Walter Schneider and Mark Detweiler The Intelligent Hand Roberta L. Klatzky and Susan J . Lederman Successive Approximations to a Model of Human Motor Programming David A. Rosenbaum Modular Analysis of Timing in Motor Skill Steven W. Keele and Richard I. lvry Associative Accounts of Causality Judgment David R. Shanks and Anthony Dickinson Anxiety and the Arnygdala: Pharmacological and Anatomical Analysis of the Fear-Potentiated Startle Paradigm Michael Davis, Janice M. Hitchcock, and Jeffrey B. Rosen Index

The Comparator Hypothesis: A Response Rule for the Expression of Associations Ralph R. Miller and Louis D. Matzel The Experimental Synthesis of Behavior: Reinforcement. Behavioral Stereotypy, and Problem Solving Barry Schwartz Extraction of Information from Complex Visual Stimuli: Memory Performance and Phenomenological Appearance Geoffrey R. Loftus and John Hogden Working Memory, Comprehension, and Aging: A Review and a New View Lynn Hasher and Rose T. Zacks Strategic Control of Retrieval Strategies Lynn M. Reder Alternative Representations Ruth S. Day Evidence for Relational Selectivity in the Interpretation of Analogy and Metaphor Dedre Gentner and Catherine Clement Index

Volume 23 Quantitative Modeling of Synaptic Plasticity David C. Tam and Donald H. Perkel Computational Capabilities of Single Neurons: Relationship to Simple Forms of Associative and Nonassociative Learning in Aplysia John H. Byrne, Kevin J. Gingrich. and Douglas A. Baxter

Volume 22 Foraging as Operant Behavior and Operant Behavior as Foraging: What Have We Learned? Sara J. Shettleworth 363

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Contents of Recent Volumes

A Biologically Based Computational Model for Several Simple Forms of Learning Robert D. Hawkins Integrating Behavioral and Biological Models of Classical Conditioning Nelson H. Donegan, Mark A. Gluck, and Richard F. Thompson Some Relationships between a Computational Model (SOP) and a Neural Circuit for Pavlovian (Rabbit Eyeblink) Conditioning Allan R. Wagner and Nelson H. Donegan Simulation and Analysis of a Simple Cortical Network Gary Lynch and Richard Granger A Computational Approach to Hippocampal Function William B. Levy Index

Volume 24 Dimensional Mnemonics David S. Olton Memory Processing by Pigeons. Monkeys, and People Anthony A. Wright Short-Term Memory for Associations Bennet B. Murdock and William E. Hockley Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem Michael McCloskey and Neal J. Cohen Fear, Stimulus Feedback, and Stressor Controllability Donald A. Warren, Robert A. Rosellini and Steven F. Maier Semantic Context Effect on Visual Word Processing: A Hybrid Prospective-Retrospective Processing Theory James H. Neely and Dennis E. Keefe Network Structures in Proximity Data Roger W. Schvaneveldt, Francis T. Durso, and Donald W. Dearholt

Individual Differences in Attention Earl Hunt, James W. Pellegrino, and Penny L. Yee Type A Behavior: A Social Cognition Motivational Perspective Nicholas A. Kuiper and Rod A. Martin Index

Volume 25 Inferences about Word Meanings Richard C. Anderson Inference Generation during Auditory Language Comprehension David A. Swinney and Lee Osterhout Bridging Inferences and Enthymemes Murray Singer, Russell Revlin, and Michael Halldorson The Impact of Inferences on Instructional Text Bruce K. Britton, Lani Van Dusen, Shawn M. Glynn, and Darold Hemphill Integration and Buffering of New Information Karl Haberlandt and Arthur C. Graesser Goal, Event, and State Inferences: An Investigation of Inference Generation during Story Comprehension Debra L. Long, Jonathan M.Golding, Arthur C. Graesser, and Leslie F. Clark Content-Based Inferences in Text Colleen M. Seifert Content-Based Inferences during Narrative Comprehension Daniel G. Morrow, Gordon H. Bower, and Steven L. Greenspan Expectations, Mental Representations, and Spatial Inferences Monika Wagener-Wender and Karl F. Wender Causal Inferences and Text Memory Jerome L. Myers and Susan A. Duffy Causal Inferences and the Comprehension of Narrative Texts Paul van den Broek Index

Contents of Recent Volumes

Volume 26 Spatial Memory in Seed-Caching Corvids Alan C. Kamil and Russell P. Balda Detecting Response-Outcome Relations: Toward an Understanding of the Causal Texture of the Environment E. A. Wasserman Priming of Nonverbal Information and the Nature of Implicit Memory Daniel L. Schacter, Suzanne M. Delaney, and Elizabeth P. Merikle Metamemory: A Theoretical Framework and New Findings Thomas 0. Nelson and Louis Narens The New Multimodal Approach to Memory Improvement Douglas J. Herrmann and Alan Searleman A Triphasic Approach to the Acquisition of Response-Selection Skill Robert W. Proctor, T. Gilmour Reeve, and Daniel J. Weeks The Structure and Formation of Natural Categories Douglas Fisher and Pat Langley Index

Volume 27 Deriving Categories to Achieve Goals Lawrence W. Barsalou Learning and Applying Category Knowledge in Unsupervised Domains John P. Clapper and Gordon H. Bower Spatial Mental Models Barbara Tversky Memory’s View of Space Timothy P. McNamara Made in Memory: Distortions in Recollection after Misleading Information Elizabeth F. Loftus Cognitive Processes and Mechanisms in Language Comprehension: The Structure Building Framework Morton Ann Gernsbacher

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Temporal Learning John E. R. Staddon and Jennifer J. Higa Behavior’s Time Peter R. Killeen Index

Volume 28 Conditioned Food Preferences Elizabeth D. Capaldi Classical Conditioning as an Adaptive Specialization: A Computational Model C. R. Gallistel Occasion Setting in Pavlovian Conditioning Peter C. Holland Pairings in Learning and Perception: Pavlovian Conditioning and Contingent Aftereffects Shepard Siege1 and Lorraine G. Allan Evolutionary Memories, Emotional Processing, and the Emotional Disorders Susan Mineka Investigations of an Exemplar-Based Connectionist Model of Category Learning Robert M. Nosofsky and John K. Kruschke Reconstructing the Past: Category Effects in Estimation Janellen Huttenlocher and Larry V. Hedges Index

Volume 29 Introduction: A Coupling of Disciplines in Categorization Research Roman Taraban Models of Categorization and Category Learning W. K. Estes Three Principles for Models of Category Learning John K. Kruschke

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Contents of Recent Volumes

Exemplar Models and Weighted Cue Models in Category Learning Roman Tarahan and Joaquin Marcos Palacios The Acquisition of Categories Marked by Multiple Probabilistic Cues Janet L. McDonald The Evolution of a Case-Based Computational Approach to Knowledge Representation, Classification, and Learning Ray Bareiss and Brian M. Slator Integrating Theory and Data in Category Learning Raymond J. Mooney Categorization, Concept Learning, and Problem-Solving: A Unifying View Douglas Fisher and Jungsoon Park Yo0 Processing Biases, Knowledge, and Context in Category Formation Thomas B. Ward Categorization and Rule Induction in Clinical Diagnosis and Assessment Gregory H. Mumma A Rational Theory of Concepts Gregory L. Murphy Concept Structure and Category Boundaries Barbara C. Malt Non-Predicating Conceptual Combinations Edward J. Shohen Exploring Information about Concepts by Asking Questions Arthur C. Graesser, Mark C. Langston, and William B. Baggett Hidden King Classifications Edward Wilson Averill Is Cognition Categorization? Timothy J. van Gelder

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What Are Concepts? Issues of Representation and Ontology William F. Brewer index

Volume 30 Perceptual Learning Felice Bedford A Rational-Constructivist Account of Early Learning about Numbers and Objects Rochel Gelman Remembering, Knowing, and Reconstructing the Past Henry L. Roediger 111, Mark A. Wheeler, and Suparna Rajaram The Long-Term Retention of Knowledge and Skills Alice F. Healy, Deborah M. Clawson, Danielle S. McNarnara, William R. Marmie, Vivian I. Schneider, Timothy C. Rickard, Robert J. Crutcher, Cheri L. King, K. Anders Ericsson. and Lyle E. Bourne, Jr. A Comprehension-Based Approach to Learning and Understanding Walter Kintsch, Bruce K. Britton, Charles R. Fletcher, Eileen Kintsch, Suzanne M. Mannes, and Mitchell J. Nathan Separating Causal Laws from Casual Facts: Pressing the Limits of Statistical Relevance Patricia W. Cheng Categories, Hierarchies, and Induction Elizabeth F. Shipley index