Cognition And Technology: Co-existence, Convergence And Co-evolution

Cognition and Technology

Cognition and Technology Co-existence, convergence and co-evolution

Edited by

Barbara Gorayska University of Cambridge

Jacob L. Mey University of Southern Denmark

John Benjamins Publishing Company Amsterdam
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Philadelphia

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The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences – Permanence of Paper for Printed Library Materials, ansi z39.48-1984.

Library of Congress Cataloging-in-Publication Data Cognition and Technology : Co-existence, convergence and co-evolution / edited by Barbara Gorayska and Jacob L. Mey. p. cm. Includes bibliographical references and indexes. 1. Human-machine systems. 2. Human-computer interaction. 3. Cognition. I. Gorayska, Barbara. II. Mey, Jacob. TA167.C62 2004 004’.01’9--dc22 isbn 90 272 3224 5 (Eur.) / 1 58811 544 5 (US) (Hb; alk. paper)

2004050172

© 2004 – John Benjamins B.V. No part of this book may be reproduced in any form, by print, photoprint, microfilm, or any other means, without written permission from the publisher. John Benjamins Publishing Co. · P.O. Box 36224 · 1020 me Amsterdam · The Netherlands John Benjamins North America · P.O. Box 27519 · Philadelphia pa 19118-0519 · usa

Table of contents

Introduction: Pragmatics of Technology Barbara Gorayska and Jacob L. Mey Part I Theoretical issues

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Towards a science of the bio-technological mind Andy Clark

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Language as a cognitive technology Marcelo Dascal

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Relevance, goal management and cognitive technology Roger Lindsay and Barbara Gorayska

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Robots as cognitive tools Rolf Pfeifer The origins of narrative: In search of the transactional format of narratives in humans and other social animals Kerstin Dautenhahn The semantic web: Knowledge representation and affordance Sanjay Chandrasekharan Part II Applications

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Cognition and body image Hanan Abdulwahab El Ashegh and Roger Lindsay

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Looking under the rug: Context and context-aware artifacts Christopher Lueg

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Body Moves and tacit knowing Satinder P. Gill

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Gaze aversion and the primacy of emotional dysfunction in autism Sarah Bowman, Lisa Hinkley, Jim Barnes and Roger Lindsay

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Communicating sequential activities: An investigation into the modelling of collaborative action for system design Marina Jirotka and Paul Luff Part III Coda “The end of the Dreyfus affair”: (Post)Heideggerian meditations on man, machine and meaning Syed Mustafa Ali

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Martin Luther King and the “ghost in the machine” Will Fitzgerald

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Name index

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Subject index

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Introduction Pragmatics of Technology Barbara Gorayska and Jacob L. Mey University of Cambridge / University of Southern Denmark

In presenting earlier Cognitive Technology (CT) volumes to the public (Gorayska and Mey, 1996; Marsh, Gorayska and Mey, 1999), we as the editors took up a mode of collaboration between ourselves and our contributors that we had found useful in collections of articles: papers are submitted, accepted, and revised — but always with this one purpose in mind: viz., to bring about a unified perspective incorporating the intentions of the contributors and the editors, while respecting the individual contributions’ diversity and independence. In the present case, we venture to present the ideas of the contributors (and some of our own ideas) in a new garb, designed to bring out the ‘convergence, co-existence and co-evolution’ that we think are characteristic for the field of Cognitive Technology. Studies in Cognitive Technology examine human condition in the technological world. They are, as Clark (2002, p. 21 and this volume) puts it, “… in a very real sense, the stud[ies] of ourselves”. Such studies are an absolute necessity for the world and its inhabitants, in order for us to survive the onslaught and temptations of revolutionary new technologies and their sometimes aggressive practitioners. To properly paint this picture of a threatening, but also promising future, where technology and the cognitive makeup of its users also converge, co-evolve and co-exist, we must trace our steps back into the murky past when the discipline was originally conceived. It is no secret that the field of CT, despite all convergence, has evolved in ways that are not always easy to combine. In effect, even the term ‘Cognitive Technology’ has been the subject of acrid disputes and ground-claiming in the past. But in this field, as in all other areas of scientific research, the important thing to do is to let things develop, ‘co-evolve’, on their own, so as to obtain the maximum ‘convergence’ in the midst of ‘coexisting’, sometimes clashing views.

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The contributors to this volume come from widely diverging orientations, scientific as well as geographical. Despite this, the common concern of all is: How to make the most of technology without ‘losing our soul’, as one of the editors once put it (Gorayska 1994, p. 441). If this can be obtained by offering the present collection to the interested general and specialist public, we will have reached our aim, and provided a forum for even more co-evolution and convergence, while safeguarding the peaceful coexistence of the various strands making up the pattern of cognitive-technological studies in our days.

A flashback It all started one sunny day in 1993, in old Hong Kong, where the two editors (BG and JM) finally met, after having corresponded about various common matters of interests for a number of years. Over lunch (in which Inger Mey, Jonathon Marsh, N. V. Balasubramanian, Ho Mun Chan, Orville Lee Clubb, Laurence Goldstein, Kevin Cox and Brian Anderson also participated), the idea of CT as a new discipline, combining findings from computer science, philosophy, psychology and pragmatics was discussed. In Hong Kong, interest in CT as an emerging academic discipline arose when a small group of academics at the City University of Hong Kong began exploring the ways in which developments in information technology had implications for human cognition. They were originally inspired by two factors. The first was the Fabricated World Hypothesis proposed by Gorayska and Lindsay (in support of their investigations of how lay people understand and cognitively utilize the concept of relevance (Gorayska and Lindsay, 1993, first presented as Gorayska and Lindsay, 1989a)): “It is usual in psychology to treat the external world as a brute given, and to see perception, memory and action as processes driven by its immutable characteristics. In fact this is not so. In learning his route, the postman does not struggle to find an algorithm to fit an arbitrary collocation of house numbers, the number system is designed to make a convenient algorithm possible. We wish to propose the Fabricated World Hypothesis. There is a deliberate allusion here to the Carpentered World Hypothesis of Segall, Campbell and Herskovits (1966). Segall et al. argued that some aspects of our conscious experience, particularly the geometrical illusions of Muller-Lyer, are a consequence of expectations induced by the regularities present in human constructions. Windows are almost always rectangles, never trapezoidal and so on. We

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believe that this feedback effect of human artifacts upon the human mind is extremely widespread: and furthermore that it is the supremely important principle by which urban man controls his behaviour and releases processing capacity. It is almost banal to say that the human environment is organised around goal satisfaction. Areas of town may be most suitable for shopping, business or leisure, buildings are almost always organised around particular goals: bars, hairdressers, libraries, and so on. Within buildings, rooms are organised around goal-oriented activities: into kitchens, bathrooms, bedrooms, etc. Even within a room there may be a dining table, and similar goal-oriented functional specialization. What is not so banal is that the principles underlying the fabrication of the external environment, make an immense difference to the processes involved in perception, memory and goal-management. To put this in a challenging way: much of human memory is in the environment. The fabricated environment is such that simple algorithms suffice to generate effective behavior within it. In particular for present purposes, in the fabricated environment situations are such as to satisfy the execution conditions of very few plans at one time.” (Gorayska and Lindsay, 1989b, pp. 16–17)

Another source of influence came from the work of Gorayska and Cox (1992) on user-friendly interfaces to expert systems. In the spirit of the Fabricated World Hypothesis, the authors saw expert systems as extensions of the human mind. Consequently, they argued that the locus of control with respect to the language of expression at the interface ought to be placed in the user’s mind and not (as it was the case then, and is still now, predominantly) in the machine. Those days also witnessed the advent of multi-media, anticipated to have a tremendous influence on the delivery of education and training, mass communications and advertising, and manufacturing product design. Multi-media developments benefited from studies in the overlap area between cognitive processing and the organization and design of Information Technology (IT) equipment for the working environment. They aimed at improving overall system performance, by optimizing the effectiveness and efficiency of the human agents. The view taken by the investigators (of what came to be known as human factors) was that this efficiency was heightened by minimizing stress and fatigue and maximizing comfort, safety, and job satisfaction. Within that broad paradigm, several different areas of investigation emerged. Cognitive Ergonomics (Card, Moran, and Newell, 1983 and 1980), for example, focused mainly on things such as the direction of eye movements, spatial proximity of input sources, and degrees of complexity in information displays so as to maximize the efficiency with which both the equipment and the information

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provided were used. Cognitive Engineering (Norman, 1986; Norman and Draper, 1986, Rasmussen, 1988) dealt with the notion of an ‘internal model’ (the user had of a system and the system had of the user) in order to achieve optimal user-control and system-flexibility. Engineering Psychology (Wickens, 1992) provided technology designers with psychological profiles of users that helped eliminate design flaws and ensure that the capabilities and limitations of the human were accounted for in optimal design. None of these approaches, however, involved examination of the semantics, syntax, and pragmatics of information itself, and how its form of delivery might impact on the cognitive make-up of users. Such studies would have to involve factors influencing the nature and design of the interface between human cognition and IT processes, as well as products externalizing human thought processes. The Hong Kong CT interest group felt that the study of the relationship between all such IT developments and the human mental processes of forming cognitive schemata was not only timely but necessary. The term Cognitive Technology was coined to express the necessity of exploring the developmental co-dependence between the human mind and tools it interacted with: “[CT] explores the nature of the information made available due to such technological advances, how as a result of this information the human/Information Technology interaction influences cognitive developments in humans and how outcomes of such interactions and influences provide feedback effects on future advances in IT.” (Balasubramanian, Gorayska and Marsh, 1993, p. 4)

An elaborated version of this manifesto was first presented at the conference on New Visions of the post-industrial society: the paradox of technological and human paradigms, in Brighton, UK (Gorayska and Mey, 1994) and appeared in print as (Gorayska and Mey, 1996c). Inger and Jacob Mey brought to the round table a complementary but distinct tradition. In Europe (especially Scandinavia), the United States, and Japan, the consideration of the processes at the interface of human cognition and technology focused on the ways that the ‘toolness’ of computer artifacts interacts (or interferes) with the intentions and needs of the user, and how the user adapts to the ‘toolness’ in various ways, while keeping his or her autonomy vis-à-vis the tool. Inspired and supported by the Scandinavian labor movement, Pelle Ehn and a group of workers at Aarhus University, Denmark, looked into the question “how well the user interface relates to the practice and language the user is familiar with” (Ehn 1988, p. 434). This perspective was not far from that

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advocated by the group working with Donald Norman in California, who as early as 1986 had published a collection of articles under the telling acronym of ‘UCSD’ (to be read as either ‘User-Centered System Design’, or more whimsically, ‘University of California at San Diego’ — the place where their research was being conducted). Ehn acknowledges his indebtedness to Norman’s and his co-workers’ ideas, when he directly refers to Hutchins et al.’s article in the 1986 Norman book on the matter of “the level of tools in the user interface”. While “the primitive command of a Turing machine gives the user the tools to perform a task that can be done with a computer artifact, … there are not many users that would be helped by this in their ordinary work practice.” (1988, p. 434). Ehn then refers to Alan Perlis’ famous quip (also quoted by Hutchins et al., 1986, p. 101) about the “Turing tar-pit in which everything is possible but nothing of interest is easy”, and its converse, the “over-specialized system where operations are easy, but little of interest is possible” (Hutchins et al., 1986, p. 103). It is in the interface between the tool’s affordances and the human’s intentions that the proper dialectic interaction can take place. Ideally, the ‘toolness’ of the tool should gradually diminish, as the tool itself recedes into the background, as the useful factotum whose presence we rely on without actually ever seeing it. This is also the notion of the ‘invisible tool’, as propounded in an early article by Mey (1988) and also taken up by Norman in his 1999 book The Invisible Computer. In all these matters, what was really at stake was the question: given that adaptation is necessary to deal with in an increasingly complicated technological environment, in which direction should the adaptation take place? Should the humans adapt to the computer, or should the computer be adapted to human needs? On the face of it, an ecological, human-oriented solution would be to let the human be the ‘adaptee’, the one that is being adapted to, while the computer is the one that adapts. But the situation becomes more complex when we consider the fact that, in making the computer adapt to our needs, we have already taken a big bite out of the adaptive process: we are ‘natural-born cyborgs’ (as Clark has expressed it; 2001a), for whom using, and identifying with, tools (including now the computer) is as natural as is breathing. In a way, we are already ‘pre-approved’ for computer use: we are as much destined to be computer-consumers as we are potential acceptors and executors of credit card offers and practices. The real question was, therefore, not if we should adapt, but how we should do it, and on whose premises. This is where CT entered the picture. The ‘adaptivity’ problem (Mey, 1998) that the Europeans, Japanese, and some

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Americans had focused on, tied in with the developments at the Hong Kong end of the CT world. Hence it was not enough to re-invent and reverse the old slogan from the 1933 Chicago World Fair (quoted by Norman, 1996, p. 253): ‘Science Finds, Industry Applies, Man Conforms’, to make technology the one “wh[ich] should do the conforming” (ibid.); a balanced view of adaptation should take into account the fact that ‘man’ is in principle a conformist, that is, a flexible tool-maker and user; and that to get the most out of our technology, we have to include the cognitive human self. Which is precisely what CT came to be all about. As Andy Clark puts it, the “tools (i.e. the technology] and culture [i.e., the cognitive environment] are indeed as much determiners of our nature as products of it” (this volume, p.31). The essence of the human dealing with the computer technology is that we are able to bridge both ‘gulfs’ (in Hutchins, Hollan and Norman’s terminology; 1986, p. 94): that of evaluation and that of execution, in order to be able to deal with both ‘worlds’: that of cognition and that of technology.

Establishing the ground Following the fruitful discussions at the momentous ‘round table’ lunch in 1993, the two “worlds” finally found a common expression in the collaborative ground work for the First International Cognitive Technology Conference, CT’95, which took place in Hong Kong in 1995. The call for papers distributed in a variety of available media read: “Cognitive Technology (CT) is the study of the interaction between people and the objects they manipulate. It is concerned with how technologically constructed tools/aids (A) bear on dynamic changes in human perception, (B) affect natural human communication, and (C) act to control human adaptation. Cognitive systems must be understood not only in terms of their goals and computational constraints but also in terms of the external physical and social environments that shape cognition. This can yield (A) technological solutions to real world problems, and (B) tools designed to be sensitive to the cognitive capabilities and affective characteristics of their users. CT takes a broader view of human capability than the current Human Computer Interface research and talks of putting more of the ‘human’ into the interface without attempting to simulate ‘humanness’ on machines. It is primarily concerned with how cognitive awareness in people may be amplified by technological means and considers the implications of such amplification on what it means to be human. It should appeal to researchers across disciplines

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who are interested in the socio-cultural and individual implications of developments in the interface between technology and human cognition. Any technology which provides a tool has implications for CT; computer technology has special importance because of its particular capacity to provide multi-sensory stimuli and emulate human cognitive processes.” (CT’95 call for papers, first published on the Internet in December 1993)

At the time of CT’95, several new important developments happening at the interface of cognition and technology were in the offing. One was the trend to consider the process of externalizing-internalizing-externalizing of the mind as a constant loop: “When we externalize our minds, we create an object. This object, in its turn, is not just an object in space: it is something that we consider, relate to, love or hate, in short, work with in our minds, hence internalize. In very simple cases, the object is ‘just’ an object for vision (as Descartes seemed to think); more sophisticated ‘considerations’ include the mirroring that takes place when the child discovers its own image as separate from itself […], or when we evaluate a mind product as to its ‘adequacy’, and compare it to the original representation that we had ‘in mind’. Consequently, removing this check can have some strange and unexpected effects, as in the cases where an artist loses the use of one of his senses: the near-blind Monet, the deaf Beethoven, who continued to externalize their minds, but with unmistakably different (but not necessarily artistically inferior) outcomes. The re-internalized object is different from the one that started the externalizing process: it retains a tie to its origin, but has also become strangely independent. It now has a life of its own, and at a certain point in time, it is its turn to become externalized. This process continues until we think the result is adequate, and in the meantime, every new version interacts dialectically with the previous one. It supersedes it, but cannot replace it quite.” (Gorayska and Mey, 1996a, p. 6)

The externalization-internalization-externalization loop gave rise to the second emergent factor — the dichotomy between Cognitive Technology (CT) and Technological Cognition (TC): “The term Cognitive Technology may be too narrow for our intentions. It serves well to describe those issues which deal with determining approaches to tool design meant to ensure integration and harmony between machine functions and human cognitive processes. Unfortunately, it does not adequately describe a number of other issues, in particular, those concerns which relate to the identification and mapping of the relationship between technological products and the processes by which human cognitive structures adapt. We see these

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two types of issues as constituting related but distinct areas of investigation which are best kept separate but must be given closely aligned treatment. We therefore reserve the term Cognitive Technology to refer to methodological matters of tool design, and propose the term Technological Cognition to refer to the theoretical explorations of the ways in which tool use affects the information and adaptation of the internal cognitive environments of humans. Human cognitive environments are constituted by the set of cognitive processes which are generated by the relationships between various mental characteristics. These environments serve to inform and constrain conscious thought. Under the new schema, theoretical developments in Technological Cognition would find concrete expression in the constructed artifacts produced by Cognitive Technology. It is this dichotomy which forms the basis for our argument and the grounds from which to develop a framework for analysis.” (Gorayska and Marsh, 1996, p. 28)

Third, and most importantly, the separation of methodologies for tool-design (externalization processes) and the processes by which existent tools in turn technologize the mind (internalization processes) allowed us to put the explorations of both on a firm pragmatic footing: “Cognitive technology thus turns necessarily (although not automatically) into technological cognition (Gorayska & Marsh, 1996). I said: ‘not automatically’, because the necessity is one that we need to realize and make our own. The conditions for using technology are not in the technology alone, but in the minds of the users. To vary Kant’s famous dictum, cognition without technology is empty, but technology without cognition is dangerous and blind. Our minds need the computer as a tool, but we need to consciously integrate the computer into our minds before we start using it on a grand scale. In this way (and in this way only), rather than being a mindless technological contraption, the computer may become a true tool of the mind. Technology creates gadgets that can be put to use without regard for their essential functions. What, in the end, such gadgets do to ourselves and to our environment, however, is not necessarily anybody’s concern in their actual conditions of use, in which the mind-captivating (not to say mind-boggling) fascination of advanced technology allows us to focus on the intermediate stage between intent and effect, the purely technological one. To take a simple example, pressing a button is, in itself, a neutral activity; yet it can in the end cause a door bell to ring just as well as it may detonate a nuclear explosion. ‘I just pressed a button’, the pilot who launched Fat Boy on Hiroshima could have said. And if Timothy McVeigh (one of the persons indicted, and subsequently convicted, in the April 19, 1995 bombing of the Alfred P. Murrah Federal Building in Oklahoma City that took 165 lives)

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would have had to kill all those 165 people by hand, he never would have gotten beyond the first two or three, especially if he had started out with the babies. Now he could just connect some wires, and leave it at that. Technology does the work; our minds are at rest. Garrison Keillor, in his News from Lake Wobegone, aired on radio station WBEZ, Chicago, on 12 August 1995, offered a philosophical reflection on Halloween pranks. His point was that you are responsible even for the unknown, and unintended, effects of the fun you have (the example was of some boys at Halloween disconnecting a box car and sending it out on the tracks). What determines your responsibility is the outcome; he called this a strictly ‘outcome-based morality’. Applying this to our subject from a slightly different point of view, one could talk of a pragmatic view of technology and cognition. The pragmatics of cognitive technology (CT) deals with technology’s effects on the users and their environment; a pragmatic view of technological cognition (TC) implies that we inquire into the conditions that determine that cognition, that is the conditions under which users can cognize their technology, in order to realize the effects of their technological efforts. We need pragmatics in our CT so that it can be environmentally correct; we need pragmatics for our TC to be morally sound.” (Mey, 1995; italics in original)

Recent developments in CT and in the philosophy of mind echo this statement. To take the latter first, Andy Clark in his recent book, Mindware (2001b), has proposed the concept of ‘wideware’ to cover environmental extensions of the human mind. Under this concept, he subsumes both the Fabricated World Hypothesis (the principles underlying the fabrication of the external environment make an immense difference to the processes involved in perception, memory and goal-management) and our earlier observation that technology externalizes processes (algorithms) which originate in the mind: “… what is distinctive about human thought and reason may depend on a much broader focus than that to which cognitive science has become most accustomed, one that includes not just body, brain, and the natural world, but the props and aids (pens, papers, PCs, institutions) in which our biological brains learn, mature and operate. A short anecdote helps set the stage. Consider the expert bartender. Faced with multiple drink orders in a noisy and crowded environment, the expert mixes and dispenses drinks with an amazing skill and accuracy. But what is the basis of this expert performance? Does it all stem from finely tuned memory and motor skills? By no means. In controlled psychological experiments (Beach 1988, cited in Kirlik, 1988, p. 707), it becomes clear that expert skill involves a delicate interplay between internal and environmental factors. The experts

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select and array distinctly shaped glasses at the time of ordering. They then use these persistent cues so as to help recall and sequence the specific orders. Expert performance thus plummets in tests involving uniform glassware, whereas novice performances are unaffected by any such manipulations. The expert has learned to sculpt and exploit the working environment in ways that transform and simplify the task that confronts the biological brain. Portions of the external world thus often function as a kind of extraneural memory store.” (Clark, 2001b, p. 141)

The need to consider moral issues in tool-design, accentuated by the pragmatic processes of tool use, has been reiterated by Chan and Gorayska (2001) in their ‘Critique of Pure Technology’. Echoing Wittgenstein (1953), they remind us that the meaning of a piece of technology is a consequence of how it is actually being used. Following Kant (1781), they point out that the way technologies will be used by people is limited by human inherent and enjoined perceptive capabilities. Therein lies a danger of tool misuse. What we need to work towards is an increased awareness 1) of the unthinking use of pure technology, i.e. technology decontextualized from the cognitive processes activated through its use, and 2) of the processes through which we become habituated to the cognitive scaffold (Clark, 1997) that technology provides. How can this be achieved? “This can be achieved through an open dialogue — an ongoing and collective deliberation of how our environment and our lives should be shaped by technical means and what kinds of risks we as the public can collectively bear. Such collective deliberations — which ultimately lead to collective responsibility in the face of adversity — require access of a wider public to details of the design and the already known evidence of potential short-term and long-term risks in adopting a given technology. […] The parallel [to Kant] in the case of pure technology is that we also need to acknowledge the limits of technology and admit the fact that human-related problems in a technology-oriented society have to be dealt with not only at the level of cognitive, rational, technology, but also at the cognitive, emotive, psychological and spiritual levels. Most importantly, they will have to be dealt with at the ethical, social and political levels as well.” (Chan and Gorayska, 2001, p. 474)

By virtue of bringing the pertinent questions of CT to the attention of a wider audience, this book is an invitation to such public, collective deliberations.

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The CT agenda It has been said that you don’t really understand a scientific problem area until you have been able to formulate some pertinent questions belonging to its remit. In this perspective, it seems to us that the first question above is subordinated to the second one, and that this latter question encapsulates the answers to the first one. For, if we can argue successfully for the need for some urgent problems to be solved in the area of CT, then of course the need for a forum discussing those questions has been established, and the audience will manifest itself: ‘If you build it, they will come’. If we endorse CT as the study of the pragmatic cognitive processes ongoing at the dialectic boundary area between human tool users and tools used by humans, we will immediately be able to recognize a whole array of problems: – – – – – –

What is the tool to a human, and what is the human to a tool? (see Gorayska, Marsh and Mey, 2001) What does it mean to say that ‘the human comes first in technology’? Where are the limits for computerizing human activities — if there are any? How does the tool, especially the computer tool, change the mind (as opposed to, or as a complement to, the mind changing the (computer) tool)? Can we have too much technology (‘the computer taking over’)? What are the secondary (‘hidden’) effects of this technology (cf. Salomon 1993; Gorayska and Mey, 1996a)?

More importantly, however, one issue that looms large in such considerations is the pertinent methodological question that has permeated CT (conceived of as a scientific discipline) to date, namely; What methods do we as investigators need to employ when we study technological cognition for the purpose of designing or evaluating cognitive tools? In other words, How do we study and apply the pragmatics of technology? And more concretely, What (kind of) question(s) do we want to address in CT, and how are we going to address them? If we want to find out what we do when we do CT in design we must first ask ourselves what it means to do CT in general. Since CT is essentially to do with the relationship between humans and their tools, the following general aspects of ‘doing CT’ are worthy of attention: –

The question of what constitutes a cognitive tool. All artifacts are in some measure cognitive artifacts and can be situated on a continuum of purposeful use between the extremes of raw material and the mind (Gorayska, Mey and Marsh, 2001). Inasmuch as we employ them as mental prostheses, AI

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robots (Pfeifer, 2002, reprinted in this volume), event modeling languages (Jirotka and Luff, this volume), or natural language (Dascal, 2002, reprinted in this volume) that help designers analyze and better understand phenomena in the real world, are in fact instances of cognitive tools. So are narratives (Dautenhahn, 2002, reprinted in this volume). Could the same be said about care-givers who help newborn babies acquire cognitive skills by simply interacting with them (Lindsay and Gorayska, 2002, reprinted in this volume)? Is it more fruitful, from the standpoint of CT, to define cognitive tools in purely functional terms? Could there be natural cognitive technologies, i.e., spontaneously evolved, functionally determined, often modular (depending on relevance discontinuities; Lindsay and Gorayska, op. cit.), mental processes that constitute the techne of the mind, as first proposed by Meenan and Lindsay (2002) and further evidenced by El Ashegh and Lindsay and by Bowman et al. (both this volume)? The question of co-evolution of the mind and tools it creates. Can minds and tools indeed co-evolve and, if so, how can this evolutionary co-dependence be shown? For interesting discussions relevant to this issue see, for example, Mithen (1996), Lindsay (1999), Clark (2002, reprinted here) and Dautenhahn (2002, reprinted here). The question of adaptivity vs. adaptability, as raised by Mey (1998), and discussed above: can we distinguish between the two in practice? Some examples of how technology can coerce behavioral change in its users can be found in Gill’s chapter (this volume). The question of the ‘transparent tool’, the tool that you use without noticing that you are using it (following Mey 1988, Norman 1999): what cognitive effects, if any, is it likely to have on their users? Dautenhahn (2002, reprinted in this volume) provides an excellent example. Arguments presented in Ali (2002) and Lueg (2002), both reprinted in this volume, are also relevant to this question. The dangers of the ‘can-must-can’ spiral, or the dialectics of ‘tool overbidding’: once you have created a tool that can do a thing, you are obliged to use it, and create a tool that can do even more, which then puts you under the obligation to use that tool, and so on ad infinitum. (We have seen sad examples of this spiral in the arms race, or the race to fill our lives with useless gadgetry that requires us to buy even more useless gadgetry, and so on.) The unthinking use of technology (discussed by Chan and Gorayska, 2001): can we predict it and how can we avoid it? The meaning of a tool lies in its use. If misapplied with little thought, the tool use can have disastrous

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consequences. The extreme case of such misapplication of use with respect to the computer that brackets emotion from cognition has come to be known in CT as the Schizophrenia Problem (Janney, 1997, further discussed in Ali, 2002, reprinted here). The related question of human wants and needs: is what we want, really what we need, or are our technological needs the result of someone else’s wants? In other words, we have to realize that cognition is not neutral, but always ‘embodied’ in some human or technological context (Clark, 2000 & 2002, reprinted in this volume). What are the social, psychological and cognitive mechanisms in enjoining need? For some answers to this question, see Lindsay and Gorayska (2002, reprinted in this volume) and Lindsay (1996). The dialectics between a routine operation facilitating our daily needs, and the mind-numbing sterilization of our daily activities through routine operation (possibly resulting in the computer user’s ‘screen rage’, in analogy to the frustrated driver’s ‘road rage’): what technologies do we need to overcome that sterilization? In this connection, a provocative piece by Will Fitzgerald (this volume) may serve as a poignant reminder. In humanizing technology, there are two directions our work can take: one is ‘from the inside out’, the other ‘from the outside in’. By this we mean that we either can take our point of departure in the human (head and body) and try to get our technological environment to fit the perceived dimensions that we have of it; or we can work from the environment as a given, and try to fit the human (body and head) into it: which direction maximizes human benefits? We will dwell some more on this distinction in the following. (See also the arguments in Jirotka and Luff, this volume.) The question of motives and benefits: we need to carefully consider what motivates technology inventors and developers, what specifies the broader scientific frameworks within which they operate, and furthermore, what motivates design perspectives. Some such perspectives, proposed for CT, include Empirical Modeling (Beynon, 1995), Cognitive Dimensions (Blackwell et al., 2001; Kutar et al., 2001), the Reflective Questioning Perspective (Gorayska and Marsh, 1996 & 1999), Goal-Based Relevance Analysis (Lindsay and Gorayska, 2002, reprinted in this volume, and Lindsay (1996)), and, with relevance to the latter, the Affordance View (Chandrasekharan, this volume). The paramount question is whether such considerations of these and other proposed perspectives on practice can put us in a better position to understand, at a deeper level, a design process? Can this benefit the formulation of a broader, synthesizing CT methodology?

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The question of contributions from the related disciplines: To what extent can empirical research in those disciplines further the design of humane technologies? Arguments, experiments and case studies relevant to this question are presented in this volume in the chapters by Roger Lindsay and Barbara Gorayska (2002, reprinted here), Marina Jirotka and Paul Luff, Hanan A. El Ashegh and Roger Lindsay, Sarah Bowman et al., Satinder Gill, and Kerstin Dautenhahn (2002, reprinted here).

Changing nature, changing us To grasp the distinction alluded to above (‘inside out’ vs. ‘outside in’), let’s walk through an imagined example of a prosthesis (cf. Mey, 2000): an artificial limb, such as a hand. If we want to define the artificial hand from the inside out, we go about defining it by its specific functions, such as we see it from the point of view of our body and our understanding of its functions in relation to our needs. If we define the hand from the outside in, the emphasis is on the existing conditions, and the question to ask is: What kind of hand (or prosthesis, in general) would fit these conditions, and how are we going about ‘technologizing’ them? Among the questions that may turn up here are: – – – –

How much does visual and tactile resemblance play a role in defining the hand? Is a hook acceptable as a prosthesis (cf. the movie ‘Edward Scissorhands’)? Does the prosthesis have to be attached to a body, or can it operate independently, by remote control? Do we have to call the prosthesis a ‘hand’ at all (given that we use the word ‘hand’ metaphorically in a number of contexts where the resemblance is nil, e.g. when we talk about the hands on a dial or a clock, or a ‘side’ or ‘direction’, as in the Japanese Yamanote ‘the hilly section of Tokyo’, lit. ‘the mountain’s hand’)?

Given our conception of CT, how would we deal with these problems? Here, typically, the question of ‘design’ in tool manufacturing crops up — a much maligned term, since it has been taken over by certain professional ‘designers’ who operate more for their own good than for that of others. As to the conditions for designing a tool from the outside in, these are similarly well-known: one starts out with a specification of the problem, then goes on to figure out how the tool will fit in. Consider the activity of fruit-or-

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berry-picking. We consider the human hand as the primary tool for this activity: from times immemorial, people have been picking berries with their fingers, apples and pears with their hands. But consider the boys who want to have some walnuts out of a tree. The nuts hang too high, and they cannot reach them by hand. What do they do? They throw branches and other objects into the tree, trying to make the nuts fall down. This may work for nuts, that don’t get damaged by the fall. But if you were to do this to avocados, the result would be disastrous. Here, the environmental conditions dictate another solution, which one of us (JM) in fact saw practiced in Brasília (the capital of Brazil) one day, when walking to school: a man was maneuvering a tall pole with a cup-like attachment at the end. Placing the pole with the cup under the avocado, he then carefully manipulated a little knife that was attached to the end of the pole, with the help of strings running down the length of the pole. After several tries, the avocado landed successfully in the cup and was lowered safely down to the owner of the tree (or the user, whichever the case might have been — we believe nobody in Brasília owns those trees outside the apartment blocks). In this case, the ‘hand’ was completely externalized and detached from the body; it fulfilled its functions in part because of its non-likeness to a ‘real’ hand (one wouldn’t even call such a prosthesis a ‘hand’, we believe). From the primitive stick thrown into the tree to this sophisticated contraption, we notice an increasing ‘instrumentalization’ (along the lines sketched in Gorayska et al., 2001): the primitive tool becomes a sophisticated instrument. But at the same time, its role as a prosthesis shifts character: from being in touch with the body, it becomes part of the environment rather than of the human. Externalizing our activities, thus, does not mean that we have to replicate them; they can be ‘reborn’ in different shapes, sometimes even unrecognizably or unidentifiably so. Figure for example, that some genetic engineering process would be able to produce an avocado with a hard bolster, a bit like a cocoa nut. Then the whole operation of avocado picking could be effected by a device like the one we use for cherry harvesting: a tractor with a big mechanical arm clamps the tree, spreads out a sheet underneath it, and proceeds to shake the cherries out of the tree and on to the sheet, which is then folded mechanically and emptied in the container attached to the tractor. The activity of fruit picking, which was originally thought of as specifically hand and fingeroriented, has now been transformed into something which happens completely outside the body. Using these examples as our guideline, we now are able to formulate a few more pertinent questions to test our CT practice:

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How do we have to think about the material conditions and parameters for using a prosthesis, or for that matter, any tool? Taking, again, the case of the hand, we would have to distinguish between US and European parameters for the opening of doors: the up-down movement that is typical for European latch-type door openers is strategically different from the rotating movement that is appropriate to the turning knobs that are standard in the US. We should be thinking also of other ‘affordances’ for hand interaction: a technology that would be proper for berry picking would not necessarily be fit for hand-shaking at conventions, for instance, or for the apostolatus manuum, ‘the ‘apostolate of the hands’, as Catholic priests were wont to call their greeting obligations at parish and other religious events. To properly assign functions to the prosthesis (the ‘hand’), we need to consider the various functional movements that are involved in hand-toworld interaction, and differentiate our prosthesis accordingly: – grasping vs. picking in relation to the nature of the objects (solid vs. scattered, flat vs. protruding, substantial vs. leafy, and so on); – pushing vs. pulling (e.g., buttons vs. knobs); – vertical vs. horizontal direction (levers vs. handles); – lifting vs. stroking (e.g., a baby or a cat).

An interesting example of how the design of the prosthesis feeds back into the design of the natural hand and vice versa is furnished by the development and progress of the common computer instrument known as the ‘mouse’. In the beginning, the mouse was just a simple button on a wire, not much different from a lamp switch, that could be moved around. When the mouse became ‘on line’, that is, when its movements could be said to emulate and control the movements of the cursor on the screen, new possibilities turned up. Some researchers started to think about the fact that the human user not only had hands, but also feet; and that the mouse-like instrument, by having only one button, did not exploit the full capacity of the human hand steering it, which had five fingers (plus the fact that there were two hands; but since people had to use at least one of them for typing, this was never a serious option). Hence, some of these people set out to design ‘mice’ that could remedy this defect. The five-finger mouse and the foot mouse were actually developed as early as the mid-eighties, in Japan, at the Department of Computer Sciences of Osaka University, where JM was witness to how the only person (a graduate student) who was able to manipulate both the foot- and the five-fingered mouse (and both at the same time!) obtained quite striking achievements as a result of this

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successful coordination. However, as one of the researchers who had developed the five-finger gadget (and who was not even able to operate it himself) remarked: ‘It’s a nice toy, but the only person who ever will ever be able to use it is Katoo-san, and it took him almost two months’. The moral of the story is, perhaps, not that the invention of the fivefingered mouse (or the foot mouse) in itself was contrary to the ideas of CT, as we see them, but that this particular invention was developed out of a wrong idea about making prostheses, namely that they should be as close as possible to the human body part that they replaced. The relevance of using fingers was overlooked. In actual fact, there isn’t much that we specifically use all of our five fingers for, singly and individually (except if we are prestidigitators or consummate piano players). Most of the time, we use the hand as such, or the fist, e.g., for throwing a ball, or punching someone in on the nose. Here, the five fingers cooperate in the movement and do not work on their own, as in the case of the five-fingered mouse. In other words, the practice of CT that went into this invention was misguided in that it took the wrong point of departure; or better, the point of departure itself was wrong, given the circumstances. The world of the mouse and the keyboard do not require more than one button to regulate the movement of the cursor. Even if there are two or three buttons on a mouse, our hunch is that most users will be content to use just the one, and resort to the other, or other two, only in cases of necessity, such as when executing special functions (which, of course, could also be realized in other ways, too, just as it is the case with most of the mouse’s functions, which can be replaced by a control stroke plus a character, if we desire to avoid tendonitis and computercaused carpal tunnel syndrome and other side benefits of ‘mousing’!)

Conclusion As the above examples and discussions have shown, the need to do CT does not stop at the edge of the human-computer interface. CT penetrates far down into the reaches where the cognitive mind reigns supreme, and from where it prepares its forays into the world of technology. In the other direction, CT reaches out to the farthest shores of technological development, bringing back the lost tribes of tooling humans into the cognitive fold. In this perspective, we can think of prostheses and other (computerized) tools as instruments helping to effect this return to humanity, the cognitive tools being extensions of the

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human body (such as in the case of the artificial limb) and of the human mind (as in Gorayska and Lindsay’s ‘fabricated world’ or Clark’s ‘wideware’), but at the same time devices that change the body and inspire the mind. An analogy from music may help us to understand this double problematic. It has become common place these days among music buffs to demand that baroque and other older music be played on ‘original’ instruments. That usually means that we construct instruments that are as close as possible to their originals (which, in itself, is a dubious proposition: no modern Stradivarius will ever come close to the beauty of sound and shape that is inherent in the few remaining originals). But one could attack the problem of reproducing technology in a quite different way, viz., by asking: If Bach had lived today, would he have been happy to only play on his old foot-and-hand driven, bellow-powered monster of a church organ, if a modern, electrically powered and electronically steered instrument were available to him? Would Beethoven have preferred to hammer out his sonatas on the ‘Hammerklavier’ rather than on a contemporary Yamaha or Steinway grand? (Of course, he was deaf anyway, so perhaps it wouldn’t matter in his case…) We think the answer is obvious. Being great minds, not merely great composers and/or performers, these cognitive geniuses would have recognized the tool for what it was: an extension of the mind, not an impediment to its development. They would have jumped at the innovative potential of the new tools, and have dispatched, perhaps with a sad smile, the old instruments to the junkyard of musical history, to be retrieved only for nostalgic purposes. Using the new tools, they would have created a totally new music, in dialectical interaction with their modern instruments. On the other hand, using the retro technology of our days, the most we can do in playing Bach on a replicated 17th century clavichord is to mechanically re-create the old work, but without the inspiration and interaction that went into the original performance, which itself was a unique product of the dialectics between then-cognition and then-technology. Similarly, when dealing with modern technological and cognitive artifacts, it behooves us to take a bold stance and demand of ourselves that we face the challenges hands-on and with open eyes. Technological cognition inspires cognitive technology; it should never clamp the brakes on our development, whether cognitive or technological. Cognitive Technology, as a scholarly discipline, sees it as its purpose in life to offer a modest contribution towards realizing that aim, a push in what we believe is the right direction.

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References Ali, S. M.(2002). The end of the “Dreyfus Affair”: (Post)Heideggerian meditations on man, machine and meaning. International Journal of Cognition and Technology, 1(1), 85–96. Reprinted in this volume. Balasubramanian, N. V., B. Gorayska & J. Marsh (1993). Establishment of a Cognitive Technology research group. Technical Report TR-93-04. Department of Computer Science. Hong Kong: City University of Hong Kong. Beach, K. D. (1988). The role of external mnemonic symbols in acquiring an occupation. In M. N. Gruneberg & R. N. Sykes (Eds.), Practical Aspects of Memory: Current Research and Issues 1, pp. 342–346. New York: Wiley. Beynon, M. (1995). Empirical modelling for educational technology. In J. Marsh, C. L. Nehaniv & B. Gorayska (Eds.), Proceedings of the Second International Cognitive Technology Conference: 54–68. August 25–28, Aizu, Japan Blackwell, A. F., C. Britton, A. Cox, T. R. G. Green, C. Gurr, G. Kadodo, M. S. Kutar, M. Loomes, C. L. Nehaniv, M. Petre, C. Roast, C. Roe, A. Wong & R. M. Young (2001). Cognitive dimensions of notations: Design tools for Cognitive Technology. In M. Beynon, C. L. Nehaniv & K. Dautenhahn (Eds.), Cognitive Technology: Instruments of mind, CT01. Lecture Notes in AI 2117, pp. 325–341. Berlin: Springer. Bowman, S., L. Hinkley, J. Barnes & R. Lindsay (this volume). Gaze aversion and the primacy of emotional dysfunction in autism. In B. Gorayska & J. L. Mey (Eds.), Cognition and Technology: Co-existence, convergence, co-evolution, pp. 267–301. Amsterdam: John Benjamins. Card, S. K., T. Moran & A. Newell (1980). The keystroke-level model for user performance time with interactive systems. Communications of the ACM, 23: 396–410. Card, S. K., T. Moran & A. Newell (1983). The psychology of human-computer interaction. Hillsdale, N. J.: Erlbaum. Chan, H-M. & B. Gorayska (2001). Critique of pure technology. In M. Beynon, C. L. Nehaniv & K. Dautenhahn (Eds.), Cognitive Technology: Instruments of mind, CT01. Lecture Notes in AI 2117, pp. 463–476. Berlin: Springer. Chandrasekharan, S. (this volume). The Semantic Web: Knowledge representation and affordance. In B. Gorayska & J. L. Mey (Eds.), Cognition and Technology: Co-existence, convergence, co-evolution, pp. 153–172. Amsterdam: John Benjamins. Clark, A. (1997). Being there: Putting the brain, body, and world together again. Cambridge, Mass.: MIT Press. Clark, A. (2001a). Natural-born cyborgs? In M. Beynon, C. L. Nehaniv & K. Dautenhahn (Eds.), Cognitive Technology: Instruments of mind, CT01. Lecture Notes in AI 2117, pp. 17–24. Berlin: Springer. Clark, A. (2001b). Mindware. Oxford: Oxford University Press. Clark, A. (2002). Towards a science of the bio-technological mind. International Journal of Cognition and Technology 1(1), 21–33. Reprinted in this volume. CT’95 (1993). Call for Papers for the First International Cognitive Technology Conference. Internet publication. Hong Kong: City University of Hong Kong.

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Dascal, M. (2002). Language as a Cognitive Technology. International Journal of Cognition and Technology 1(1), 35–61. Reprinted in this volume. Dautenhahn, K. (2002). The origins of narrative: In search of the transactional format of narratives in humans and other social animals. International Journal of Cognition and Technology 1(1), 97–123. Reprinted in this volume. Ehn, P. (1988). Work-oriented design of computer artifacts. Stockholm: Arbetslivscentrum (distributed by Almquist & Wiksell International). El Ashegh, H. A. & R. Lindsay (this volume). Cognition and body image. In B. Gorayska & J. L. Mey (Eds.), Cognition and Technology: Co-existence, convergence, co-evolution, pp. 175–223. Amsterdam: John Benjamins. Fitzgerald, W. (this volume). Martin Luther King and the “Ghost in the Machine”. In B. Gorayska & J. L. Mey (Eds.), Cognition and Technology: Co-existence, convergence, coevolution, pp. 345–353. Amsterdam: John Benjamins. Gill, K. S (1996). Information Society: New media, ethics and Postmodernism. Berlin: Springer. Gill, S. (this volume). Body moves and tacit knowing. In B. Gorayska & J. L. Mey (Eds.), Cognition and Technology: Co-existence, convergence, co-evolution, pp. 241–265. Amsterdam: John Benjamins Publishing Company. Gorayska, B. (1994). How to lose the soul of language. Journal of Pragmatics 22: 536–547. Gorayska, B. & K. Cox (1992). Expert systems as extensions of the human mind. AI & Society 6: 245–262. Gorayska, B. & R. Lindsay (1989a). Metasemantics of relevance. The First International Congress on Cognitive Linguistics. Print A265. L. A. U. D. (Linguistic Agency at the University of Duisburg) — Catalogue: Pragmatics, 1989. Available from http:// www.linse.uni-essen.de:16080/linse/laud/shop_laud. Gorayska, B. & R. Lindsay (1989b) On relevance: Goal dependent expressions and the control of planning processes. Technical Report 16. School of Computing and Mathematical Sciences. Oxford: Oxford-Brookes University. (First published as Gorayska and Lindsay 1989a). Gorayska, B. & R. Lindsay (1993). The roots of relevance. Journal of Pragmatics 19(4): 301–323. Gorayska, B. & J. Marsh (1996). Epistemic technology and relevance analysis: Rethinking Cognitive Technology. In B. Gorayska & J. L. Mey (Eds.), Cognitive Technology: In search of a humane interface, pp. 27–39. Amsterdam: North Holland. Gorayska, B. & J. Marsh (1999). Investigations in Cognitive Technology: Questioning perspective. In B. Gorayska, J. Marsh & J. L. Mey (Eds.) Humane interfaces: Questions of methods and practice in Cognitive Technology, pp. 17–43. Amsterdam: North Holland. Gorayska, B., J. Marsh & J. L. Mey (2001). Cognitive Technology: Tool or instrument. In Meurig Beynon, Chrystopher L. Nehaniv & Kerstin Dautenhahn (Eds.), Cognitive Technology: Instruments of mind, CT01. Lecture Notes in AI 2117, pp. 1–16. Berlin: Springer. Gorayska, B. & J. L. Mey (1994). Cognitive Technology. In Karamjit S. Gill (Ed.) Proceedings of the conference on New Visions of the Post-industrial Society: The paradox of technological and human paradigms, SEAKE Centre, Brighton 1994. Reprinted in Karamjit Gill (Ed.) 1996, pp. 287–294.

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Gorayska, B. & J. L. Mey (1996a). Of minds and men. In B. Gorayska & J. L. Mey (Eds.), Cognitive Technology: In search of a humane interface, pp. 1–24. Amsterdam: North Holland. Gorayska, B. & J. L. Mey (Eds.) (1996b). Cognitive Technology: In search of a humane interface. Amsterdam: North Holland. Gorayska, B. & J.L. Mey (Eds.) (1996c). AI & Society 10, Special Issue on Cognitive Technology. Hutchins, E. L., J. D. Hollan & D. A. Norman (1986). Direct manipulation interfaces. In D. A. Norman & S. W. Draper (Eds.) User-centered computer design, pp. 87–124. Hillsdale, N. J.: Erlbaum. Janney, R. W. (1999). Computers and psychosis. In J. P. Marsh, B. Gorayska & J. L. Mey (Eds.), Humane Interfaces: Questions of methods and practice in Cognitive Technology, pp. 71–79. Amsterdam: Elsevier Science. (An earlier version of this paper appeared as Janney, R. W. (1997). The prosthesis as partner: Pragmatics and the Human-Computer Interface. In J. P. Marsh, C. L. Nehaniv & B. Gorayska (Eds.), Proceedings of the Second International Cognitive Technology Conference CT’97: Humanizing the Information Age, pp. 1–6. IEEE Computer Society Press.) Jirotka, M. & P. Luff (this volume). Communicating sequential activities: An investigation into the modelling of collaborative action for system design. In B. Gorayska & J. L. Mey (Eds.), Cognition and Technology: Co-existence, convergence, co-evolution, pp. 303–330. Amsterdam: John Benjamins. Kant, I. (1781). Critique of pure reason. (Translated by Norman Kemp Smith.) London: MacMillan, 1933. Kirlik, A. (1988). Everyday life environments. In W. Bechtel and G. Graham (Eds.), A Companion to Cognitive Science, pp. 702–712. Oxford: Blackwell. Kutar, M. S., C. L. Nehaniv, C. Britton & S. Jones (2001). The Cognitive dimensions of an artifact vis-à-vis individual human users: Studies with notations for the temporal specification of interactive systems. In M. Beynon, C. L. Nehaniv & K. Dautenhahn (Eds.), Cognitive Technology: Instruments of mind, CT01. Lecture Notes in AI 2117, pp. 344–355. Berlin: Springer. Lindsay, R. (1996). Cognitive Technology and the pragmatics of impossible plans — a study in Cognitive Prosthetics. AI & Society. 10, 273–288. Special issue on Cognitive Technology. Lindsay, R. (1999). Can we change our minds? The impact of computer technology on human cognition. In B. Gorayska, J. Marsh & J. L. Mey (Eds.) Humane interfaces: Questions of methods and practice in Cognitive Technology, pp. 45–69. Amsterdam: North Holland. Lindsay, R. & B. Gorayska (2002). Relevance, goal management and Cognitive Technology. International Journal of Cognition and Technology 1(2), 187–232. Reprinted in this volume. Lueg, C. (2002). Looking under the rug: Context and context aware artifacts. International Journal of Cognition and Technology 1(2), 287–302. Marsh, J., B. Gorayska, & J. L. Mey (Eds.) (1999). Humane interfaces: Questions of methods and practice in Cognitive Technology. Amsterdam: North Holland. Meenan, S. & R. Lindsay (2002). Planning and the neurotechnology of social behaviour. International Journal of Cognition and Technology 1(2), 233–274.

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Mey, J. L. (1988). CAIN and the invisible tool: Cognitive Science and the Human-Computer Interface. Journal of the Society of Instrument and Control Engineers (Tokyo) 27(1), 247–252. Mey, J. L. (1995). Cognitive Technology — Technological Cognition. In: Proceedings of the First International Cognitive Technology Conference, August 1995, Hong Kong. Reprinted in AI & Society (1996) 10, 226–232. Mey, J. L. (1998). Adaptability. In: Concise encyclopedia of pragmatics, pp. 5–7. Oxford: Elsevier Science. Mey, J. L. (2000). The computer as prosthesis. Hermes, Journal of Linguistics 24, 14–29. Mithen, S. J. (1996). The prehistory of the mind: A search for the origins of art, religion and science. London: Orion Books Ltd. Norman, D. A. (1986). Cognitive Engineering. In D. A. Norman & S. A. Draper (Eds.), User Centred Systems Design, pp. 31–63. Hillsdale, N. J.: Erlbaum. Norman, D. A. (1993). Things that make us smart. Reading, Mass.: Addison-Wesley. Norman, D. A. (1999). The invisible computer. Cambridge, Mass.: MIT Press. Norman, D. A. & S. W. Draper (Eds.) (1986). User-centered computer design. Hillsdale, N. J.: Erlbaum. Pfeifer, R. (2002). Robots as cognitive tools. International Journal of Cognition and Technology 1(1), 125–143. Reprinted in this volume. Rassmussen, J. (1988). Information Processing and Human-Machine Interaction: An approach to Cognitive Engineering. New York: North Holland. Salomon, G. (Ed.). (1993). Distributed cognition: Psychological and educational considerations. Cambridge: Cambridge University Press. Segall, M. H., D. T. Campbell & M. J. Herskovits (1966). The influence of culture on visual perception. Indianapolis: Bobbs-Merrill. Wickens, C. (1992). Engineering Psychology and Human Performance. 2nd edition. New York: Harper Collins. Wittgenstein, L. (1995). Philosophical Investigations. Oxford: Blackwell.

Part I

Theoretical issues

Towards a science of the bio-technological mind* Andy Clark Indiana University

“Soon, perhaps, it will be impossible to tell where human ends and machines begins”. Maureen McHugh, China Mountain Zhang, p. 214.

1.

A sketch

The study of Cognitive Technology is, in a very real sense, the study of ourselves. Who we are, what we are, and even where we are, are all jointly determined by our biological natures and the web of supporting (and constraining) technologies in which we live, work and dream. We humans, I would argue, are naturally pre-disposed (in ways unique to our species) to create cascading torrents of non-biological structure within which to think and act. We do not need neural implants and prosthetic limbs to count as Nature’s very own Cyborgs. For we are, and long have been, bio-technological symbionts: reasoning and thinking systems spread across biological matter and the delicately codetermined gossamers of our socio-technological nest. This tendency towards bio-technological hybridisation is not an especially modern development. On the contrary, it is an aspect of our humanity which is as basic and ancient as the use of speech, and which has been extending its territory ever since. We see some of the ‘cognitive fossil trail’ of the Cyborg trait in the historical procession of potent Cognitive Technologies that begins with speech and counting, morphs first into written text and numerals, then into early printing (without moveable typefaces), on to the revolutions of moveable typefaces and the printing press, and most recently to the digital encodings that bring text, sound and image into a uniform and widely transmissible format. Such technologies, once up-and-running in the various appliances and institutions

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that surround us, do far more than merely allow for the external storage and transmission of ideas. They actively re-structure the forms and contents of human thought itself. And there is no turning back. What’s more, the use, reach and transformative powers of these technologies are escalating. New waves of user-sensitive technology will bring this ageold process to a climax, as our minds and identities become ever more deeply enmeshed in a non-biological matrix of machines and tools, including software agents, vast searchable databases, and daily objects with embedded intelligence of their own. We humans have always been adept at shaping our minds and skills to fit our current tools and technologies. But when those tools and technologies start trying to fit us, in turn — when our technologies actively, automatically, and continually tailor themselves to us, just as we do to them — then the line between tool and user becomes flimsy indeed. Such technologies will be less like tools and more like part of the mental apparatus of the person. They will remain tools in only the thin and ultimately paradoxical sense in which my own unconsciously operating neural structures (my hippocampus, my posterior parietal cortex) are tools. I do not really ‘use’ my brain. There is no user quite so ephemeral. Rather, the operation of the brain makes me who and what I am. So, too, with these new waves of sensitive, interactive technologies. As our worlds become smarter, and get to know us better and better, it becomes harder and harder to say where the world stops and the person begins. What are these technologies? They are many, and various. They include potent, portable machinery linking the user to an increasingly responsive worldwide-web. But they include also, and perhaps ultimately more importantly, the gradual smartening-up and interconnection of the many everyday objects which populate our homes and offices. This brief note, however, is not going to be about new technology. Rather, it is about us, about our sense of self, and about the nature of the human mind. The goal is not to guess at what we might soon become, but to better appreciate what we already are: creatures whose minds are special precisely because they are naturally geared for multiple mergers and coalitions. Cognitive technologies, ancient and modern, are best understood (I suggest) as deep and integral parts of the problem-solving systems we identify as human intelligence. They are best seen as proper parts of the computational apparatus that constitutes our minds. If we do not always see this, or if the idea seems outlandish or absurd, that is because we are in the grip of a simple prejudice: the prejudice that whatever matters about MY mind must depend solely on what goes on inside my own biological skin-bag, inside the ancient fortress of skin and skull. But this fortress has been built to be breached. It is a

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structure whose virtue lies in part in it’s capacity to delicately gear its activities to collaborate with external, non-biological sources of order so as (originally) to better solve the problems of survival and reproduction. Thus consider two brief examples: one old (see the Epilogue to Clark, 1997) and one new. The old one first. Take the familiar process of writing an academic paper. Confronted, at last, with the shiny finished product the good materialist may find herself congratulating her brain on its good work. But this is misleading. It is misleading not simply because (as usual) most of the ideas were not our own anyway, but because the structure, form and flow of the final product often depends heavily on the complex ways the brain co-operates with, and depends on, various special features of the media and technologies with which it continually interacts. We tend to think of our biological brains as the point source of the whole final content. But if we look a little more closely, what we may often find is that the biological brain participated in some potent and iterated loops through the cognitive technological environment. We began, perhaps, by looking over some old notes, then turned to some original sources. As we read, our brain generated a few fragmentary, on-the-spot responses which were duly stored as marks on the page, or in the margins. This cycle repeats, pausing to loop back to the original plans and sketches, amending them in the same fragmentary, on-the-spot fashion. This whole process of critiquing, re-arranging, streamlining and linking is deeply informed by quite specific properties of the external media, which allow the sequence of simple reactions to become organised and grow (hopefully) into something like an argument. The brain’s role is crucial and special. But it is not the whole story. In fact, the true power and beauty of the brain’s role is that it acts as a mediating factor in a variety of complex and iterated processes which continually loop between brain, body and technological environment. And it is this larger system which solves the problem. We thus confront the cognitive equivalent of Dawkins’ (1982) vision of the extended phenotype. The intelligent process just is the spatially and temporally extended one which zigzags between brain, body and world. Or consider, to take a superficially very different kind of case, the role of sketching in certain processes of artistic creation. Van Leeuwen, Verstijnen and Hekkert (1999) offer a careful account of the creation of certain forms of abstract art, depicting such creation as heavily dependent upon “an interactive process of imagining, sketching and evaluating [then re-sketching, re-evaluating, etc.]” (Op. cit., p. 180). The question the authors pursue is: why the need to sketch? Why not simply imagine the final artwork “in the mind’s eye” and then execute it directly on the canvas? The answer they develop, in great detail and

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using multiple real case-studies, is that human thought is constrained, in mental imagery, in some very specific ways in which it is not constrained during on-line perception. In particular, our mental images seem to be more interpretatively fixed: less able to reveal novel forms and components. Suggestive evidence for such constraints includes the intriguing demonstration (Chambers and Reisberg, 1989) that it is much harder to discover (for the first time) the second interpretation of an ambiguous figure (such as the duck/rabbit) in recall and imagination than when confronted with a real drawing. Good imagers, who proved unable to discover a second interpretation in the mind’s eye, were able nonetheless to draw what they had seen from memory and, by then perceptually inspecting their own unaided drawing, to find the second interpretation. Certain forms of abstract art, Van Leeuwen et al. go on to argue, likewise, depend heavily on the deliberate creation of “multi-layered meanings” — cases where a visual form, on continued inspection, supports multiple different structural interpretations. Given the postulated constraints on mental imagery, it is likely that the discovery of such multiple interpretable forms will depend heavily on the kind of trial and error process in which we first sketch and then perceptually (not merely imaginatively) re-encounter visual forms, which we can then tweak and re-sketch so as to create a product that supports an increasingly multi-layered set of structural interpretations. This description of artistic creativity is strikingly similar, it seems to me, to our story about academic creativity. The sketch-pad is not just a convenience for the artist, nor simply a kind of external memory or durable medium for the storage of particular ideas. Instead, the iterated process of externalising and re-perceiving is integral to the process of artistic cognition itself. One useful way to understand the cognitive role of many of our self-created cognitive technologies is thus as affording complementary operations to those that come most naturally to biological brains. Consider here the connectionist image (Rumelhart, McClelland and the PDP Research Group, 1986; Clark, 1989) of biological brains as pattern-completing engines. Such devices are adept at linking patterns of current sensory input with associated information: you hear the first bars of the song and recall the rest, you see the rat’s tail and conjure the image of the rat. Computational engines of that broad class prove extremely good at tasks such as sensori-motor co-ordination, face recognition, voice recognition, etc. But they are not well-suited to deductive logic, planning, and the typical tasks of sequential reasoning. They are, roughly speaking, “Good at Frisbee, Bad at Logic” — a cognitive profile that is at once familiar and alien. Familiar, because human intelligence clearly has something of that flavour. Yet

Towards a science of the bio-technological mind

alien, because we repeatedly transcend these limits, planning family vacations, running economies, solving complex sequential problems, etc., etc. A powerful hypothesis, which I first encountered in Rumelhart, Smolensky, McClelland and Hinton (1986), is that we transcend these limits, in large part, by combining the internal operation of a connectionist, pattern-completing device with a variety of external operations and tools which serve to reduce various complex, sequential problems to an ordered set of simpler pattern-completing operations of the kind our brains are most comfortable with. Thus, to borrow the classic illustration, we may tackle the problem of long multiplication by using pen, paper and numerical symbols. We then engage in a process of external symbol manipulations and storage so as to reduce the complex problem to a sequence of simple pattern-completing steps that we already command, first multiplying 9 by 7 and storing the result on paper, then 9 by 6, and so on. The value of the use of pen, paper, and number symbols is thus that — in the words of Ed Hutchins; “[Such tools] permit the [users] to do the tasks that need to be done while doing the kinds of things people are good at: recognising patterns, modelling simple dynamics of the world, and manipulating objects in the environment.” (Hutchins, 1995, p. 155)

This description nicely captures what is best about good examples of cognitive technology: recent word-processing packages, web browsers, mouse and icon systems, etc. It also suggests, of course, what was wrong with many of our first attempts at creating such tools — the skills needed to use those environments (early VCR’s, word-processors, etc.) were precisely those that biological brains find hardest to support, such as the recall and execution of long, essentially arbitrary, sequences of operations. See Norman (1999) for further discussion. The conjecture, then, is that one large jump or discontinuity in human cognitive evolution involves the distinctive way human brains repeatedly create and exploit various species of cognitive technology so as to expand and re-shape the space of human reason. We — more than any other creature on the planet — deploy non-biological elements (instruments, media, notations) to complement our basic biological modes of processing, creating extended cognitive systems whose computational and problem-solving profiles are quite different from those of the naked brain. In this way human brains maintain an intricate cognitive dance with an ecologically novel, and immensely empowering, environment: the world of symbols, media, formalisms, texts, speech, instruments and culture. The computational circuitry of human cognition thus flows both within and beyond the head, through this extended network in ways which

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radically transform the space of human thought and reason. Such a point is not new, and has been well-made by a variety of theorists working in many different traditions. This brief and impressionistic sketch is not the place to delve deeply into the provenance of the idea, but some names to conjure with include Vygotsky, Bruner, Latour, Dennett, Hutchins, Norman and (to a greater or lesser extent) all those currently working on so-called ‘situated cognition’. My own work on the idea (see Clark 1997, 1998, 1999, 2001a) also owes much to a brief collaboration with David Chalmers (see our paper, ‘The Extended Mind’ in ANALYSIS 58(1), 1998, p. 7–19). I believe, however, that the idea of human cognition as subsisting in a hybrid, extended architecture (one which includes aspects of the brain and of the cognitive technological envelope in which our brains develop and operate) remains vastly underappreciated. We cannot understand what is special and distinctively powerful about human thought and reason by simply paying lip-service to the importance of the web of surrounding Cognitive Technologies. Instead, we need to understand in detail how our brains dovetail their problem-solving activities to these additional resources, and how the larger systems thus created operate, change and evolve. In addition, and perhaps more philosophically, we need to understand that the very ideas of minds and persons are not limited to the biological skin-bag, and that our sense of self, place and potential are all malleable constructs ready to expand, change or contract at surprisingly short notice.

2. Some questions The challenge, then, is to take these tempting but impressionistic ideas and to turn them into a genuine science (or sciences) of the technologically-scaffolded mind. This is new territory, and even the shape of the major problems and issues remains largely up for grabs. But some major ones look to me to include: 2.1 Origins Since no other species on the planet builds such varied, complex and constantly evolving designer environments as us, what is it that allowed this process to get off the ground in our species in such a spectacular way? Otherwise put, even if it is the designer environments that, in a familiar, boot-strapping kind of way make us what we now are, what biological difference lets us build/discover/use them in the first place?

Towards a science of the bio-technological mind

This is a serious, important and largely unresolved question. Clearly, there must be some (perhaps quite small) biological difference that lets us get our collective foot in the designer environment door — what can it be? (Contenders might include biological innovations for greater neural plasticity, and/or the extended period of protected learning called “childhood”. (See Quartz, 1999; Quartz and Sejnowski, 1997; Griffiths and Stotz, 2000.) Thus, Griffiths and Stotz argue that the long human childhood provides a unique window of opportunity in which “cultural scaffolding [can] change the dynamics of the cognitive system in a way that opens up new cognitive possibilities” (op. cit.) These authors argue against what they nicely describe as the “dualist account of human biology and human culture” according to which biological evolution must first create the “anatomically modern human” and is then followed by the long and ongoing process of cultural evolution. Such a picture, they suggest, invites us to believe in something like a basic biological human nature, gradually co-opted and obscured by the trappings and effects of culture and society. But this vision (which is perhaps not so far removed from that found in some of the more excessive versions of evolutionary psychology) is akin, they argue, to looking for the true nature of the ant by “removing the distorting influence of the nest”. Instead we humans are, by nature, products of a complex and heterogeneous developmental matrix in which culture, technology and biology are pretty well inextricably intermingled. In short it is a mistake to posit a biologically fixed “human nature” with a simple “wrap-around” of tools and culture. For the tools and culture are indeed as much determiners of our nature as products of it. 2.2 Our self-image as a species How should the recognition of ourselves as naturally bio-technological hybrids affect our views of human nature? How do these ideas fit with, or otherwise impact, accounts which emphasize ancestral environments (see, e.g., Pinker, 1997)? At the very least we must now take into account a plastic evolutionary overlay which yields a constantly moving target, an extended cognitive architecture whose constancy lies mainly in its continual openness to change. Even granting that the biological innovations which got this ball rolling may have consisted only in some small tweaks to an ancestral repertoire, the upshot of this subtle alteration was a massive leap in cognitive potential. For our cognitive machinery is now intrinsically geared to self-transformation, artifact-based expansion, and a snowballing/bootstrapping process of computational and

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representational growth. The machinery of human reason (the environmentally extended apparatus of our distinctively human intelligence) may thus be rooted in a biologically incremental progression while simultaneously existing on the far side of a precipitous cliff in cognitive-architectural space. 2.3 Social policy Educational policy, and social policy in general, need to be geared to our best current scientific image of the human mind. What educational and social policies best serve a race of constantly changing bio-technological hybrids? How can contemporary art help us to better understand these aspects of our nature? (Performance artists like Stelarc (www.stelarc.va.com.au) are tackling this latter issue head-on, with work in which the biological and technological merge and change places). 2.4 The mechanisms of co-adaptation The complex fit between biological brains and technological scaffolding depends on a two way give-and-take. Brains need to be plastic enough to factor the technologies deep into their problem-solving routines. And the technologies need (over cultural-evolutionary time at first, and most recently, during their own life-spans) to adapt to better fit the users. We urgently need to understand the multiple factors and forces that shape this complex dynamic. (See, e.g., Norman’s (1999) work on ‘human-centered technologies’ and Daniel Dennett’s (1995) work on the ‘Cranes of Culture’.) 2.5 Types of scaffolding The single most important task, it seems to me, is to better understand the range and variety of types of cognitive scaffolding, and the different ways in which non-biological scaffoldings can augment (or impair) performance on a task. For example, there is interesting work comparing reasoning using selfconstructed external props (e.g., a diagram you draw to help you think about a problem) and reasoning using ‘found’ or given props (the diagrams in a textbook, say). There is also work on the role of individual differences (of cognitive style, etc.) and their impact on the usefulness of specific types of external structure. (For both the above, see Cox, 1999.) And there are detailed studies of the specific properties of various kinds of prop and aid (e.g., Scaife and Rogers (1996) work

Towards a science of the bio-technological mind

on graphical representations). These bodies of work cry out for extension and unification. The Holy Grail here is a taxonomy of different types of external prop, and a systematic understanding of how they help (and hinder) human performance. Such an understanding would also have immediate implications for the design process itself (see Norman, 1999; Dourish, 2001). 2.6 Collective effects A major part of our cognitive environment is other people, and their distinctive knowledge-bases. How can new technologies help us make the most of this resource? The use of collaborative filtering techniques, in which individuals’ activities leave electronic traces that can be used to automatically generate new knowledge (e.g., the familiar Amazon prompt: people who bought such-andsuch also liked….) is one simple tool whose power is not yet fully appreciated or exploited. But the potential is vast. For some discussion, see Bonabeau and Theraulez (2000). 2.7 Frameworks and organizing principles What general principles and concepts will allow us to make systematic sense (indeed, to make a science) of the bio-technological mind? Should we (following Hutchins, 1995) think in terms of the flow and transformation, through a series of external tools and media, of representations? This is, in effect, to extend traditional cognitive scientific approaches to mind outwards. Or should we be creating new analytic tools and approaches, perhaps borrowing ideas from dynamical systems theory and the study of complex, coupled systems (see, e.g., Thelen and Smith, 1994; Kelso, 1995; and discussion in Clark, 1997). What key concepts will help make unified sense of the complex and varied roles of external scaffolding? Contenders include: Off-loading
: Several writers (e.g., Dennett, 1996, pp. 134–135) stress the way cognitive technologies can be used to ‘off-load’ work from the biological brain to external arenas. Complementarity
: While straightforward off-loading certainly occurs, especially with regard to shifting stuff from our limited short-term memory out into the world, it is surely only part of the story. In my own recent work (e.g., Clark, 2001b, ch. 8), the focus is more on complementarity, and on the way external stuff can be configured so as to do the kinds of thing that biological brains

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either don’t do at all, or do fairly badly (think of the role of the artist’s sketchpad as described above). Transformation
: Yet another approach (Rumelhart et al., 1986; Clark, 1998) takes the key notion to be that of problem transformation. Here, the external aids turn the problems that need to be solved (to perform a given task) into the kinds of problems brains like ours like best (e.g., using pen and paper to transform complex arithmetical tasks into sequences of simple ones). Stabilization of Environments
: Hammond et al. (1995) deploy the notion of ‘environmental stabilization’ to analyze the problem-solving activity of embodied, environmentally active artificial agents. Such agents act so as to keep the environment steady in ways that allow the easy reuse of once-successful plans and stratagems (e.g., putting pots and pans away in the same places, so as to reuse a cooking routine…). Do we need just one or all of these concepts (and are there more?)? Do they all emerge as special cases of some deeper organizing principle? And within what kinds of explanatory framework are they best deployed?

3. Conclusions The project of understanding human thought and reason is easily misconstrued. It is misconstrued as the project of understanding what is special about the human brain. No doubt there is something special about our brains. But understanding our peculiar profiles as reasoners, thinkers and knowers of our worlds requires an even broader perspective: one that targets multiple brains and bodies operating in specially constructed environments replete with artifacts, external symbols, and all the variegated scaffoldings of science, art and culture. Understanding what is distinctive about human reason thus involves understanding the complementary contributions of both biology and (broadly speaking) technology, as well as the dense, reciprocal patterns of causal and coevolutionary influence that run between them. Turning this kind of vision into a genuine science of the bio-technological mind is a massive task, calling for interdisciplinary co-operation on a whole new scale. I hope and believe that this volume will contribute to a crucial forum for that important endeavor.

Towards a science of the bio-technological mind

Note * Section 1 is an expanded version of a text which appeared as “Natural-Born Cyborgs” in John Brockman’s Reality Club (2000). It is electronically published at http://www.edge.org, and is reproduced with permission.

References Bonabeau, E. & G. Theraulaz (2000). Swarm Smarts. Scientific American 282(3), 72–79. March 2000. Chambers, D. & D. Reisberg (1989). Can Mental Images Be Ambiguous? Journal of Experimental Psychology: Human Perception and Performance II(3), 317–328. Clark, A. (1989). Microcognition: Philosophy, Cognitive Science and Parallel Distributed Processing. Cambridge, MA: MIT Press. Clark, A. (1997). Being There: Putting Brain, Body and World Together Again. Cambridge, MA: MIT Press. Clark, A. (1998). Magic Words: How Language Augments Human Computation. In J. Boucher & P. Carruthers (Eds.), Language and Thought, pp. 162–183. Cambridge: Cambridge University Press. Clark, A. (1999). An Embodied Cognitive Science? Trends In Cognitive Sciences 3(9), 345–351. Clark, A. (2001a). Reasons, Robots and The Extended Mind. Mind And Language 16(2), 121–145. Clark, A. (2001b). Mindware: An Introduction to the Philosophy of Cognitive Science. New York & Oxford: Oxford University Press. Clark, A. & D. Chalmers (1998). The Extended Mind. Analysis 58, 7–19. Cox, R. (1999). Representation, Construction, Externalised Cognition and Individual Differences. Learning and Instruction 9, 343–363. Dawkins, R. (1982). The Extended Phenotype. New York & Oxford: Oxford University Press. Dennett, D. (1995). Darwin’s Dangerous Idea. New York: Simon and Schuster. Dennett, D. (1996). Kinds of Minds. New York: Basic Books. Dourish, P. (2001). Where The Action Is. Cambridge, MA: MIT Press. Griffiths, P. E. & K. Stotz (2000). How the mind grows: A developmental perspective on the biology of cognition. Synthese. 122(1–2), 29–51. Hammond, K, T. Converse & J. Grass (1995). The Stabilization of Environments. In P. Agre & S. Rosenschein (Eds.), Computational Theories of Interaction and Agency, pp. 304–327. Cambridge, MA: MIT Press. Hutchins, E. (1995). Cognition In The Wild. Cambridge, MA: MIT Press. Kelso, S. (1995). Dynamic patterns. Cambridge, MA: MIT Press. Latour, B. (1993). We Have Never Been Modern. Cambridge, MA: Harvard University Press. Norman, D. (1999). The Invisible Computer. Cambridge, MA: MIT Press. Pinker, S. (1997). How the Mind Works. New York: Norton.

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Quartz, S. (1999). The Constructivist Brain. Trends In Cognitive Science. 3(2), 48–57. Quartz, S. & T. Sejnowski (1997). The Neural Basis of Cognitive Development: A Constructivist Manifesto. Behavioral and Brain Sciences. 20, 537–596. Rumelhart, D., P. Smolensky, D. McClelland & G. Hinton (1986). Schemata and Sequential Thought Processes in PDP Models. In J. L. McClelland, D. E. Rumelhart, & the PDP Research Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition 2, pp. 7–57. Cambridge, MA: MIT Press. Scaife, M. & Y. Rogers (1996). External Cognition: How Do Graphical Representations Work? International Journal of Human-Computer Studies. 45, 185–213. Thelen, E. & L. Smith (1994). A Dynamic Systems Approach to the Development of Cognition and Action. Cambridge, MA: MIT Press. Van Leeuwen, C. I. Verstijnen & P. Hekkert (1999). Common Unconscious Dynamics Underlie Common Conscious Effects: A Case Study in the Iterative Nature of Perception and Creation. In J. Scott Jordan (Ed.), Modeling Consciousness Across the Disciplines, pp. 179–219. Lanham, MD: University Press of America.

Language as a cognitive technology* Marcelo Dascal Tel Aviv University

1.

Introduction

In our time we live surrounded by objects and devices we have created that are able to perform complex tasks, whose performance used to demand much concentration and mental effort by us. One can say that we have managed to transfer to these objects and devices some of the capacities that were considered — until not long ago — typical and exclusive to the human intellect. In this sense, we have created “cognitive artifacts” (Hutchins, 1999) or, more generally, “cognitive technologies” (Gorayska and Mey, 1996). These inventions save a considerable portion of our intellectual power, setting it free for the performance of higher cognitive tasks — those for which we have not yet been — and, as believed by some (e.g., Dreyfus, 1971, 1992), will never be — able to invent mechanical, computational or other kinds of ersatz. Behind every technology created by humankind — be it the wheel, agriculture, or the cellular telephone — there is, of course, a lot of cognitive effort. But this does not make it, as such, a cognitive technology in the sense in which I propose to use this expression. What I have in mind are the main uses of a technology, rather than the process of its creation or its effects. Primarily, the wheel serves for transportation; agriculture, for food production; the cellular phone, for communication. Secondarily, these technologies can also be useful for cognition: transportation allows us to participate in scientific conferences where we are supposed to learn something; nourishing ourselves gives us also mental energy; the cellular phone can occasionally serve to communicate cognitive content. Cognitive technologies, however, are those that either have been designed for cognitive uses or else have been appropriated for such uses. They can of course retain their original uses and have non-cognitive effects such as the production of jobs, the waging of war, or space travel. By ‘cognitive technology’ (CT) I mean, thus, every systematic means — material or mental — created by humans that is significantly and routinely used

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for the performance of cognitive aims.1 By ‘cognitive aims’ I mean either mental states of a cognitive nature (e.g., knowledge, opinion, belief, intention, expectation, decision, plan of action) or cognitive processes (e.g., perception, memorization, conceptualization, classification, learning, anticipation, the formulation of hypotheses, demonstration, deliberation, evaluation, criticism, persuasion, discovery) that lead to cognitive states or help to reach them.2 Natural languages (NL), unlike formal languages created for specific purposes, can hardly be considered — as such — as prototypical ‘artifacts’, for they have not been purposefully ‘designed’. Yet they evolved — genetically and culturally — in view of certain human needs, and some of their features may have been appropriated (deliberately or not) in order to satisfy needs other than those whose pressure caused them to emerge in the first place. In-so-far as such needs are ‘cognitive’, it seems to me appropriate to view the corresponding features of natural languages and their use as ‘cognitive technologies’. Researchers and developers in many fields of technology have been increasingly interested in natural languages. In an ad published in Time magazine a few years ago, the Daimler-Benz corporation asks, “How will man and machine work together in the future?”, and replies: “With the spoken word”. The ad reveals that one of the corporation’s divisions “is currently researching and developing sophisticated new word-based systems”, which will bring to life the Biblically sounding prophecy: “Man will talk to Machine and Machine will respond — for the benefit of Man”. Language-based technology also occupies a central position in MIT’s multimillion “Oxygen” project, which is heralded as the next revolution in information technology — comparable to those achieved by the introduction of the computer and the internet. Oxygen will “address people’s inherent need to communicate naturally: we are not born with keybord and mouse sockets but rather with mouths, ears and eyes. In Oxygen, speech understanding is built-in — and all parts of the system and all the applications will use speech” (Dertouzos, 1999, p. 39). The intense current interest in NL-based technologies, however, is almost entirely focused on one of its functions — human-machine communication. Thus, in spite of its central position in the Oxygen system, the designers seem to confine NL to its communicative role in the human-computer interface.3 For example, the system is intended to be able to satisfy “people’s need to find useful information” by being able to understand and respond adequately to a user who simply says “Get me the big red document that came a month ago” (Dertouzos, 1999, p. 39). This would be no doubt a great communicative achievement, for it would require endowing machines with advanced syntactic,

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semantic, as well as pragmatic processing abilities. But no less important is to realize how fundamental for satisfying the cognitive “need to find useful information” is to fully use the syntactic, semantic and pragmatic potential characteristic of NL — which is in fact what humans do in order to find useful information. The Daimler-Benz ad seems to come close to realizing the cognitive potential of NL, for its heading declares: “Language is the picture and counterpart of thought”. Nevertheless, the research it announces is primarily concerned with the interface issue: “And the machines will understand the words and respond. They will weld, or drive screws, or paint, or write — they will even understand different languages”. Such a focus on the use of spoken language as the upcoming predominant mode of human-machine interface orients research toward important issues such as auditory pattern recognition and automated voice production, identification of eventual sources of misunderstanding, elimination of artificial constraints in communication (naturalness), effortlessness (no need of special learning), comfort (user-friendliness), ubiquity (widespread use of miniaturized devices, with minimal energy requirements, literacy requirements, etc.), security (through voice recognition), and, more recently, human-computer conversation in specific environments, the incorporation of non-verbal channels in human-computer communication, and social agent technologies.4 Most of these applications rely upon sophisticated cognitive means, namely those that subtend the communicative use of language. But they are not, as such, “cognitive technologies” in the sense defined above. These technologies are concerned with the role of NL in the cognitive processes themselves, regardless of whether and how they may be communicated to other humans or machines. It is to this cognitive use of NL, so far overlooked by researchers and developers of new technologies, that the approach here proposed wants to call attention. In my opinion, until this potential of NL is tapped, the truly revolutionary effect of the technological appropriation of NL will not be achieved. In terms of the above definition of CT, the question of whether certain aspects of NL are properly viewed as CT’s is independent of the current or prospective state of technological development. In other words, this question is independent of the question whether a presumed CT aspect of language can be implemented by computational or other devices, enhanced by such devices, or used in interfaces with them. These questions depend upon the design and development of artifacts capable of simulating, enhancing or making use of the cognitive-technological features of NL, rather than upon the existence and use of such features themselves. Of course, the better we understand the nature of

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NL’s cognitive-technological functions, the better we will be in a position to develop the corresponding artifacts. We may eventually reach the conclusion that not all of these functions can be satisfactorily emulated by such artifacts. In this sense, the approach proposed here might provide some relevant input to the issue of “what computers can’t do”. I believe this approach will also be valuable for a better understanding of why a proper handling of the cognitive uses of language is crucial for the development of other, not necessarily linguistic, more efficient cognitive technologies, for the design of ‘humane’ interfaces, and — more generally — for the epistemology and philosophical anthropology of the “digital culture”. However, since my primary concern here is to show how several aspects of language and language use can be fruitfully conceptualized as cognitive technologies, the exploration of these further implications of such a conceptual shift will have to be left for another occasion. In the next section, I present a number of parameters in terms of which a typology of cognitive technologies in general can be outlined. Section 3 summarizes the main antagonistic positions in the traditional debate about the primacy of thought over language or language over thought and proposes to re-frame this debate with the help of the notion of cognitive technology. Section 4 analyzes some examples of linguistic structures and language use as possible candidates of language-based cognitive technologies. In the Conclusion (Section 5), I point out some of the gains to be derived from viewing language as a cognitive-technological resource.

2. Towards a typology of cognitive technologies In addition to their being directed at either cognitive states or processes — as pointed out in the Introduction — it is convenient to distinguish cognitive technologies according to other significant parameters. 2.1 ‘Strong’ and ‘weak’ cognitive technologies Mental states can be distinguished (from each other) according to their ‘modal’ qualifications. For instance, an epistemic state can be certain or probable, intuitive or explicit, definitive or hypothetical, justified or accepted without justification, etc. Cognitive processes, in turn, can be oriented towards reaching mental states endowed with any of these modalities. A logical or mathematical demonstration, for instance, leads to an epistemic state of definitive certainty, whereas argumentation or deliberation may lead at most to a doxastic state of

Language as a cognitive technology

holding a belief which, although well grounded, is only provisional.5 Cognitive technologies vary according to the modal aims of their designers. When they choose those modalities one could call ‘strong’ (certainty, irrevocability of the conclusions, etc.), they usually seek to endow the proposed technology with an algorithmic decision procedure which is entirely error-free and therefore irrevocable in its results. When they content themselves with ‘weak’ modalities, they can employ less rigid algorithms (e.g., non-monotonic or probabilistic logics), which do not ensure that the results cannot be called into question and modified. 2.2 ‘Integral’ and ‘partial’ cognitive technologies ‘Integral’ technologies are those that provide for the full execution of a given cognitive aim, without requiring any human intervention. ‘Partial’ technologies are those that provide only ‘helps’ for the performance of a given cognitive aim. These helps make it easier for a human agent to perform the task, but cannot dispense with his or her intervention. Often the designers’ ambitions lead them to propose maximalist projects of the first kind; but quite often, when they realize the enormous difficulties involved, they are likely to be less ambitious and satisfy themselves with partial technologies. The failure of the projects of full ‘mechanical translation’ in the 50’s and 60’s thus led — not without first wasting hundreds of millions of dollars — to the more modest current projects, in which the technologies developed merely suggest to the human translator alternative translations. The translator not only has to choose the most appropriate one; s/he must also edit it quite heavily in order to finally produce an acceptable text.6 This ‘evolutionary’ pattern from more to less ambitious technologies for a certain cognitive aim is quite frequent. However, maximalist ambitions tend to reappear whenever new technological and scientific developments make the conditions seem ripe for achieving ‘integral’ aims. 2.3 ‘Complete’ and ‘incomplete’ cognitive technologies One should further distinguish between the pragmatic notion of an ‘integral’ technology in the above sense and the notion of a syntactically and/or semantically ‘complete’ technology. The latter has to do with the ability of a technology to ‘cover’ completely a given domain or ensemble of ‘objects’ with respect to some desired property. For instance, if one creates an ‘alphabet of traffic signs’ in order to express through the combinations of its signs all the instructions to

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be given to drivers, and if the alphabetical system in question has no means to express one of these instructions, it is incomplete. It may be incomplete either due to the insufficiency of its formation rules or due to that of its transformation rules.7 In so-called non-standard logics, degrees of completeness are distinguished, so that one can talk of ‘weakly complete’ systems, ‘very weakly complete’ systems, and so on.8 2.4 ‘Constitutive’ and ‘non-constitutive’ cognitive technologies Some technologies are ‘constitutive’ of certain cognitive states or processes, whereas others are not. The former are such that without them certain cognitive operations cannot be performed. The latter, although extremely useful for the facilitation of the achievement of certain cognitive aims, are not a sine qua non for that. An example of the first kind could be the alleged necessity of supercomputers in order to generate very large numbers so as to be able to decide whether numbers endowed with certain arithmetical properties exist; or, more generally, the alleged necessary reliance on computational technologies in order to prove certain theorems. An example of the second kind is the dramatic increment in the efficacy of many of our cognitive performances thanks to computers, in spite of the fact that the latter have not (so far) become indispensable for the former. It is not easy to discern whether a given technology is constitutive or not. The endless debate about whether language is a necessary condition for thought, discussed in Section 3, illustrates such a difficulty. 2.5 ‘External’ and ‘internal’ cognitive technologies Cognitive technologies can be ‘external’ or ‘internal’. The former consist in physical devices or processes that are instrumental in achieving cognitive aims. The most ubiquitous example today of this kind of technology is the computer. But it is not the only one. Its predecessor the abacus, as well as paper and pencil, graphs and diagrams, and even the book can be included in this category. Discussions of cognitive technologies usually focus on such ‘external’ or ‘prosthetic’ — as they are often called — technologies. The significance of ‘internal’ technologies should not be overlooked, however. ‘Internal’ cognitive technologies are mental procedures thanks to which we can improve our cognitive activity. In this category one should include, for instance, mnemonic techniques that improve our capacity of storage of and access to information, formal methods of reasoning that permit to draw correct

Language as a cognitive technology

conclusions from given premises, definitions that clarify and fixate the meaning of concepts, and so on. Underlying these mental technologies there are — according to current belief — cerebral physical processes. But so far the mental has not yet been successfully reduced to its underlying neural layer. What characterizes the ‘internal’ technologies, even in cases where they employ external devices, is the fact that they are part and parcel of the cognitive processes themselves at the mental level, rather than attempts to reduce to or replace such processes by devices or processes operating at another level.

3. Language and thought: Re-framing the classical debate Language has been — and still is — conceived as having as its primary function communication. In this respect, it serves to convey thought or other forms of cognitive content, but need not play any role in the formation of the thoughts it conveys. Descartes, who considered the ability to use language appropriately in any context to be a distinctive trait of humans (as opposed to animals and machines) and insisted that this ability shows that humans have minds, categorically ruled out the possibility that language may be constitutive of thought processes such as reasoning, as suggested by Hobbes. In the same vein, Turing (1950) considered that success in linguistic communication is a test for determining the presence of intelligence in a machine, but did not claim that this would also show that intelligence consists in the ability to manipulate linguistic symbols.9 Both Descartes and Turing assumed that the capacity to use language appropriately for communication requires high cognitive abilities, and therefore can testify to the existence of “mind” or “intellect”. To argue that language itself has a crucial function in cognition would not only violate Descartes’s mindbody dualism (which perhaps wouldn’t bother Turing), but would also seem to involve an egg-chicken circularity that would certainly bother Turing, as it bothered many others earlier.10 As opposed to such a view of the relationship between language and mind as purely ‘external’, the former having only an indicative role vis-a-vis the latter, other thinkers have argued that language is much more intimately connected with mental life. For such thinkers, language plays an essential role in cognition. They argue that it is constitutive of fundamental cognitive processes (Hobbes), necessary for their performance (Leibniz), responsible for their historical emergence (Condillac), determinant of their structure and content (Whorf), required for their explanation (Sellars), the behavioral substrate of thinking and

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other mental processes (Watson), an essential component of the social, cultural or ontological context where thought and other aspects of mental life take place (Vygotsky, Mead, Geertz, Heidegger), and so on. The centuries-old debate on the nature of the relationship between language and thought was mesmerized by these polar positions regarding which one of them is, in some sense, “dependent” upon the other.11 Under close scrutiny, however, both sides in the debate acknowledge the existence of language-thought interactions that do not fit the sweeping versions of their claims. For example, avowed “externalists” like Bacon and Locke, undertake to criticize language as a dangerous source of cognitive mistakes and suggest methods (which gave rise to the attempt to elaborate “scientific” languages) to avoid such a danger. Yet, in so doing, they in fact admit that thought is not impervious to the influence of language. On the other side of the fence, Leibniz, who argued forcefully for the view that language and other semiotic systems are indispensable for even moderately complex forms of cognition, acknowledged the non-linguistic character of certain kinds of knowledge, such as “intuitive” and “clear but not distinct” knowledge. As in many other debates in the history of ideas, the tendency to focus on mutually exclusive dichotomous positions renders them insoluble and to some extent sterile. I have suggested elsewhere that, instead of focusing exclusively on the “primacy” or “dependency” issue when discussing the relationship between language and thought, it might be more useful to envisage the details of how language is actually used in mental processes.12 The application to language of the notion of cognitive technology as defined above provides, I submit, a fruitful way of further exploring this suggestion.

4. Language as environment, resource and tool of cognition Language’s presence in human life is overwhelming. Poets excelled in evoking the subtle ways in which words penetrate every corner of our mind,13 and — as we have seen in the preceding section — some thinkers have seen in language an essential and inevitable component of mental processes. This fact is not necessarily “good” or “useful”, if evaluated from the point of view of specific cognitive and practical aims. Hence the recurrent attempts to identify those aspects of language that are deemed to be “pernicious” and to propose a variety of “therapies” to filter them out. However justified it may be, such a critique testifies to the importance of language’s influence on cognition.

Language as a cognitive technology

Without going as far as Heidegger, who claimed that language is “the house of being”, I would say it is certainly a major component of the context of thinking.14 Without going as far as Geertz (1973, p. 83), who claimed that language, being one of our key “cultural resources”, is “ingredient, not accessory, to human thought”, I would rather emphasize that it is a ready-at-hand resource that thinking can easily make use of. Without suggesting, as does Watson, that thinking is nothing but sub-vocal speech,15 I would claim that certain linguistic resources do become sharp cognitive tools that afford the emergence and performance of certain types of cognition. The label “cognitive technology” is, of course, more straightforwardly applicable to those aspects of language that were shaped into cognitive tools, both because of their specific cognitive function and because they comport an element of “design”. But one should not overlook the fact that such tools emerge from a background where language’s potential and actual role as a cognitive environment and resource is unquestionable. In fact, the relationship between these three levels is dynamic and multi-directional. Just as “environmental” properties of language (e.g., sequential ordering) can give rise to resources (e.g., narrative structure) and thence to tools (e.g., explanatory strategies), so too a tool (e.g., a successful metaphor created in order to understand a new concept) can become a resource (a frozen metaphor) and then recede into the “environmental” background (e.g., by becoming incorporated into the semantic system as a lexical polysemy). 4.1 Language as environment As an environment of thought, language, through its sheer overwhelming presence in the mind, influences cognition independently of our awareness or will. Perhaps the most important of the environmental contributions of language to cognition derives from its general structural properties. Languages are articulated systems; linguists describe them as consisting in a “double articulation” comprising two different sets of basic units and principles of organization: units of meaning (lexemes or morphemes) and, say, units of sound (phonemes).16 These units, in turn, can be combined and recursively recombined in rule-governed ways at different levels — a mechanism that accounts for language’s impressive “generative” potential. This elaborate analytic-combinatorial system provides a natural model for other cognitive activities where the segmentation of complex wholes into parts, the “abstraction” of recurrent units and patterns from their actual context of use, and their use in any number of

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other contexts are performed. The application of such a model to cognitive needs other than strictly linguistic ones need not be deliberately undertaken, but the fact that we are familiar with it and master it perfectly in our daily language use certainly grants it a privileged position in our practice and conceptualization of how cognition in general does and should proceed. No wonder that Descartes, Leibniz, Locke and many other thinkers used this analytic-combinatorial model as the core of their epistemology, and that Condillac considered the availability of language as a sine qua non for moving from “sensation” to the higher level of cognitive ability he calls “analysis”, without which humans would not be able to generate distinguishable and recurrently usable “ideas”. Another fundamental influence of the linguistic environment on cognition derives from the fact that language is a rule-based system. The power of the notion of “rule” is apparent in the attraction it exerts on a child’s mind, as soon as the child gives up its “egotistic” privilege of creating its own communicative symbols and submits to the socially imposed linguistic rules. Not only does the child attempt to impose absolute exception-free regularity on the linguistic rules themselves through the well-attested phenomenon of “over-generalization” (e.g., by “regularizing” irregular verbs: “eated” instead of “ate”, “shaked” instead of “shook”). It also projects this strict rule model onto other activities such as games where no violations of the rules are tolerated. The strong appeal of the “computation model of the mind”, as well as of its earlier counterpart, the mind-machine analogy, may ultimately derive from our familiarity with the machine-like rules of grammar.17 The sequential organization of speech — another structural characteristic of language — imposes upon oral communication a linear and unidirectional pattern. This pattern is imitated in cognitive processes, even when they are not communication-oriented. As a result, an ante-post, step by step ordering of thoughts acquires a privileged canonical status, where what comes “first” is assumed to be, in some sense, cognitively “prior” to what comes “after”. Such a priority may be interpreted as logical, epistemological, causal, psychological, or chronological, but the pattern is the same, and tends to be viewed as an indication that a cognitive process that follows it is “rational”. Obviously, this pattern does not fit all cognitive processes, some of which (e.g., associative thought) display rather a net-like structure.18 Speech permits deviations from linear and unidirectional thematic order (e.g., digressions, flashbacks), and writing and electronic text-processing provide further means for so doing (e.g., footnotes, hypertext). But the fact that such deviations are perceived as “exceptions” to the

Language as a cognitive technology

standard linear pattern implies that both linguistically and cognitively they must be sparingly used and their use needs to be especially justified. In this sense, the environmental influence of the linguistic sequential model obstructs, rather than helps, the performance of certain cognitive processes.19 The analytic-combinatorial, rule-based, and sequential models are not, however, the only ones the linguistic environment provides for cognitive imitation. Adam Smith observed that modern “analytic” languages stand to ancient “synthetic” languages, as far as their simplicity and systematicity is concerned, as early machines, which are “extremely complex in their principles”, stand to more advanced ones, which produce their effects “with fewer wheels and fewer principles of motion”. Similarly, “in language every case of every noun, and every tense of every verb, was originally expressed by a particular distinct word, which served for this purpose and for no other. But succeeding observations discovered, that one set of words was capable of supplying the place of all that infinite number, and that four or five prepositions, and half a dozen auxiliary verbs, were capable of answering the end of all the declensions, and of all the conjugations in the ancient languages” (Smith 1761, pp. 248–249). But he immediately made clear that the language-machine analogy breaks down as soon as one goes beyond grammar: “The simplification of machines renders them more and more perfect, but this simplification of the rudiments of languages renders them more and more imperfect, and less proper for many of the purposes of language” (ibid.). Smith had in mind the expressive needs that language must provide for, such as eloquence and beauty or, more generally, its ability to express not only the “thought but also the spirit and the mind of the author” (Smith 1983, p. 19). It is for such purposes that the simplified machinery of “analytic” languages is inadequate due to their inherent “prolixness, constraint, and monotony” (Smith 1761, p. 251). Since even a paradigmatic analytic language such as English obviously overcomes these inadequacies and provides for the expressive needs mentioned (didn’t Shakespeare write in English?), it must do so — if we follow Smith’s argument — by evolving some “non-mechanical” means that compensate for its “mechanical” limitations. Smith’s point can be generalized. First, the expressive needs for which more than the rules of grammar are needed comprise not only lofty literary-rhetorical ideals, but also down-to-earth everyday communicative needs. Second, there is no difference between analytic and synthetic languages in this respect; in fact, no known natural language can dispense with additional “wheels” and “principles of motion”, other than the syntactic and semantic ones, in order to fulfill its expressive and communicative duties. Such additions to the basic linguistic

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system range from syntactic rules that permit to “transform” or adjust the output of the basic syntactic rules without substantial meaning change to devices that allow one to say one thing and mean another. The former can be compared to the addition of epicycles to the Ptolemaic system in order to cope with the observed phenomena, without modifying its methodological assumption about the kinds of “wheels” and “principles” that are supposed to account for the machine’s “competence”.20 The latter, studied mainly by pragmatics and rhetoric, are generally believed to obey different kinds of “rules” — of an heuristic, rather than an algorithmic nature.21 A particularly significant feature of the pragma-rhetorical component of a linguistic system is that it sometimes achieves its aims by resorting to explicit violations of the system’s rules — be they the algorithmic ones (as in metaphor, puns, and nonsense poetry) or the heuristic ones (as in conversational “implicatures”). A rule-based system that employs different kinds of rules and does not rule out, but rather permits and even exploits the violation of its own rules, is extremely valuable from a cognitive point of view. For it provides a living and effective model for many important cognitive processes that are open-ended, flexible, creative, and yet not aleatory. It also shows that there is an alternative to treating — as virtually all LN interfaces and applications to date do — rule violations as mistakes to be corrected (sometimes automatically, thereby irritating users). Apart from its generic influence, the linguistic environment can have quite specific effects upon cognition, which should not be overlooked. An interesting case is the presumed role of language in causing deviations from logically valid forms of reasoning. For example, there is evidence that the evaluation of invalid syllogisms as valid has to do with an “atmosphere” effect, produced by the particular linguistic formulation of the premises. Thus, syllogisms whose premises were both affirmative and universal tended to be viewed as having also an affirmative and universal conclusion, irrespective of whether the disposition of the subject and predicate terms in the premises logically warranted such a conclusion (Woodworth and Sells, 1935; Evans, 1982, pp. 89–90). Similarly, a robust finding in studies of conditional reasoning using Wason’s well-known “selection task” is the linguistically-driven “matching bias”. The subjects in this task are given a conditional sentence referring to a set of four cards laid down on a table. Each card has a letter on one side and a number on the other. The subjects are asked to determine which cards they would have to turn in order to tell whether the sentence is true or false. The matching bias consists in the fact that subjects tend to pick up those cards that match those named in the

Language as a cognitive technology

sentence, regardless of whether they verify or falsify it.22 Further, allegedly pernicious, specific examples of linguistic influence on cognition will be mentioned in the next section. 4.2 Language as resource Under this rubric I include those aspects of language that are regularly and, for the most part, consciously put to use for cognitive purposes, with minimal elaboration. They deserve to be considered “technologies” in-so-far as the choice of a particular linguistic feature stands in a means-end relationship with the cognitive purpose in view. An example of a linguistic resource widely employed for an extremely important cognitive purpose is the use of words for gathering, organizing, storing, and retrieving information. This has been done for so long that it is taken for granted and we are unaware of its linguistic-cognitive underpinnings as well as of the fact that in its current uses — be it in printed indices or in computerized search engines — its potential is far from being fully exploited. For, whereas the value of words for tracing relevant information lies in their semantic content, most applications make use only of their graphic form in order to locate matching graphic strings that are assumed to lead to semantically relevant material. The cognitive burden to sort out truly relevant information is for the most part left to the user. Few systems make use of the truly relevant linguistic resource for information storage and retrieval, the resource humans naturally and effortlessly use, namely the rich semantic structure of natural languages.23 The semantic network of language is based on a set of semantic relations that connect expressions in a variety of ways — as synonyms, near-synonyms, paraphrases, analytic, super-ordinate, subordinate, belonging or not to a semantic field, antonyms, contrary, contradictory, etc. By structuring the “mental lexicon”, such a network is an inescapable resource the mind constantly resorts to in most of its cognitive operations, which rely upon conceptual similarities and differences. The semantic network also comprises information — such as connotations, prototypes, and factual information — that belongs rather to the “mental encyclopedia” but, being standard, widely known and normally activated in the understanding of linguistic expressions, counts as “semantically relevant”.24 This extension makes the network an even more useful and constantly used cognitive resource, only minimally exploited to date by technologies that make use of thesauri.

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The possibility of precision afforded by natural languages should not make us overlook the wide variety of syntactic, semantic and pragmatic means they have for expressing indeterminacy — an umbrella term here used to refer to phenomena such as indefiniteness, ambiguity, polysemy, unspecificity, imprecision and vagueness. Although considered a hindrance from the point of view of certain cognitive needs, such linguistic means are, from the point of view of other cognitive needs, an asset. For instance, they are an invaluable — perhaps indispensable — resource for the cognitive processes that begin with a foggy initial intuition which they undertake to clarify in a stepwise way, or vice-versa, for those processes that seek to sum up the gist of a theory, an argument, or a story. They are also essential for conceptualizing those situations in which the mind hesitates between alternatives, none of which seem to fall clearly into welldefined categories. While we often wish everything could be clearly classified as either black or white, good or bad, true or false, we often stumble at borderline cases, which force our mind to abandon dichotomous thought and rather think in terms of gradual, continuous, and vague concepts (Gullvåg and Naess, 1995). Language also provides its users with a repertoire of ready-at-hand, more or less conventionalized patterns that can be put to use not only communicatively but also cognitively. This repertoire ranges from phrases and sentences to fullfledged discursive structures. It includes, among other things, formulaic expressions, conventional metaphors, proverbs, topoi,25 argumentative formulae, dialogue patterns, and literary forms.26 These resources are ready-at-hand for organizing thought. The existence of argumentative canonical formulae structured by prepositions and adverbs such as if … then, but, either…or, therefore provides “directives” to the reasoner, which allow her to complete what is missing, to determine if something in her reasoning is irrelevant or redundant, etc. So too the current canonical form of the “scientific article”, say, in psychology, provides guidelines not only for the presentation of the author’s results, but also for the way in which his mental and practical steps leading to such results should be executed.27 Before concluding this sample of language-based cognitive resources, I want to mention a number of related linguistic devices that, I believe, are extremely important for cognition. Consider the parenthetical ‘I believe’ I have employed in the preceding sentence. Its position could have been occupied, instead, by ‘I know’, ‘I am sure’, ‘I have no doubt’, ‘I hypothesize’, ‘I submit’, ‘I argue’, ‘I contend’ or, with slight syntactical modifications, by ‘I wonder’, ‘I doubt’, ‘allegedly’, etc. Some of these expressions express propositional attitudes; others, the illocutionary force of speech acts. Both act as operators on propositional

Language as a cognitive technology

contents, which reflect the variety of different degrees of commitment, epistemic status, intentions, etc. with which the mind may relate to such contents. They thus belong to a family of expressions which perform a distinction between two layers of “content” — the one referring to or modulating the other. The most familiar linguistic devices of this kind are metalinguistic operators such as quotation, thanks to which natural languages can act as their own metalanguage. As a whole, these linguistic resources correspond to and reveal the inherent reflexivity of human mental processes, i.e., the fact that cognition is conscious of itself, and therefore involves “metacognition”.28 It seems to me that this is not a one-way road, leading from metacognition to its linguistic expression, but at least a two-way road, in which the existence of metalinguistic resources should also be credited with the enhancement of metacognitive awareness and its development. The mechanism of joint attention (towards perceptual objects, towards each other in an interaction), for example, which is a necessary ingredient of intentional communication (Brinck, 2001), involves the recognition of the other’s attentional state, as well as awareness of one’s own. Similarly, the mother’s attribution of intentions to the infant has been suggested to play a decisive role in the infant’s development of her self-perception as an intentional agent (De Gelder, 1981; discussed in Dascal, 1983, pp. 99ff.). Let us now turn to the alleged negative effects of language as a resource. The careless cognitive use of linguistic resources has been blamed, throughout the centuries, for inducing all sorts of cognitive mistakes. The indiscriminate use of linguistically productive (and legitimate) patterns of word generation (e.g., white Æ whiteness) has been held responsible for yielding in fact vacuous terms (e.g., nothingness) which are the source of conceptual confusion and pointless dispute (Locke). The existence in language of general terms was blamed for inducing the false belief that there are general ideas and general objects (Berkeley). Natural language categorization, based on “vulgar” knowledge, was considered to be the most dangerous of the “idols” that threaten scientific thinking (Bacon). Vagueness was considered incompatible with logic and therefore utterly unreliable (Russell). Reliance on grammatical analogy was blamed for causing “category mistakes” and “systematically misleading” the understanding (Ryle). A list of “pseudo-problems” in which generations of metaphysicians were entangled was added to language’s long list of cognitive deficits (Carnap). Uncritical linguistic practice was singled out as the most dangerous cause — whether deliberate or not — of cognitive distortion, manipulation, and ultimately “un-sanity” (Count Korzybski and the General

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Semantics movement).29 And so on. This small sample of criticism certainly shows that language’s influence on cognition can be indeed pernicious. But it also highlights the extent and variety of this influence. The lesson to be drawn, as in many other cases, is simply that we must be aware of this variety, extent, and sometimes insidious nature, so as to be able to rely upon the linguistic environment of thought only judiciously. 4.3 Language as tool A language-based cognitive technology can be viewed as a tool when it is the result of the engineering of linguistic resources for a specific cognitive task. Let us consider some examples. The linguistic resource of explaining the meaning of one term by correlating it with a string of other terms that “define” the former has been sharpened into the powerful cognitive tool of formal definition. This tool permits the creation of special terminology (new terms for new concepts, or redefinition of existing terms) or of new notational systems.30 Usually the model of definition adopted in these cases is the “classical” one, i.e., the specification of necessary and sufficient conditions. But natural language semantics also provides other models of capturing concepts, e.g., in terms of similarity to a prototypical member of the denoted class or in terms of clusters of properties which are hierarchically organized in terms of their centrality or weight, although none of them is per se necessary or sufficient. Such “non-classical” models are characteristic of so-called “natural kind” terms (Achinstein, 1968). The elaboration of each of these kinds of “definition” yields different types of linguistic tools or technologies that are fit for different cognitive purposes. The various forms of indeterminacy available in natural languages can be shaped into cognitive tools. For example, the linguistic possibility of generating scales of quantifiers, making them as subtle as desired (e.g., everyone, virtually everyone, almost everyone, most of the people, the majority of the people, some people, nearly nobody, virtually nobody, nobody, etc.) can give rise to rigorous systems of quantification other than the standard one. The same is true of linguistic tense systems that can be elaborated into a variety of temporal logics. And vagueness has been elaborated semantically into “fuzzy logic”, that permits to reason rigorously with vague concepts (Zadeh, 1975; see also Black, 1963), as well as pragmatically, into a dynamic interpretive tool for gradually increasing precision until what appears to be an agreement or disagreement is shown to be in fact a pseudo-agreement or a pseudo-disagreement (Naess, 1966).

Language as a cognitive technology

Formulaic expressions can become powerful cognitive tools. A remarkable example, analyzed by Reviel Netz (1999), is the role of linguistic formulae in ancient Greek mathematics. Netz shows that, in contrast to deduction in modern mathematics, where one resorts to typographic symbols, thus opting for exploiting a visual resource, Greek mathematics made use of formulaic expressions of a linguistic resource presumably of oral origin. He analyzes in detail Book II of Euclid’s Elements, identifying and sorting out the 71 such formulaic expressions, i.e., highly repetitive and standardized phrases and sentences, which make up for most of the text. He argues that deduction as a cognitive tool may have been made possible due to the systematic use of such formulae: “The constant re-shuffling of objects in substitutions may be securely followed, since it is no more than the re-fitting of well-known verbal elements into well-known verbal structures. It is a game of decomposition and recomposition of phrases, indeed similar to the jigsaw puzzle of putting one heroic epithet after another, which is to a certain extent what epic [Homeric] singers did” (Netz 1999, p. 161). If we jump from mathematics to religion, we may find in the Hindu mantra a similar phenomenon. The nature and role of mantras is quite controversial, as is patent from the papers in Alper’s (Ed., 1989) collection. Some scholars even doubt their linguistic nature, and most view them as belonging to the religious ritual, where — according to some — they are akin to prayer. Nevertheless, there is no doubt that, at least in some of their variants, they are self-addressed linguistic or quasi-linguistic tools whose main purpose is to play a definite role in their user’s mental life. This is the case, for instance, in Yogi meditation; and a classical text presumably of the 3rd or 4th century, the Arthas´a¯ stra, goes as far as attributing to it impressive intellectual effects: “a mantra accomplishes the apprehension of what is not or cannot be seen, imparts the strength of a definite conclusion to what is apprehended, removes doubt when two courses are possible [and] leads to inference of an entire matter when only a part is seen” (quoted by Alper 1989, p. 2). Literary resources can also develop into cognitive tools par excellence. Tsur and Benari (2001) have shown how a specific poetic device — ‘composition of place’ — employed in meditative poetry, is designed to overcome the linear and conceptual character of language so as to convey “such non-conceptual experiences as meditation, ecstasy or mystic insights” and thus to “express the ‘ineffable” (Tsur and Benari, 2001, p. 231). In the same vein, Zamir (2002b) shows, through a close reading of Hamlet, how literature is able to create awareness of “ineffable” content.31 In both cases, I would suggest, literary tools not only express or induce certain mental states, but also in a sense create the

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conditions for the very existence of these states in the first place. As a last example of a linguistic resource that gives rise to a cognitive tool, I would like to mention the dialectical use of dialogue structures. Ever since Plato, philosophers developed what can be seen as a genre — the “philosophical dialogue” — in order to expound their ideas. Its formal structure, however, carries with it cognitive requirements quite different from other forms of exposition. To expound one’s ideas for a specific interlocutor and to defend them against her specific objections — even if both the character and objections are fictional creations of the writer — requires techniques of persuasion, argumentation and justification other than those used in a linear text that addresses a generalized, non-present, and unknown reader. Other dialectical techniques developed independently, in oral rather than in written form. In the Middle Ages, codified forms of debate such as the disputatio and the obligatio evolved and success in them became part of the academic requirements to obtain a university degree. But the cognitive implications of these practices transcended both pedagogical needs and the Middle Ages. For the basic idea that a rational debate should obey a set of principles that define the duties of the “defendant” and the “opponent”, the types of moves they are allowed to perform, and what will count as a “victory”, remains in force in fact up to this day — even though the contents of such principles have changed. There is no space here to trace the development of dialectical techniques, which involves an interesting interplay between logic and rhetoric, culminating with “dialogical logic” on the one hand and “the new rhetoric” on the other. What is important to realize, for the purposes of this paper, is that the ensemble of techniques thus developed transcends pedagogical, expository, or communicative ends, for it becomes a powerful tool for actually implementing the idea that at the core of rationality lies the practice of critical thought.32 In this sense, a system of “electronic argumentation” should be designed not only to improve one’s ability to express oneself (Sillince, 1996), but also as a tool to improve one’s ability to think rationally.

5. Concluding remarks In this chapter I have proposed to look at language not only as a communicative interface between cognitive agents, but as a technology involved in cognition itself. I surveyed instances of how language functions as an environment, a resource, and a tool of cognition. Some of these examples are more easily

Language as a cognitive technology

acknowledged as “cognitive technologies” than others, but all of them share the main characteristics I have attributed to this notion. They contribute systematically and directly to cognitive processes and to the achievement of cognitive aims. And all of them are clearly language-based. In terms of the parameters presented in Section 2, most of the examples of language-based cognitive technologies discussed are “internal”, and await the eventual development of “external” counterparts; in spite of optimistically exaggerated claims of some designers, virtually all of the extant such developments are “partial” rather than “integral”; some of the language-based cognitive technologies are useful for “strong” cognition, others for “weak” cognition, and still others for both; very few purport to be “complete”; and only a few of them have been suggested to be “constitutive”. By emphasizing the direct contribution of language-based technologies to cognition, I want to stress that they are not mediated by the communicative use of language — the kind of use that monopolizes the attention of designers of human-computer interfaces. I obviously do not deny the importance of the latter, but I think the justified desire to develop humane interfaces and, in general, humane technologies, requires a better understanding of how the human mind makes use of and is affected by naturally evolved or designed technologies. In this respect, this paper should be seen as a contribution to the incipient field of an “epistemology of cognitive technology” (Gorayska and Marsh, 1996). By focusing on language, it connects this field with one of the main philosophical achievements of 20th century thought, the “linguistic turn”, which transformed language into the fulcrum of research in philosophy, psychology, the social sciences, and the cognitive sciences. In his intriguing book Meaning in Technology, Arnold Pacey defends a worldview “in which human relationships and human purposes may have a closer connection with technological progress than sometimes seems possible” (Pacey, 2001, p. 38). He distinguishes between the prevalent detached approach to science and technology and a participatory approach, in which we “feel ourselves to be involved in the system on which we are working” (p. 12). According to him, it is the latter that endows technology with meaning. Pacey might have found support for his insights in the present paper. Not only because we have an intimate participatory relationship with language in general and language-based cognitive technologies in particular, but also because such technologies are, ultimately, the technologies of meaning par excellence.

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Notes * I have presented some of the ideas put together in this paper, in one way or another, in the following forums: “Dialogue Analysis 2000” (International Association for Dialogue Analysis, Bologna, June 2000); “Limited Cognitive Resources: Inference and Reference” (Center on Resource Adaptive Cognitive Processes, Saarbrücken, October 2000); “IV Encontro Brasileiro Internacional de Ciência Cognitiva”; Marília, Brazil, December 2000); and “Ciencia, Tecnología y Bien Común: La Actualidad de Leibniz” (Universidad Politécnica de Valencia, Spain, March 2001). I thank the organizers as well as the participants who enlightened me with their comments and criticism. 1. Notice that my definition is substantially narrower than those attributed to this term by other researchers (e.g., Dautenhahn 2000). 2. It should be noticed that some of the expressions in these two lists of illustrations — e.g., ‘demonstration’, ‘persuasion’, ‘decision’, etc. — display the well-known process/product ambiguity. This is why they can belong both to the list of states and to that of processes. 3. See Zue (1999). 4. On the three last items, see for example the papers presented in Proceedings (2000), as well as those collected in Cassell et al. (Eds., 2000) and Dautenhahn (Ed., 2000). 5. I have proposed a distinction between ‘demonstration’ and ‘argumentation’ as preferred moves in different types of polemics in Dascal (1998a). 6. For a critique of the initial projects of mechanical translation, which pointed out the insufficiency of linguistic theory to support them, see Bar-Hillel (1964, Chapters 10–14). 7. Usually, in the first case it is said that it is syntactically incomplete, while in the second it is said to be semantically incomplete. In both cases, however, semantics — in the broad sense of correspondence between a symbolic system and the properties it purports to represent — is involved. The formation rules in fact select a set of well-formed formulae or combinations of symbols according to some criterion of well-formedness that is supposed to correspond to some property (e.g., ‘grammaticality’ in a linguistic system or ‘propositionality’ in the propositional calculus), whereas the transformation rules select a set of derivation relations between formulae that is supposed to correspond to another property (e.g., ‘meaning invariance’ in the standard model of generative grammar or ‘validity’ in the propositional calculus). 8. See Anderson & Belnap (1975, pp. 403ff.). 9. Some passages in Turing’s paper may suggest that he took success in playing the “imitation game” (i.e., what I called the “test”) as an operational definition of intelligence, and thus — from the point of view of behaviorism — as equivalent to it, rather than a sign of it. See, for example, Block (1981) and Richardson (1982). Eli Dresner tried to persuade me that this is the case, but he concedes that “Turing definitely does not describe himself as a behaviorist” (personal communication). 10. Among them Rousseau and Adam Smith (cf. Dascal 1978 and Forthcoming). 11. For an analysis of this debate, see Dascal (1995), where several of the authors mentioned in this and the preceding paragraphs are discussed. Those interested particularly in Leibniz

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and Hobbes should consult Dascal (1998b and 1998c, respectively). On the implications of this debate for AI and current work in the philosophy of mind and of language, see Dascal (1992b, 1997a) and Dresner & Dascal (2001). 12. I coined the term ‘psychopragmatics’ for the branch of pragmatics that deals not with the social uses of language such as communication (a task reserved for ‘sociopragmatics’) but with the mental uses of language. See Dascal (1983) and references therein. 13. For example: “We thought a day and night of steady rain / was plenty, but it’s falling again, downright tireless … / …Much like words / But words don’t fall exactly; they hang in there / In the heaven of language, immune to gravity / If not to time, entering your mind / From no direction, travelling no distance at all, / And with rainy persistence tease from the spread earth / So many wonderful scents … (Robert Mezey, “Words”; quoted in Aitchison, 1994, p. v). The images employed in this poem capture several of the “environmental” properties of language described in Section 4.1. 14. In this respect, I am much more moderate than Winograd & Flores, who interpret Heidegger’s dictum as claiming that “nothing exists except through language” (Winograd and Flores, 1986, p. 68). 15. Watson later rejected this reductionist claim (see Watson & McDougall, 1928). 16. In fact, linguistic articulation goes well beyond this, since one can identify sub-phonemic features out of which phonemes are formed, as well as supra-lexical meaningful compounds such as idioms, whose meaning cannot be accounted for in terms of lexical-syntactic composition. 17. “Grammar itself is a machine / Which, from innumerable sequences / selects the strings of words for intercourse …/ When the words have vanished, grammar’s left, / And it’s a machine / Meaning what? / A totally foreign language” (Lars Gustafsson, “The machines”, quoted by Haberland, 1996, p. 92). 18. The philosopher Gilles Deleuze, who describes this kind of structure using the botanical model of the rhizome, rather than the now popular neural net model, has highlighted its centrality for understanding the multi-layered complexity of human thought and its expression. See Deleuze & Guattari (1976, 1980). 19. A striking example of the sheer linguistic difficulty in overcoming this obstacle is exemplified by Alejo Carpentier’s story “Viaje a la semilla” (in Carpentier, 1979, pp. 63–94). The story moves backwards from a current event to the “seed” whence it derives. In spite of the author’s ingenious efforts, however, it becomes apparent that it is virtually impossible to neutralize the temporal order embedded in various levels of linguistic structure. 20. For example, Smith pointed out one of these expressive devices used to circumvent the basic syntactic order in English (subject-verb-object), namely the anteposition of “whatever is most interesting in the sentence” (Smith 1983, p. 18), which is accounted for in modern syntactic theory in terms of an “epicyclic” rule. 21. For example, the “maxims” that govern conversation according to Grice. For further exploration, application, and theoretical grounding of these and other pragmatic rules and principles, see Dascal (2003). For a critique of the view that, since conversation is not ruled

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by constitutive rules of a grammatical kind, it is not, properly speaking, a rule-governed phenomenon, see Dascal (1992a). 22. For discussion and interpretation of the “matching bias” phenomenon, see Evans (1982, pp. 140–144) and Dascal (1987). 23. Leibniz devoted much thought, in his projects for an encyclopedia and its role in the “art of discovery”, to the cognitive role of a variety of types of indexing. See Dascal (2002). 24. On the notion of “semantic relevance”, see Achinstein (1968). On the difficulty of establishing a clear distinction between “dictionary” and “encyclopedia”, see Peeters (2000) and Cabrera (2001). 25. Topoi, loci communes, or “commonplaces” occupied a central place in humanist education in the renaissance and the early modern period. Dozens of “CommonplaceBooks” were printed at the time, and students were required to write and use their own commonplace lists. Such a practice not only established shared forms of expression, but also shared conceptual tools, which thus constituted a background of “mental structures” guiding the thought and understanding of educated persons throughout Europe for at least two centuries. For a study of this linguistic-based cognitive resource, see Moss (1996). 26. Some of these resources have been put to use in computer applications. Chinese wordprocessors, taking advantage of the Chinese habit of systematically using proverbs (mainly four-character ones), “propose” to the writer possible proverbial continuations once the first two characters of the proverb are typed. Attempts to simulate and exploit the dialogical resources of natural language for human-computer interfaces are now proliferating. The pioneer classic ELIZA employed a number of phrasal structures routinely occurring in nondirective psychotherapy in order to create the impression of a real dialogue between therapist and patient (Weizenbaum, 1966). The MUD robot-agent JULIA, like ELIZA, employs lists of common queries and a matching procedure in order to generate naturallooking “conversation” with users (cf. Foner, 2000). More recent rule-based systems of dialogue and conversation (e.g., Kreutel and Matheson, 2000; Webb, 2000) are no doubt much more sophisticated and useful tools than ELIZA, but they still remain excessively subordinated, in my opinion, to the rule-following model. 27. For a rhetorical analysis of the scientific paper and its evolution from the 17th century onwards, see Gross (1990) and Gross et al. (2002). 28. For a sample of research on metacognition, see Metcalfe & Shimamura (1994). For the relationship between metacognition and consciousness, see Newton (1995), and for its relationship with conversation, see Hayashi (1999). For a critique of the exaggerated emphasis on metacognitive abilities in education, see Roth (2004) 29. A striking example of the use of language for alleged “scientific” purposes is Scientology. This religious movement, based on the “science” of “Dianetics”, claims to provide its followers with a “cognitive technology” that allows them to achieve the status of “Clears”, essentially through linguistic manipulation. For an analysis of this phenomenon, see Mishori & Dascal (2000).

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30. Lavoisier, who was in this respect a follower of Condillac, viewed his new chemical nomenclature as having cognitive implications far beyond those of a mere terminological reform (cf. Bensaude-Vincent, 1993). 31. Zamir (2002) also proposes an epistemological account of how literature can express and eventually generate cognitive content that the resources of philosophical discourse are unable to capture. 32. See Astroh (1995), Barth (1992), Dascal (1997b, 1998a, 2000) and references therein.

References Achinstein, P. (1968). Concepts of science: A philosophical analysis. Baltimore: The Johns Hopkins Press. Aitchison, J. (1994). Words in the mind (2nd ed.). Oxford: Blackwell. Alper, H. P. (1989). Introduction. In H. P. Alper (Ed.), pp. 1–14. Alper, H. P. (Ed.) (1989). Mantra. Albany: State University of New York Press. Anderson, A. R. & N. D. Belnap, Jr. (1975). Entailment: The logic of relevance and necessity, vol. 1. Princeton: Princeton University Press. Astroh, M. (1995). Sprachphilosophie und Rhetorik. In Dascal et al. (Eds.) (1992–5), pp. 1622–1643. Bar-Hillel, Y. (1964). Language and information. Reading, MA & Jerusalem: Addison-Wesley & Magnes Press. Barth, E. M. (1992). Dialogical approaches. In Dascal et al. (Eds.), pp. 663–676. Bensaude-Vincent, B. (1993). Lavoisier: Mémoires d’une révolution. Paris: Flammarion. Black, M. (1963). Reasoning with loose concepts. Dialogue, 2, 1–12. Block, N. (1981). Psychologism and behaviorism. The Philosophical Review, 80, 5–43. Brinck, I. (2001). Attention and evolution of intentional communication. Pragmatics & Cognition 9(2), 255–272. Cabrera, J. (2001). Words, worlds, words. Pragmatics & Cognition 9(2), 313–327. Carpentier, A. (1979). Cuentos completos. Barcelona: Brughera. Cassell, J., J. Sullivan, S. Prevost & E. Churchill (Eds.) (2000). Embodied conversational agents. Cambridge, MA: The MIT Press. Dascal, M. (1978). Aporia and theoria: Rousseau on language and thought. Revue Internationale de Philosophie 124/125, 214–237. Dascal, M. (1983). Pragmatics and the philosophy of mind, vol. 1: Thought in Language. Amsterdam: Benjamins. Dascal, M. (1987). Language and reasoning: Sorting out sociopragmatic and psychopragmatic factors. In B. W. Hamill, R. C. Jernigan & J. C. Bourdreaux (Eds.), The role of language in problem solving II, pp. 183–197. Amsterdam: North Holland. Dascal, M. (1992a). On the pragmatic structure of conversation. In H. Parret and J. Verschueren (Eds.), (On) Searle on Conversation, pp. 35–56. Amsterdam: Benjamins. Dascal, M. (1992b). Why does language matter to artificial intelligence?. Minds and Machines 2, 145–174.

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Dascal, M. (1995). The dispute on the primacy of thinking or speaking. In Dascal et al. (Eds.), pp. 1024–1041. Dascal, M. (1997a). The language of thought and the games of language. In M. Astroh, D. Gerhardus, and G. Heinzman (Eds.), Dialogisches Handeln: Ein Festschrift für Kuno Lorenz, pp. 183–191. Heidelberg: Spektrum Akademischer Verlag. Dascal, M. (1997b). Critique without critics? Science in Context 10(1), 39–62. Dascal, M. (1998a). Types of polemics and types of polemical moves. In S. Cˇmejrková, J. Hoffmannová, O. Müllerová & J. Sveˇtlá, Dialogue analysis VI, vol. 1, pp. 15–33. Tübingen: Max Niemeyer. Dascal, M. (1998b). Language in the mind’s house. Leibniz Society Review 8, 1–24. Dascal, M. (1998c). O Desafio de Hobbes. In L. Ribeiro dos Santos, P. M. S. Alves & A. Cardoso (Eds.), Descartes, Leibniz e a Modernidade, pp. 369–398. Lisboa: Colibri. Dascal, M. (2000). Controversies and epistemology. In Tian Yu Cao (Ed.), Philosophy of science (= Vol. 10 of Proceedings of the Twentieth World Congress of Philosophy), pp. 159–192. Philadelphia: Philosophers Index Inc. Dascal, M. (2002). Leibniz y las tecnologías cognitivas. In A. Andreu, J. Echeverría & C. Roldán (Eds.), Ciencia, tecnología y el bien común: La actualidad de Leibniz, pp. 159–188. Valencia: Universidad Politécnica de Valencia. Dascal, M. (2003). Interpretation and Understanding. Amsterdam: Benjamins. Dascal, M. (Forthcoming). Adams Smith’s theory of language. In K. Haakonssen (Ed.), The Cambridge Companion to Adam Smith. Cambridge: Cambridge University Press. Dascal, M., D. Gerhardus, K. Lorenz & G. Meggle (Eds.) (1992–5). Philosophy of Language — A handbook of contemporary research, vols. 1 & 2. Berlin & New York: Walter de Gruyter. Dautenhahn, K. (2000). Living with intelligent agents: A cognitive technology view. In K. Dautenhahn (Ed.), Human cognition and social agent technology, pp. 415–426. Amsterdam: Benjamins. De Gelder, B. (1981). Attributing mental states: A second look at mother-child interaction”. In H. Parret, M. Sbisà & J. Verschueren (Eds.), Possibilities and limitations of pragmatics, pp. 237–250. Amsterdam: Benjamins. Deleuze, G. & F. Guattari (1976). Rhizome. Paris: Minuit. Deleuze, G. & F. Guattari (1980). Mille plateaux. Paris: Minuit. Dertouzos, M. L. (1999). The future of computing. Scientific American 281(2), 36–39. Dresner, E. & M. Dascal (2001). Semantics, pragmatics, and the digital information age. Studies in Communication Sciences 1(2), 1–22. Dreyfus, H. (1971). What computers can’t do. New York: Harper & Row. Dreyfus, H. (1992). What computers still can’t do. Cambridge, MA: The MIT Press. Dror, I. E. & M. Dascal (1997). Can Wittgenstein help free the mind from rules? The philosophical foundations of connectionism. In D. M. Johnson & C. E. Erneling (Eds.), The future of the cognitive revolution, pp. 217–226. New York: Oxford University Press. Evans, J. T. St. B.. (1982). The psychology of deductive reasoning. London: Routledge & Kegan Paul. Foner, L. (2000). Are we having fun yet? Using social agents in social domains. In K. Dautenhahn (Ed.), Human cognition and social agent technology, pp. 323–348. Amsterdam: Benjamins. Geertz, C. (1973). The interpretation of cultures. New York: Basic Books.

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Gorayska, B. & J. Mey (Eds.) (1996). Cognitive technology: In search for a humane interface. Amsterdam: Elsevier. Gorayska, B. & J. Marsh (1996). Epistemic technology and relevance analysis: Rethinking cognitive technology. In Gorayska & Mey (Eds.), pp. 27–39. Gross, A. G. (1990). The rhetoric of science. Cambridge, MA: Harvard University Press. Gross, A. G., J. E. Harmon & M. Reidy (2002). Communicating science: The scientific article from the 17th century to the present. Oxford: Oxford University Press. Gullvåg, I. & A. Naess (1995). Vagueness and ambiguity. In Dascal et al. (Eds.), pp. 1407–1417. Haberland, H. (1996). “And we shall be as machines” — or should machines be as us? On the modeling of matter and mind. In Gorayska & Mey (Eds.), pp. 89–98. Hayashi, T. (1999). A metacognitive model of conversational planning. Pragmatics & Cognition 7(1), 93–145. Hutchins, E. (1999). Cognitive artifacts. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences, pp. 126–128. Cambridge, MA: The MIT Press. Kreutel, J. & C. Matheson (2000). Information states, obligations and intentional structure in dialogue modelling. In Proceedings, pp. 80–86. Metcalfe, J. & A. P. Shimamura (Eds.) (1994). Metacognition: Knowing about knowing. Cambridge, MA: The MIT Press. Mishori, D. & M. Dascal (2000). Language change as a rhetorical strategy. In Harish Narang (Ed.), Semiotics of language, literature and cinema, pp. 51–67. New Delhi: Books Plus. Moss, A. (1996). Printed commonplace-books and the structuring of renaissance thought. Oxford: Oxford University Press. Naes, A. (1966). Communication and argument: Elements of applied semantics. Oslo: Universitetsforlaget. Netz, R. (1999). Linguistic formulae as cognitive tools. Pragmatics & Cognition 7, 147–176. Newton, N. (1995). Metacognition and consciousness. Pragmatics & Cognition 3(2), 285–297. Peeters, B. (Ed.) (2000). The lexicon-encyclopedia interface. Amsterdam: Elsevier. Pacey, A. (2001). Meaning in technology. Cambridge, MA: The MIT Press. Proceedings (2000). Proceedings of the 3rd International Workshop on Human-Computer Conversation (Bellagio, July 2000). Richardson, R. (1982). Turing tests for intelligence: Ned Block’s defense of psychologism. Philosophical Studies 41, 421–426. Roth, M. (2004). Theory and praxis of metacognition. Pragmatics and Cognition 12(1), 153–168. Sillince, J. A. A. (1996). Would electronic argumentation improve your ability to express yourself?. In B. Gorayska & J. L. Mey (Eds.), pp. 375–387. Smith, A. (1761). Considerations concerning the first formation of languages and the different genius of original and compounded languages. In J. R. Lindgren (Ed.), The Early Works of Adam Smith, pp. 225–251. New York: Augustust M. Kelley Publisher, 1967. Smith, A. (1983). Lectures on rhetoric and belles lettres, ed. J. C. Bryce & A. S. Skinner. Oxford: Clarendon Press. Tsur, R. & M. Benari (2001). ‘Composition of place’, experiential set, and the meditative poem. Pragmatics & Cognition 9(2), 201–234.

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Turing, A. M. (1950). Computing machinery and intelligence. Mind 59, 433–460. Watson, J. B. & W. McDougall (1928). Battle of behaviorism: An exposition and an exposure. London: K. Paul, Trench, Trubner & Co. Webb, N. (2000). Rule-based dialogue management systems. In Proceedings, pp. 164–169. Weizenbaum, J. (1966). ELIZA — A computer program for the study of natural language communication between man and machine. CACM 9, 36–45. Winograd, T. & F. Flores (1986). Understanding computers and cognition: A new foundation for design. Reading, MA: Addison-Wesley. Woodworth, R. S. & S. B. Sells (1935). An atmosphere effect in syllogistic reasoning. Journal of Experimental Psychology 18, 451–460. Zadeh, L. A. (1975). Fuzzy logic and approximate reasoning. Synthese 30, 407–428. Zamir, T. (2002a), An epistemological basis for linking philosophy and literature. Metaphilosophy, 33(3), 321–336. Zamir, T. (2002b). Doing nothing. Mosaic 35(3), 167–182. Zue, V. (1999). Talking to your computer. Scientific American 281(2), 40–41.

Relevance, goal management and cognitive technology* Roger Lindsay and Barbara Gorayska Psychology Department, Oxford Brookes University / SPS, University of Cambridge

1.

Introduction

Understanding what is relevant is absolutely fundamental to every cognitive operation carried out by humans from low-level feature recognition, to highlevel problem solving. In Artificial Intelligence (AI) work the importance of relevance is easily passed over. AI programs are written by human programmers in such a way as to ensure that all the cognitive resources needed by the program are available at exactly the time the program needs them. Indeed, most of the challenge involved in programming computers comes from having to anticipate and make available what is relevant at different stages of a processing cycle, and having to exclude information and operations that are irrelevant. The central contention underlying this chapter can be expressed as a positive and a negative thesis. The negative thesis is that the central role of relevance in cognition passes largely unacknowledged in cognitive neuroscience, despite the fact that neuroscientists are forced to employ or grapple with the concept at every turn. The positive thesis is that by according to relevance the central role that it should properly have in explaining cognition, it is possible to clear up a considerable number of issues and problems that presently seem mysterious in connection with problem-solving, ethics, symbol-connection hybridism and the motivation-action nexus. The first researchers with an interest in cognitive science to realise that relevance is important were Sperber and Wilson (1986/1995). Their updated Relevance Theory (Sperber and Wilson, 2004) is briefly summarized below (preserving, in as much as possible, the authors’ own terminology and style of expression).

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1.1 Sperber and Wilson’s Theory of Relevance According to Sperber and Wilson (1986/1995) their Theory of Relevance (RT) is a cognitive psychological theory which aims to explain, in cognitively realistic terms, the mental processes people employ when they overtly communicate. It is set within, and further elaborates on, the Gricean framework of Inferential Pragmatics where intended communicative acts, which include, but are not limited to, natural language utterances, are comprehended in situational contexts rather than merely decoded from, or coded in, what is strictly being said or ostensively done. It attempts to provide an empirically plausible account of human cognition at the interface between intention, action, and the language-mediated, perceivable world. Any cognitive psychological theory relies on how the mind is understood at the time of its formulation. RT is no exception. When it was first developed (Sperber and Wilson, 1986), it adopted the mental architecture proposed by Fodor (1983) which postulated that the mind comprised the largely undifferentiated, central thought processor (reflective reasoning mechanism) and a set of peripheral input-modules, or faculties, of which language module was one. Comprehending verbal and non-verbal behaviour intended to communicate was thus inferential (employing intuitive and spontaneous inference) but dissociated from the cognitive processes that related mental states to other forms of action. This dissociation, albeit weakened, is still in place in the current version of RT updated (Sperber and Wilson, 2004, summarized in this section) to better fit the modern, highly modular view of the mind in the cognitive sciences. What is now proposed is a dedicated inferential comprehension module which, according to the authors, is comparable to an Intention Detector, or an Eye Direction Detector (Leslie 1991; Premack & Premack, 1995; Baron-Cohen, 1995). It has its own proprietary concepts and mechanisms, which do not have to be learnt but come as a substantial innate endowment. This module, they say, is a part of a more general module for processing motivated action, but comprises special-purpose inferential comprehension procedures (or heuristics) attuned to, and taking advantage of, the regularities in the communicative domain. (Note that, in principle, a “substantial innate endowment” does not rule out procedures and heuristics that have to be learnt. If so, the proposed module is a prime candidate for inclusion into the category of natural technologies discussed in Meenan & Lindsay (2002), El Ashegh & Lindsay and Bowman et al. (both this volume). Further empirical research is necessary to validate this point.) Exploring this possibility, of a separate

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specialized comprehension sub-module, is worthwhile, Sperber and Wilson argue, because of the disparate nature of the phenomena involved: Firstly, the range of actions an agent can possibly intend in situational contexts is limited while the range of meanings a speaker can intend in any situation is virtually unlimited. Secondly, a single level of metarepresentation is generally sufficient for attributing intentions to agents (regular mind-reading) while several layers of metarepresentations are typically necessary for inferential comprehension. It is therefore unclear, they say, how the communicator’s meaning (a communicative intention) could be inferred by the same standard procedures that attribute intention to actions or, by the same token, if this were so, how a child of two who failed on regular first-order false belief tasks could recognize and understand the multi-levelled representations involved in verbal comprehension.

The inferential comprehension module RT postulates that the search for relevance is basic to human cognition and people exploit it when communicating. Modifications to world models happen within cognitive environments when assumptions, the basic building blocks of these models, “became manifest” (i.e., are available to conscious awareness). Overt communicative signals from speakers (or communicators) provide evidence of their intentions and create precise and predictable expectations of relevance sufficient to guide the recipients (at whom communication is directed) towards the speakers’ meaning. Note a departure from Grice (1961 and compendium: 1989) in abandoning the Cooperative Principle, the maxims of Quality, Quantity, and Manner and the role of maxim violations. For details see Sperber and Wilson (1986/1995 and 2004). Upon receiving the communicative signals (a sight, a sound, an utterance or a memory), recipients access from memory available assumptions (background information or contexts) that, when connected with the input signals (and not otherwise), yield conclusions (contextual implications) that make a worthwhile difference to their world models (by answering queries, improving what is known on a given topic, eliminating a doubt, confirming a suspicion, or correcting a mistake). To Sperber and Wilson relevance of a communicative input is a matter of degree; It is a function of cognitive costs and awards: the greater the positive cognitive effects and the lower the costs of mental processing expended to achieve them (mainly due to the relative salience of input stimuli), the more relevant the (preferred interpretation of the) input signal. For this reason, selected input stimuli are not just relevant but are more

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relevant that any alternative available, and are, hence, the outcome of making the most efficient use of available resources. Note that the corollary of this view is that in RT the purpose of communicating is narrowed down to, and the relevance of what is said, seen or remembered is sought in association with, a mere desire to improve one’s understanding of the world, i.e., the world model, by either adding new assumptions to it or by strengthening or weakening the already entertained assumptions within it. (For further discussion, see Gorayska and Lindsay, 1993.) Framing the notion of degrees of relevance in comparative rather than quantitative terms, RT bypasses the problem of how the ratio of effect and effort is to be “measured” in real time in psychologically plausible ways. Note that computation itself is effort expending and not all cognitive factors, e.g., levels of attention, are measurable. (For detailed criticisms, see, e.g., Sperber and Wilson, 1987.) Effort and effect are treated as non-representational dimensions of mental processes and comparative judgments of relevance are presumed intuitive rather than absolute, numerical ones. Consequently, the First, or Cognitive, Principle of Relevance (a regularity specific to the communicative domain) claims that humans automatically maximize relevance (because of the way their cognitive mechanisms have evolved due to constant pressure for increased efficiency). We automatically perceive relevant stimuli, activate in memory relevant assumptions, or spontaneously infer conclusions in the most productive way. Further, the degree of relevance that the audience settles for in comprehending communicative stimuli is optimal. In inferential communication ostensive stimuli are designed to attract attention. Communicators intend both to inform others of something and, at the same time, also to inform them of their desire to inform. Consequently, the Second, or Communicative, Principle of Relevance claims that all ostensive stimuli convey a presumption of their own optimal relevance. They are the most relevant that communicators can and want to produce. What this means to the audience in terms of judging efforts vis-à-vis effects is that the designed stimuli are at least relevant enough to be worth processing, and as easy as possible to understand. This leads straightforwardly to the Relevance-theoretic comprehension procedure (a “fast and frugal heuristic”) whereby the path of least effort is followed in automatic computing of cognitive effects. Linguistically-encoded word meanings provide clues to the communicator’s meaning. Inferential processes are employed both in deriving explicatures (completing decoded logical forms of utterances, i.e., conceptual representations of

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what is said, that are fragmentary or incomplete due to linguistic indeterminacy: disambiguating, resolving reference, or lexical-pragmatic processes such as narrowing or loosening in figurative uses of language, etc.) as well as implicatures (conclusions drawn from the explicatures and the background information). Comprehension of what is being communicated is an on-line cognitive task of the recipient whose goal is to formulate and confirm plausible hypotheses about the communicator’s meaning. In a highly parallel manner three subtasks are executed: (1) constructing appropriate hypotheses about the explicit content of utterances (explicatures), (2) constructing appropriate hypotheses about the intended contextual assumptions (implicated premises), and (3) constructing appropriate hypotheses about the intended contextual implications (implicated conclusions). Upon the evidence provided, interpretive hypotheses about the communicator’s meaning are tested in order of accessibility via mutual adjustment of context, content and cognitive effects. The process terminates when the first plausible hypothesis is entertained, which is then considered most plausible and therefore most relevant in the context. Sperber and Wilson show that outcomes of the inference comprehension procedures (or heuristics), hence the operations of the mechanism in comprehending communication, are empirically testable: For example, predictable variations can be witnessed in deriving the speaker’s intended meaning or her deception due to degrees of sophistication in (meta)representation capacity of the interpreter (noticeable in child development). The operations of the mechanism can also serve to explain why in selection tasks such as the Wason task, responses of the subjects can be seen as the output (of deriving optimal relevance) according to the linguistic evidence (clues) provided: people choose options that are supported by the different situational contexts (that manipulate the effort and effect factors) made explicitly available to them (Sperber, Cara and Girottto, 1995). (See also Evans (1982) and Dascal (1987) who show that people also choose options explicitly named in utterances.) 1.2 Limitations of Sperber and Wilson’s Theory of Relevance Though Sperber and Wilson deserve a great deal of credit for their vision in coming to appreciate the importance of the relevance construct, their vision, at least as far as it is so far realised in print remains seriously limited. For example, according to Sperber and Wilson, relevance relationships only exist between propositions, and hence relevance is fundamentally a relationship between symbols or symbol strings. By contrast, we will argue below that relevance is the

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key concept underlying all forms of cognitive processing, non-verbal as well as propositional, connectionist as well as symbolic, not just one of a number of important concepts in one or another sub-domain of cognition. Further, as indicated above, Sperber and Wilson (2004) suggest that the special-purpose inferential comprehension procedures (or heuristics) underlying the linguistic relevance relations with which they almost exclusively deal in their published work are a part of a more general module for processing motivated action. However, there is almost nothing in their work that offers insight into how actions are planned, or how motivation impinges upon this process. The theory is, even being charitable, loosely coupled to mechanisms such as Working Memory and the Episodic Buffer (Baddeley, 2001) or Norman and Shallice’s SAS (Norman and Shallice, 1986). Last but not least, Sperber and Wilson clearly intend their theory to be interpreted as a theory of cognitive processing that is in some sense supposed to be implemented in the brains of human agents. Again however, there are few hints in their work that identify the actual neuropsychological mechanisms that are responsible for processing relevance information. Our intention in the sections below is to describe a theory of relevance that rectifies these omissions: our theory seeks to explain how relevance connects with motivation. Because relevance is a key variable in goal management, the theory tries to link relevance processing with the constructs of psychology by suggesting that relevance plays a fundamental part in problem-solving and action planning. Finally, the theory claims that relevance is neuropsychologically grounded because it is the mechanism by which associative processing in neural networks is converted into hypothesis testing in symbolically represented problem spaces. The first step in delineating our theory is to explain how the concept of relevance is intrinsically bound up with the process of goal management. This will be followed by a discussion of the cognitive function of ethics in managing goals. The paper will end with some consideration of how the proposed theory of relevance processing and goal management can be put to technological use.

2. The ontogenesis of relevance The conceptual intimacy of the link between relevance and goal management derives from the fundamental fact that relevance is a “goal-dependent predicate”; That is to say, whether something can be accurately or meaningfully described as relevant, depends upon the prior specification of a goal (Gorayska

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and Lindsay, 1989a,b; Gorayska and Lindsay, 1993; Lindsay, 1996a). Lindsay, Gorayska, and Cox (1994) have reported evidence which suggests that subjects can reliably match plan elements to goals, and while they can readily formulate effective plans to achieve specified goals using relevant plan components generated by other subjects, they are quite unable to do so when the plan components provided for them are not relevant. Gorayska and Lindsay (1989a,
b, 1993, 1995) and Lindsay and Gorayska (1995) have offered a formal definition of relevance that attempts to capture this goal-dependent character: P is relevant to G iff G is a goal, and P is an essential element of some plan that is sufficient to achieve G. Several computer-based problem-solving systems have been developed which employ relevance (defined as above) as a central theoretical construct (Gorayska et al., 1992; Gorayska and Tse, 1993; Tse, 1994; Ip, Gorayska and Lok, 1994; Gorayska, Tse and Kwok, 1997; Zhang, 1993, Zhang, Nealon, and Lindsay, 1993; Johnson, 1997). It would seem that the idea has some practical utility for supporting AI systems which have at least a limited capability for reasoning and dialogue. As important as the evidence that planning systems based upon the processing of relevance relations are sufficient to generate goal-oriented action plans, however, is the fact that a relevance-based theory can also supply a solution to a problem that at present fatally afflicts symbol-based AI models of reasoning and problem-solving. AI systems for planning and reasoning almost all operate within a set of assumptions developed by Newell and Simon (Newell and Simon, 1972; Newell, 1990; Vera and Simon, 1993). This framework is often called the Symbolic Search Space Paradigm (SSSP) approach (Partridge, 1991). According to SSSP, a problem consists of a set of givens (objects or events), a set of operations, and a set of goals. Application of every allowable operation in every possible order to the givens, generates the state space of a problem. Any sequence of allowable operations is a plan. Solution of a problem requires the identification of a sequence of operations that can be applied to the given state of a problem so as to transform it into the goal state. Problem-solving may be difficult because such solution paths are sparsely distributed within the state space, and because a solver has no direct access to the state space but must construct its own symbolic representation of it. A symbolic representation of the state space of a problem which is constructed with the aim of locating an effective plan for solving the problem is called a problem space. A problem space may differ from the state space of a problem by over- or under-inclusion of objects or operations.

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A central difficulty for AI research, and for theories of human problemsolving is the question of how problem spaces are constructed. Once the givens and operations are known, generating an effective plan is more-or-less mechanical. For some relatively formal problems such as chess playing, the issue is trivial: the problem space must include a representation of the chessboard, the pieces, and the allowable moves. In most cases, however, construction of the problem space is by far the most challenging aspect of problem-solving in AI. In practice, this difficulty has been handled by “hand-crafting” the problem space, that is to say, by using human intuitions to decide what objects and operations are to be used. This tactic bootstraps over the problem, but leaves in its wake the worry that the models that employ it are little more than wellintentioned fakes, appearing successful only because they are being fed a preprocessed version of difficult problems that disguises their limitations. The SSSP framework is immensely powerful and has been successfully applied in AI models of perception, robotics, reasoning in formal and natural language and many types of learning. However, it is clear that for genuinely creative problemsolving to occur, particularly with problems which are incompletely or informally defined, it is essential that a better understanding is achieved of problemspace construction. If human beings can make reliable judgements concerning the relevance of plan elements to goals, it seems possible that problem spaces might be constructed on the basis of such judgements: first objects and operations relevant to some goal are retrieved, then “standard” problem-solving in the sense of trying to locate an effective plan to transform givens to goal may proceed. There is however, a serious obstacle to this proposal: the analysis of relevance offered above defines the relevance of an element in terms of whether or not it is a component of an effective plan. This information cannot be available prior to the existence of the plan, so how can relevance information form part of the input to the planning process? The analysis of problem-solving and relevance processing offered so far is assumed to occur entirely within SSSP systems that manipulate symbolic representations. One possible way in which the circularity identified above may be broken, is for relevance information to originate outside the symbol processing system. How plausible is this suggestion in the case of human information processing? We wish to argue that both ontogenetically and psychogenetically, the case is a strong one. Though it is inappropriate to argue the case in detail here, it seems plausible that human infants are capable of learning, and have considerable problem-solving competence before they have symbolic planning capabilities

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(Cohen and Strauss, 1979; Gottlieb and Krasnegor, 1985). Not only is there a strong a-priori case that such presymbolic learning is essential, as symbols must be interpreted before they can be used, and the process of symbol interpretation must require learning, but there is an accumulating body of evidence that the cognitive competences of human infants in early life can be more successfully explained by non-symbolic connectionist models than by models sharing SSSP assumptions (Elman, Bates, Johnson, Karmiloff-Smith, Parisi and Plunkett, 1996; Plunkett, McLeod and Rolls, 1998). Similarly, there is mounting evidence that adults can show evidence of learning through improved performance without being able to symbolically represent or articulate their knowledge in some domains (Mack and Rock, 1998) or prior to the development of explicit access to relevant knowledge in others (Reber, 1993). It is obvious that learning requires the acquisition of relevance information. If it is true that learning can precede symbolic planning, then it must be true that the acquisition of relevance information can precede symbolic planning. The claim that relevance information may be a precursor of symbol processing raises an immediate further question: what pre-symbolic mechanisms could make relevance information available? A plausible answer is that two distinct processing systems might exist: a higher-level system running on interpreted (or ‘grounded’, Harnad, 1990; but see also Kay, 2001) symbols, the other a subsymbolic system that does not itself use symbols, but that generates outputs capable of satisfying the requirements of its symbol-based partner. The view that human beings have access to two different processing systems has arisen in a variety of forms. Perhaps the best known is the “controlled” versus “automatic” processing distinction of Shiffrin and Schneider (Schneider and Shiffrin, 1977; Shiffrin and Schneider, 1977). This distinction is now treated as a core element in two-process theories of action control such as Norman and Shallice’s Supervisory Attentional System (Norman and Shallice, 1986). In visual perception it is now generally accepted that there are two quite separate cortical pathways involved in processing sensory signals: the ventral route, terminating in the temporal lobe, is believed to process the kind of ‘what’ information originating from the fovea of the eye and carries out detailed featural analysis of static objects. The dorsal route terminates in the parietal lobe and is thought to processes ‘where’ information originating from peripheral vision, and concerned with controlling movement and directing eye movements (Mishkin, Ungerleider and Macko, 1983; Baizer, Ungerleider and Desimone, 1991; Boussaoud, di Pellegrino and Wise, 1996). In memory, it is now common wisdom that a conscious declarative memory system underlies performance in recognition and

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recall whilst a quite separate non-declarative or ‘procedural’ system is responsible for implicit memory phenomena in priming and associative tasks (Squire, 1992). In learning, there is widespread acceptance that “distinct learning systems encode very different sorts of information, one system inducing rules… while a second system memorises instances” (Shanks and St. John, 1993). Shanks and St. John suggest that the system which memorises instances is based on connectionist principles, and the learning exhibited is responsible for the phenomenon known as “implicit learning” (Cleeremans, 1993), which is less likely to be available for verbal report. “In contrast [they claim] connectionist models do not fit well with our understanding of the explicit hypothesis testing also found in the grammar learning literature”. The separate operation of the two systems subserving language is perhaps most readily evident to human agents, who have full awareness of the propositional content and semantic aspects of speech but are entirely unconscious of grammatical operations and the processes underlying speech perception and production. Broca had begun to assemble evidence for the neurological independence of these two subsystems shortly after the middle of the nineteenth century (Broca, 1861). It is unlikely to be a coincidence that within each of the major sub-domains of cognition: action, visual perception, memory, learning and language, there seem to be two distinct systems operating in parallel. In each case, one system requires attention at input, content is explicit and directly available to conscious report processes, and real-time responses are generally fairly slow. The other system operates with unattended or incidentally processed material, is implicit, unavailable to consciousness and generally supports fast nonverbal responses. It does not seem to involve straying much beyond the available evidence to suggest that this duplex architecture is a general structural feature of human cognition, and that in every case the characteristics of the unconscious, nonsymbolic member of the pair seem to resemble those associated with connectionist systems, whilst the conscious and explicit processes correspond more closely with what might be expected in systems conforming to the assumptions of the SSSP. Smolensky (1988), among others, has also proposed that human cognition is likely to be subserved by connectionist systems whose operations are integrated with symbol processing mechanisms. The evidence seems to strongly indicate that a type of cognitive processing exists that is non-symbolic, largely or entirely implicit, and possibly mediated by connectionist mechanisms. This sub-symbolic system seems to support cognitive operations in many tasks when they are carried out by very young children. In adults sub-symbolic processing seems to occur when attention is

Relevance, goal management and cognitive technology

not paid, or is not available (Mack and Rock, 1998). Could sub-symbolic processing of this type yield relevance information? There is every reason to believe that it could. A connectionist system seeks to maximise the probability of certain classes of feedback by varying the probability of outputs as a function of input. In effect, a connectionist system seeks to converge upon a set of input/ output contingencies that do not require it to make any further adjustments to its internal parameters. Without knowing anything about the internal processes of such a device, and merely by treating it as a “black box”, a second system monitoring the behaviour of a connectionist learning system could infer relevance in the following way: An input I is relevant to a goal G when I causes some variation in output which is associated with a change in the value of feedback Roughly, this principle says that if a second system is monitoring a connectionist system during learning, the monitoring system can infer that an input is relevant to the goal state sought by the connectionist system when the output of the latter changes as a result of receiving that input. The validity of the inference arises because the change in output must be an effect of feedback being used to modify internal parameters, and hence of progress towards the goal. Johnson (1997) has demonstrated by building a working AI model, that using this associative relevance principle, a higher-order symbolic planning system can successfully capture and use relevance information. It is important, and not without interest, to note that the “goal” of a connectionist system is usually not represented within the system itself, as it would have to be within an SSSP problem-solver. For example, consider a connectionist system designed to analyse satellite data on CO2 emissions from earth-surface industrial installations. The goal of the system might be to generate one output when emissions exceed an acceptability threshold, and another when they do not. The problem is complicated by the fact that atmospheric variation interacts with source variation, and the system only has access to data collected from beyond the atmosphere. In training the system, the standard against which output accuracy will be judged is independently measured emission levels at the earth’s surface. The design objective (one kind of goal) is to identify as unacceptable all and only those installations that exceed threshold values at ground level. This abstract specification of the system goal will be found nowhere within the system itself. Even the operationalised proxy for the designer goal used in training, a list of acceptable and unacceptable installations in terms of surface measurements, cannot be an essential part of the system, as the system is intended to correctly

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classify installations for which the true value is unknown. For connectionist problem-solvers then, the system goal can, in general, only be inferred from the behaviour of the system. When such problem-solvers are artefacts designed by humans, the system developer can tell us what the design objective was, what the training criterion was and so forth, but even then the criterion might have been ill-chosen (and so fail to match the design objective), or the system may not have been trained on all contingencies falling under the criterion (so that there are behaviour/criterion mismatches in some situations). In either eventuality, the result will be that in some circumstances the observed goal (implicit in system behaviour) will differ from the intended goal (as described by the system originator). In the case of organic connectionist systems that have developed via evolution rather than an intentional design process, there are no design objectives to consult. Extraction of the system goal will require an analysis of those interactions between the structure and behaviour of the organism within which the system is embedded and the environmental constraints under which it behaves that affect the probability of survival and reproduction. Fortunately, the practical unavailability of system goals need not impede inferences about relevance. If, following a particular input-output pairing, a feedback-driven modification of system parameters occurs, then the input triggering the change must be relevant to the system goal, whatever that goal may be. This may seem a long way from the more familiar controlled versus automatic processing distinction which has now become a familiar landmark in the skills literature. The claim is that controlled processing is slow, serial. conscious, error-prone and characterises the early stages of skill acquisition. Automatic processing is fast, parallel, accurate, unconscious, and occurs when a skill is thoroughly mastered. However, if there are two systems, there seems no good reason why the parallel version should be inoperative during the early stages of learning, and if both systems operate throughout the course of learning, the high error rate during the early stages may as well result from poorer quality decisions by the parallel system as from the serial system. Let us propose an alternative scenario. Two learning systems operate in tandem throughout the course of learning. The symbolic planning system is serial, learns by hypothesis testing, which is usually all-or-none, executes relatively slowly, and depends upon relevance information from its connectionist fellow. The connectionist system is parallel, learns slowly and usually incrementally, but executes quickly. The time course of learning could be considerably compressed if relevance information from the connectionist system was used by the symbolic planning system to construct an effective plan,

Relevance, goal management and cognitive technology

which in turn was fed back to the connectionist system, allowing more rapid convergence on a set of network weights which approximated to optimum performance. This architecture may also seem more satisfactory from a functional perspective than a dual component system in which first one, and then the other component does nothing, and the relationship between the two is largely unknown. The proposed architecture has a further advantage: a puzzling feature of relevance is that it seems to have both a subjective and an objective characterisation; this can readily be accounted for by assuming that, while a connectionist learning system seeks to exploit objective relevance information signalled by changing relationships between input, output, and feedback, symbolic plans are always hypothetically related to the external world and are thus subjective in the sense that they may incorrectly represent relationships underlying the regularities they seek to capture. What we have attempted to establish to date is that it is possible that relevance information could become available to a symbolic planning system from a connectionist learning system which operates in parallel with it, and that this suggestion is compatible with views of human information processing which are widespread in the current literature. Our argument goes beyond this however: we want to suggest that relevance is an essential theoretical construct which underpins all symbolic planning processes. An analogy might be made with familiarity in the domain of memory. The case for the utility of familiarity as a construct depends in part upon the demonstration that it has explanatory value, but the case for arguing that familiarity is a real dimension of information processing in memory is bolstered by evidence that the familiarity assignment mechanism can malfunction, for example in cases of déjà vu, or in the moments immediately preceding an epileptic seizure (Penfield and Roberts, 1959). Is there any evidence that the relevance assignment process can function inappropriately in a similar way? Part of our answer is that inappropriate relevance assignment is a common human experience which is often responsible for problem-solving failure. (See further Lindsay, Gorayska and Cox, 1994.) A second part is that inappropriate relevance assignment is a common feature of cognitive disorder in conditions such as schizophrenia: for example Meehl (1962) has discussed cases of what he calls “cognitive slippage” which exhibit precisely the features to be expected as a result of dysfunctional assignment of relevance. Similarly Cutting (1985) cites a wealth of cases of delusional thinking such as the following: “A young man felt that the whole of London was uncertain and strange. He could only be sure of the date if he bought a newspaper that had been printed

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outside London, and only sure of the year if he visited a well known beauty spot to the south of London where there was a fallen tree whose age he knew by the number of rings on the trunk.” (Cutting 1985, p. 319)

It seems hard to deny that cases such as this involve a failure to make appropriate relevance judgements. It is not that a pathology of relevance does not exist; rather, disorders of relevance processing have been attributed to other causes. Together with the demonstrations described elsewhere (Gorayska et al., 1992; Tse, 1994; Ip, Gorayska and Lok, 1994; Gorayska, Tse and Kwok, 1997; Zhang, 1993; Zhang et al., 1993; Johnson, 1997) that relevance can be used to support learning and problem-solving in AI systems, it would seem that the considerations reviewed here establish at least a prima facie case for regarding relevance as a neglected, but important, component of symbolic planning processes. The effect of irrelevant information on problem solving received considerable attention from researchers in the 1930s. Woodworth and Schlosberg (1954) conclude their classic text on experimental Psychology with a discussion of the “atmosphere effect” which includes a review of some of this research. It is reported that in studies of syllogistic reasoning Woodworth and Sells (1935) “hypothesised that the global impression or “atmosphere” of the premises was an important factor in erroneous reasoning” (Woodworth and Schlosberg 1954, p. 846). Woodworth and Schlosberg also note that “the atmosphere effect is not confined to syllogisms. In speaking or writing you are likely to make the verb agree with the single or plural atmosphere of the subject phrase instead of with the grammatical subject, as in the examples: “The laboratory equipment in these situations were in many instances essentially the same as those used before. Is trial and error blind or not?” (Woodworth and Schlosberg 1954, p. 847)

Whilst the phrase “atmosphere effect” is a useful and evocative label, it does nothing to explain the phenomena that Woodworth and Schlosberg discuss. An explanation may however lie in inappropriate relevance attribution which in turn induces subjects to set up problem spaces incorrectly. This phenomenon arises not only spontaneously in syllogistic reasoning and syntactic concordance, but is sometimes engineered by conjurers and experts in legerdemain, as well as by school children in constructing riddles. A well-known example of the latter category is the riddle which proceeds by reminding a dupe that there is an English word meaning a humorous saying or story, which is spelt J-O-K-E and is pronounced joke, and that there is also an English word meaning of, or to do with the common people, which is spelt F-O-L-K and pronounced folk.The

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victim is then asked how to pronounce the word for the white of an egg. Few give the correct answer: A-L-B-U-M-E-N. The initial information concerning spelling is in fact irrelevant, but serves to induce the respondent to believe that the required answer rhymes with “joke” and “folk”. Victims of this riddle presumably establish a problem space which contains some plan such as: ‘find a word which refers to part of an egg and which rhymes with “joke”. Pronounce the word which is found’. For the riddle to work, respondents must also fail to fully process and check all of the information in the riddle question. This failure to exhaustively check information has also reported in the case of “semantic illusion” sentences such as: ‘How many animals of each type did Moses take into the ark?’. Respondents commonly reply “two” to this question, even after repeating the question aloud, and when fully aware that it was Noah, not Moses, who was the protagonist in the biblical flood story (Erickson and Mattson, 1981; Reder and Kusbit, 1991). The “egg-white” example uses redundant preliminary information to establish a fallacious presumption of relevance. There is another type of riddle sentence which, unlike the Moses illusion but like the egg-white trick, uses irrelevant supplementary information to induce a false answer to a perfectly straightforward question. The “paradigm” form of the riddle sentence is illustrated by the following example: ‘I am going to name a colour and ask you a question. Answer the question as quickly as you can: White. What do cows drink?’ Most subjects who have not encountered the problem before erroneously respond “milk” to this query, even though they are fully aware that cows drink water. It is easy to demonstrate that the incorrect “milk” response can equally well be induced by presenting a sheet of white paper immediately before the query, instead of using the word “white”. The “colour-query” problem provides a useful way of demonstrating the difficulty that people can have in correctly assigning relevance to materials associated with a problem. In the cases cited by Woodworth and Schlosberg (1954), it is easy to dismiss the atmosphere effect as a relatively trivial failure to process local syntactic cues concerned with number or negative/affirmative information. The fact that non-linguistic visual input can also misdirect the verbal question-answering process seems to support the much more general theoretical claim that the locus of the atmosphere effect is a modality neutral problem space in which symbolic planning

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processes operate over elements rightly or wrongly classified as relevant to some goal. Deceptive colour-query sentences are not intrinsically deceptive, but when they are preceded by a colour cue, an incorrect answer to the query is primed by the cue and is in many cases articulated instead of the correct answer. We suggest that this occurs because the colour cue is wrongly identified as relevant, and, as a result, is included within the problem space. At least the colour query illusion demonstrates that errors in cognitive processing occur because of failures in relevance processing: if such errors can be triggered by playground riddles, it seems likely that the same phenomenon occurs in less contrived situations. The fact that cognitive processing is also affected when the query sentence is linguistic but the misleading cue is not, may be interpreted as at least weakly supporting the suggestion that the locus of the effect is a modality independent problem space established to support the symbolic planning of responses.

3. The origin and function of goals Goals are symbolic representations of states of the world, or of a planning system itself, which are the “target” of planning processes. Planning processes are attempts to sequence symbolic representations of actions and objects in a manner that allows a goal to be achieved. Planning processes are applied to models of the world and goals, to produce goal-plan pairings which are believed to be sufficient to shift the world from its current state to the target state when the plan is implemented. Goals are always and necessarily abstract and symbolic, though they usually stand for, or represent, states that are not. Goals arise from two different sources: a. Cognitive Goals Most goals are part of complex goal chains, and can perhaps more properly be classified as sub-goals. A goal is cognitive if its achievement contributes to the construction or execution of a higher order plan. Any fully-specified goal must be associated with goal satisfaction conditions (GSCs). GSCs are conditions which an agent believes that the world will satisfy when it is in the goal state. For example, in a chess game the goal is to win, the GSCs are the conditions for believing that “checkmate” has been achieved. The GSCs for a cognitive goal are derived from the requirements of the higher order plan to which its achievement would contribute. Its justification (the answer to a question such as: ‘Why are you doing x?’) is entirely in terms of the grounds for believing that the contribution it makes to a higher order plan is essential or efficient, and that the

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higher order plan will be effective in achieving the higher order goal with which it is associated. b. Terminal Goals The top goal of a complex goal chain does not contribute to a higher order plan. It is therefore non-cognitive. We call a non-cognitive top-goal of this sort a terminal goal. The justification of a terminal goal is exclusively in terms of the desirability of the state brought into existence by the achievement of that goal. All cognitive goals derive their justification ultimately from the terminal goal at the head of the chain of which they form a part. How are GSCs for terminal goals specified? The specification cannot derive from the requirements of a higher order plan, because no higher order plan exists. The question ‘where do terminal goals come from?’ is equivalent in the human case to the question: ‘how does cognition interface with motivation?’ It has been claimed already that goals are symbolic representations. The relationship between any symbolic expression and the world is hypothetical; that is to say, the symbolic expression is one of a set of possible models of the world. A terminal goal must therefore be the consequence of a hypothesis concerning the relation between some possible state of the world that does not currently exist and the motivational system of the cogniser. How could a system that generates such hypotheses develop, and what would its function be? It seems likely that motivation can control behaviour in the absence of intermediary symbolic processes. Human neonates vary their behaviour as a function of motivational states, as do non-human organisms. In many cases this occurs when there is no evidence of a capacity to manipulate symbols. The lack of a capacity for symbolic representation is not however demonstrable, as it requires the proof of a negative existential proposition. For present purposes, the absence of a developed capacity for symbolic representation in most nonhumans and in human newborns will be assumed on grounds of parsimony. Motivation can be conceptualised as the result of a set of subsystems which produce an output in proportion to an increase or a reduction in the value of some system variable, such as the concentration of substances in the bloodstream, or tension in a sphincter muscle. Let us assume that motivational changes of this kind are without effect until they reach some threshold value. A system which is controlled by motivation without cognitive mediation will behave in one of two ways: either the effect of the output of a motivational subsystem reaching threshold will be to nonspecifically increase the intensity and variability of behaviour, or when some “fixed action-pattern” (Tinbergen, 1953) is available, this will be executed as soon as the appropriate “releasers” (ibid.)

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are detected. The problems and limitations associated with such systems are obvious and severe: survival depends upon evolution having anticipated all important needs of the organism and provided appropriate releasers when and where they are required; energy will be wasted on inappropriate behaviour in contexts where consummation is not possible; motivational effects will interfere with one another; prioritisation and deferred gratification are impossible. No doubt all manner of inhibitory links between motivational subsystems could be developed, but the effect of this will be to make increasingly complex bets about the ecological context of behaviour. For example, an organism which suppresses motivational impulses to mate in favour of vigorous action until food is ingested, might fail to eat and to reproduce in difficult times. In the absence of symbolic control, the somewhat bleak picture presented above as well describes a human being as any other organism. If human neonates lack the capacity for symbolic control of behaviour then the features described might be expected to characterise the behaviour of human neonates. Indeed it does seem to be true that some motivational subsystems in newborns operate to switch on fixed action patterns such as those involved in excretion, whereas others increase the variability and intensity of behaviour until a carer intervenes with a diagnosis of the motivational source of the problem, and the offer of whatever is necessary to alleviate it. The point of importance here is that the organism need not necessarily know (indeed in the absence of a symbolic capacity, cannot know, as knowledge is propositional, and therefore symbolic) either what is the motivational cause of its extreme behaviour, or what is required to prevent the cause from operating. The human carer for a newborn acts as an external cognitive support system, proposing hypotheses about underlying motivational states and testing them by changing the infant’s state in various ways. When an organism does not have a carer, it must simply allow itself to be carried along on the current of motivationally driven behaviour until the appropriate context for consummation is encountered. Evolution must ensure a close match between motivation, behaviour, and what exists in the environment to be encountered, if the organism is to survive. If an organism is capable of symbolic representation, then it can learn to internalise the operations carried out by a carer in the case of a human infant. An orphan creature surviving alone could similarly relate a symbolic representation of its motivational condition to a representation of the state of the world that has previously changed that condition in a desirable way. In both cases the same sequence of events must occur: a. Symbolic representation of the motivational condition

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b. Symbolic representation of a world state in which condition (a) does not exist c. Derivation from (a) and (b) a criterion by which the achievement of the appropriate world state can be identified d. Symbolic representation of an action sequence which can cause the world state in (b) to exist. The predisposing motivational context provides the activation conditions for the goal: some aspect of these activation conditions is taken as criterial for triggering the whole goal structure, just as some satisfaction condition is taken as criterial for achievement of the goal. The criterial activation condition might be a feeling, a set of sensations, or a type of motor activity. The symbolic representation of actions capable of causing the world to enter the state which changes the motivational condition is a plan, and (a)–(d) above taken together, define a goal-plan pair. In the AI and problem solving-literature, all of the objects, or “givens”, which are required to solve a planning problem, are usually available. For instance, appropriate pieces and a chessboard are provided when a planner is required to solve a chess problem. In real-world planning a problem-solver does not usually have all required objects to hand. In these circumstances planning processes must specify what objects or material are required to achieve a particular goal, as well as the operations to be performed. It is undesirable for plan implementation to proceed until all of the objects which the plan requires are available. A plan will therefore be associated with implementation conditions; a plan will not normally be executed until these implementation conditions are satisfied. Goals are the cognitive representations of self-diagnosed needs, and wants, together with prescriptions for the states of the world which it is believed will satisfy them. The diagnosis of their own motivational state which individuals arrive at may well be wrong, and it is always possible that a third person who is more experienced or insightful, can provide a more accurate diagnosis. It seems likely that in the case of human beings, diagnosis of motivation is never perfect or complete, though no doubt it continues to improve throughout life. Failure to correctly detect and represent a source of motivation will result in behaviour which does not correspond to any cognitive goal, and which is not under cognitive control. Behaviour of this kind is often said to be the result of “unconscious motivation”. There is an important distinction to be made between positive and negative goals. The distinction is important because the two types of goal can have quite

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different relationships to planning processes. It is hypothesised that wants and needs are unavailable to consciousness unless and until they are symbolically represented as goals. Planning processes are intrinsically and necessarily cognitive and symbolic: plans represent sequences of operations which have not yet occurred, and which if implemented, will result in states of the world which do not yet exist The point of symbolically representing needs, wants, threats, and aversions, as goals is to enable planning processes to be brought to bear upon them. Positive goals represent states of the world that a plan is intended to achieve. Negative goals represent constraints on plans intended to achieve positive goals. The children’s game “Snakes and Ladders” provides a helpful analogy — planning seeks to achieve the positive goal ladders, while avoiding the negative goal snakes. The constraints imposed upon planning processes by the necessity to avoid negative goals, do not necessarily make planning processes more complex or difficult. One of several major problems for planning systems is the “combinatorial explosion problem”. This problem exists because the number of plans to be generated and evaluated rises exponentially with the number of plan elements. With only a small number of elements, the number of possible plans can easily exceed the processing capacity of any realistic system. Negative goals prune planning trees, that is, they reduce the number of plans which need to be considered. In this way, planning may actually be made easier by the existence of negative goals. It is unlikely that negative goals alone suffice to eliminate the combinatorial explosion problem. A similar device can, however, be used to make any planning problem tractable: this is the introduction of further quite arbitrary constraints on planning, which have no other function but to limit the class of possible plans. It seems at least possible that this is the functional explanation for the existence of subjective preferences in human beings. By definition, subjective preferences have no other functional justification, and it is hard to understand how a propensity for subjective preferences could evolve without some functional value. If some preferences need to exist to enable planning to occur, but their value can be assigned quite arbitrarily, then it becomes comprehensible that biology should dictate that people have preferences, but individuals should be free to decide what they are. If this suggestion is correct, it would seem to follow the existence of subjective preferences in a species is an indication that the species is capable of symbolic planning. This analysis of terminal goals and their relation to motivation implies that there are two distinct, and possibly even competing, control systems for human behaviour. The initial control system (ICS) is non-symbolic, unconscious, and

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inefficient in terms of minimising the energy expended to satisfy a given want or need. The goal management system (GMS) is symbolic, conscious, and capable of achieving high levels of efficiency. The problem for evolution is how to pass control from ICS to GMS when the symbolic representations required by GMS cannot be specified in advance of experience. We suggest that in effect, a neonate is equipped with an on-board computer, which seeks to symbolically represent motivational conditions, states of the world that will modify them, and plans that will produce those states of the world. The symbolic planning system is a second-order system creating symbolic representations which capture regularities in the behaviour of the first order system, which operates under the autonomous control of the ICS. As appropriate symbolic representations are established, so the occurrence of motivational activation conditions for goals already represented will result in control passing to the on board computer which can utilise symbolic information in memory to modify those motivational conditions more efficiently. It is hypothesised that consciousness is intimately tied to planning processes, but that once formulated plans may be executed automatically: the activation conditions for a goal can initiate an action sequence directly without any need for further conscious planning processes to occur. There is a clear resemblance here to distinctions such as those between automatic and controlled processing (Schiffrin and Schneider, 1977) and supervised versus schema-controlled action (Norman and Shallice, 1986). Conscious symbolic planning thus has a “managerial” role in the regulation of behaviour: identifying problems, generating solutions, and delegating the implementation of effective plans to lower level agencies whenever possible. The set of goals identified and available at a particular point in the developmental history of the system constitutes the system’s model of its own motivations. There is no reason why this model should ever be complete or accurate. Consequently individuals might be expected to misidentify some sources of motivation, and to fail to identify others, the result of these failures will be the frustration, conflict, and control of behaviour by forces of which they are unaware, which we observe in other people, all the time. Oddly, there seems to be some resemblance between the processes assumed in this theoretical analysis and those of Freudian Psychoanalysis. For example, transfer of control from ICS to GMS has some features in common with the Freudian idea that “where Id is, there Ego shall be”; and the possibility of continued control by ICS where motivated behaviour has not been symbolically captured by GMS might be expected to result in behaviour the origins of which are unavailable to consciousness.

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Some of the advantages resulting from the shift from ICS to GMS and establishing cognitive goals are summarised in Figure 1 below. Evidence for the employment of goal-driven categorisation and an extended discussion of its utility is provided by Barsalou (1991). The importance of cognitive goals in controlling dynamic aspects of behaviour (Figure 1, point 8), is discussed, for example by Carver and Scheier (1982), and Hyland (1987, 1988). Oatley and Jenkins (1992) have also explicitly linked cognitive goals to emotion: “Emotions have a biological basis. The most plausible current hypothesis is that they function (a) within individuals in the control of goal priorities, and (b) between people to communicate intentions and set outline structures for interaction” (Oatley and Jenkins, 1992, p. 78). In the account we offered earlier of the origin of relevance information, we proposed that a symbolic planning system runs in tandem with a connectionist learning system and establishes relevance relationships by seeking systematic relationships between input to the connectionist system, and variations in output and feedback. In our account of goals, we have proposed that a symbolic planning system establishes symbolically represented goals by seeking systematic relationships between motivational conditions and those states of the world that change them. We have noted already (see pp. 68–78 above) that this theoretical claim is consistent with evidence that in many cognitive domains two distinct processing systems exist. A clear example comes from work on the “blindsight” phenomenon, whereby visual information can apparently continue to guide forced-choice motor responses, even though, as a result of cortical damage, such visual informationship is not available to verbal report or to guide planning processes (Weiskrantz, 1988).

Benefits of Cognitive Goals 1. 2. 3. 4. 5. 6. 7. 8.

Generation and testing symbolically represented hypotheses Goal management, including use of goals as plan elements, and goal prioritisation Plan optimisation Voluntary control of behaviour Relation of current goals to symbolically represented material in memory External control of behaviour via symbolic input Enabling goal-driven categorisation Provision of reference criteria for dynamic aspects of behaviour

Figure 1.A summary of some of the benefits conferred by establishing cognitive goals.

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4. A goal management system On grounds both of parsimony and theoretical coherence, it would seem reasonable to suggest that there is a single goal management system which utilises (and may lose contact with), perceptual information, motivational information, and relevance information as part of an integrated process of establishing goals, diagnosing when it is appropriate to seek them, and formulating plans for their achievement. There is a good deal of plausibility in the general notion that the symbolic representational system which mediates understanding of the world is unified and second-order. It is not essential for motivation, perception, or action — how could it be without symbols having innate meaning — for newborns can be motivated, can perceive, and initiate action. The symbolic planning system can however offer the possibility of rational planning and systematic management. It can hugely enhance the efficiency of learning by making available hypothesis testing as an additional problem-solving procedure. But most importantly, it can create a whole new world of possibilities through communication and cooperation. The existence of a goal management system implies the necessity for planning processes that use goal representations as their components. This in turn implies the existence of metagoals and heuristics for establishing and manipulating goals. Whilst we do not intend to discuss these processes in detail in the present paper, some examples of the kind of heuristics we have in mind are presented in Figure 2 below. Many of these heuristics involve plans that incorporate other agents. Goal management inevitably forms a bridge between individual cognition and social processes. We next turn to ethical reasoning, an aspect of cognition with respect to which social behaviour is brought centre stage. Though there have been some instructive explorations of the development of ethical values (Kohlberg, 1981), Cognitive Psychology and Cognitive Science have almost entirely neglected ethical problems and ethical decision-making. This neglect is unfortunate, as it is difficult to see how the cognitive regulation of human action, understanding of the actions of others, and comprehension of action related discourse can dispense with ethical notions. Ethical language and concepts constitute such a large proportion of human discourse that it is almost inconceivable that such terms and concepts are devoid of cognitive significance.

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Metagoals and their Implications 1. Bring as much behaviour as possible under cognitive control — identify and symbolically represent as many needs and wants as possible. 2. Plan so as to maximise the value of positive goals achieved. Three factors will contribute to decisions here: the value of each goal in the set about which a decision is to be made; the number of goals which can be attempted simultaneously or during the interval over which the decision process ranges; the expectancy that goals will be achieved if an attempt is made to achieve them. 3. Plan so as to minimise the number of negative goals experienced. Negative goals will include the experience of failure. This metagoal will therefore entail decisions concerning the competence of the system itself, the probable effectiveness of plan proposals, and the level of difficulty of tasks to be attempted. A system’s estimation of its own competence is hypothesised to be equivalent to the construct labelled “self-esteem” in studies of human judgement and performance. 4. Monitor achievement and switch goals if progress is unsatisfactory. This metagoal can be related to emotion via the role of control theory in goal-setting. (Hyland, 1988) 5. Plan to optimise parameters such as time and cost (effort, and derivatives such as money, materials, etc.). 6. Store frequently used plans. 7. Optimise planning and representation processes. This metagoal will require the relationship between plans and states of the world to be represented in memory as simply as possible. 8. Enlist the cooperation of other agents whenever this is advantageous. Other agents may assist not only in developing and executing plans, but also in, for example developing symbolic representations for the efficient use of memory. 7 and 8 together suggest that science may be a socially externalised institution for the discovery of efficient data compression algorithms. This would explain why parsimony features as such an important constraint on scientific theorising; something which has hitherto proved resistant to explanation. 9. Increase the capability of the system for planning and goal representation. GMS must have learning goals, as well as performance goals 10. Learn from other agents under appropriate conditions. The potential gains from utilising the experience of others are clear. There are however many important questions to be asked about the circumstances under which a cognitive system can reasonably decide to modify its permanent memory on the authority of another. Clearly such attributes of the “authority” as credibility, consistency, honesty, integrity, character, etc., are relevant. In our own society, free reign is usually given to professional teachers to modify and shape the cognitive system of children. Quality control of the modification process is handed over to external agencies. 11. Try to maximise the probability that other agents can be relied upon. This will entail minimally, choosing friends and teachers carefully, but less directly, advocacy of honesty, discouraging deception, insistence that promises be kept, etc. 10 and 11 together make a case for the pragmatic utility of ethics in the management of cognitive processes. This case is further explored below. 12. Teach other agents if this might enhance their power to cooperate or to assist others to do so.

Figure 2.Examples of metagoals and heuristics used in the cognitive management of goals.

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5. Goal management and the cognitive function of ethics The function of ethical language has been largely ignored, both by Linguistics and Cognitive Psychology. In a recent study by one of the authors (RL), in which undergraduates were tape-recorded while discussing the design for a laboratory practical, no less than 45% of sentences contained some element which could loosely be classified as ethical, such as “ought”, “should”, “good”, etc. We do not wish to make a great deal of this, and accept that different samples of people on different days or with different topics might yield different data. But the finding does serve to underscore what is probably obvious: ethical language is a ubiquitous feature of the everyday cognitive environment. Philosophy is probably much to blame for the lack of attention to the cognitive function of ethics. There is an apocryphal story of an Oxford graduate who was asked whether he had learned anything which had endured from his study of the philosophy of ethics. He replied that he had learned never to use the word “good” as it was clear to him that no-one knew what it meant. Ethics in general has become almost inextricably mired in metaphysics, and this has served to distract attention from its cognitive function. This is in spite of the fact that a number of relatively recent ethical philosophers (Urmson, 1950; Hare, 1952; Hampshire, 1960; Gauthier 1963) have offered theories of ethical language which emphasise its role in making and influencing decisions, and in planning actions. It seems likely that the major reasons for the failure of Linguistics and Cognitive Psychology to take up and apply these theoretical analyses are: a bias towards individualism, the absence of any satisfactory theoretical framework which can offer an account of how ethical principles can be related to cognitive mechanisms, and how ethical language can be used by one person to influence the planning processes of another. The theory outlined in this paper attempts to overcome individualist bias, and to explain how ethical language and cognitive mechanisms are interrelated. To begin quite generally, we believe, along with many other recent commentators, that syntactic and semantic approaches to language have proved incapable of providing any real insight into language mechanisms. A more promising approach is to seek to analyse language processes at the level of pragmatics. On this assumption an articulated sentence is an implemented plan intended to influence the behaviour of its audience. The locus of its intended effect is the symbolic planning processes of the recipient of the sentence. On the basis of arguments presented earlier, this means that a

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sentence may have its impact upon the recipient’s goals, or plans to achieve a goal (holding objects and operations constant), or upon the objects and operations employed in planning. In the GMS framework, sentence comprehension is the process by which the hearers of a sentence recover the intended effect of that sentence upon their own planning processes; utterance comprehension is the process by which hearers seek to reconstruct the plan which led a speaker to utter a particular sentence. These may differ: a speaker may interpret the sentence “Fire!” to mean that the goal of vacating a building should be adopted, but the plan leading to its utterance may be to steal the hearer’s briefcase. This approach has been growing in popularity over recent years, both in Linguistics (Levy, 1979) and in Artificial intelligence (Bruce, 1975; Allen and Perrault, 1978; Cohen and Perrault, 1979; Appelt, 1985; Carberry, 1990). Its main attraction is that it moves beyond analyses of language which are little more than impoverished paraphrases in some logical formalism; it embeds language processing in more general cognitive operations, and in action planning in particular; and it offers some solution to the problem of indirect speech, which has proved quite intractable within syntactic and semantic approaches to language. To illustrate this last problem: it has not proved possible to find convincing syntactic or semantic grounds for treating ‘Buy me a drink’, ‘My glass is empty again’, ‘I wonder if the barman will cash a cheque?’, and ‘Would you like another?’ as linguistically equivalent. It is much less difficult to see how each could be uttered with a view to producing an equivalent effect on the planning processes of an unobservant companion. We have proposed elsewhere (Gorayska and Lindsay, 1989a,
b; Lindsay, 1996b), that the processing of goals, plans, and plan elements are constrained by social factors as well as by the characteristics of the physical world. Other social agents may impose or enjoin the adoption of particular goals, or the use of prescribed plans or plan elements to achieve a goal which has been freely selected. In our culture imperatives are used to signal an attempt to impose constraints upon symbolic planning processes, while ethical terms are used to enjoin an agent to adopt a goal, a plan, or a plan element. To give one or two examples: ‘you should try to get a job’ is to enjoin a goal; ‘drive on the left’ is an imposed plan; ‘you ought to take a bottle of wine to the party’ is an enjoined plan element. A probable objection to this claim is that at best it can only apply to “prudential” and not to “genuinely moral” reasoning — the distinction which Kant (1953) sought to capture by contrasting hypothetical with categorical imperatives. Our answer has three components: firstly metaphysics has hampered

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understanding of ethical language, not helped it; there is no respectable independent justification for the notion of objective moral ends. Secondly, there is no convincing case for the claim that words like “ought” are ambiguous: ‘It is surely more plausible to argue that “ought” is not multiply ambiguous, that ……different equivalents correspond not to different uses of “ought”, but only to the differing grounds underlying the judgement’ (Gauthier 1963, p. 21). Thirdly, the use of “ought” and related words for the purpose of enjoinment are best construed as signals that the narrow interests of the agent are not the only interests at stake: the prudential concerns of social groups of which the agent is a member are also relevant, and these concerns may be presented “as if” they are objective constraints on an agent’s choice of action. There now exists a well-established AI technology concerned with problemsolving, decision-making and action planning, which is known as “expert systems” research. The basic premise upon which this research is founded is that intelligent decisions and actions require the application of logical operations to knowledge. Expert systems thus consist essentially of an “inference engine” which controls inference, and a “knowledge base” which contains appropriately represented knowledge. “Knowledge” consists of facts, rules, and heuristics, which are elicited from human experts. Though humans are indubitably more complex than current expert systems, these distinctions are often helpful in considering human cognition. In the present context, the question suggested by analogy with expert systems is whether ethical knowledge which constrains human action planning can be captured as facts, rules and heuristics? Our view is that not only is it true that ethical knowledge can be accommodated within this framework, but that some traditional ethical problems cease to be troublesome when so conceptualised. Propositions such as ‘x is good’ are clear candidates for ethical facticity, closely resembling as they do such naturalistic facts as ‘grass is green’. Similarly ‘do not steal’ has both the form and the substance of a rule. Perhaps the most interesting of the three categories, however, is that of ethical heuristics. Heuristics are decision-making principles which can profitably be employed when it is too costly to compute and evaluate every possible cognitive option. An important feature of a heuristic is that though in general it increases the probability of a positive outcome, there is no guarantee that this is so. For example, in chess playing, where the number of alternative moves rapidly exceeds exhaustive computability, such heuristics as ‘dominate the centre squares’ and ‘protect your queen’ are often used. No matter how diligently such heuristics are employed, their use will not ensure victory. Plausible candidates for heuristic status in the area of ethical knowledge

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are principles such as ‘killing is wrong’ and ‘promises should be kept’. Much philosophical energy has been expended on demonstrating that universal application of these latter principles results in ethical paradoxes when, for example, killing is morally justifiable, or promise-keeping results in manifest harm. These paradoxes cease to puzzle when the principles which give rise to them are interpreted as heuristics, when invoked by an agent in action planning, which are bound to yield negative outcomes in some cases; and as enjoinment devices when invoked by a third party, which function to constrain the actionplanning of others, not to capture ethical absolutes in propositional form. It is widely acknowledged that limitations on processing time and capacity make it inevitable that human beings use heuristics in planning and decisionmaking (Kahneman and Tversky, 1982; Kahneman, Slovic, and Tversky, 1982). Most studies to date which have presented empirical evidence demonstrating the effects of heuristic use, have focused on the processes of memory search and retrieval. There is a strong case for arguing that effects of heuristic use might be expected to manifest themselves most strongly in the domain of action planning, particularly where agents must allow for interactions between their own behaviour and that of others. Certainly, limitations on computability will drastically restrict decision-making in this area. The problem of planning actions which affect, and are affected by other agents, is precisely the domain to which ethical principles apply, and it is therefore tempting to suggest that much of the sophisticated wrangling over the universality of ethical principles in which philosophers have engaged is best interpreted as dramatic evidence of the operation of cognitive heuristics. A compelling recent example is Baron’s (Baron, 1994) discussion of “nonconsequentialism”. Baron argues that while rational agents should seek to maximise the extent to which they achieve their goals, their are classes of case in which people demonstrably fail to do so, for example in laboratory studies, subjects consistently award “third-party compensation on the basis of the cause of an injury rather than the benefit of the compensation, they ignore deterrent effects in decisions about punishment, and they resist coercive reforms that they judge to be beneficial” (Baron, 1994, p.1). He infers that in such cases defective decision rules are being employed which should be identified and corrected. No-one would similarly be tempted to suggest that an expert system which produced suboptimal decisions in some cases was malfunctioning: this is exactly what would be expected of a heuristicbased decision-making system. Similarly, if consequentialist principles function as heuristics, then the reported cases of tolerated suboptimality are entirely to be expected.

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If actions affect other agents, and actions are partially determined by knowledge, then the mechanisms of enjoinment and imposition might be expected to apply even to knowledge that is not intimately connected with action planning, as ethical knowledge obviously is. We believe that these processes do operate on knowledge which is relatively naturalistic. To give an example: in Western society, university science is one of the main institutionalised vehicles of knowledge manufacture. As the process of knowledge refinement is endless, those involved in the refinement process are aware that that knowledge is always provisional and revisable. In teaching and disseminating the results however, belief in currently accepted theory is enjoined. It is that which students and the public must believe and act upon, if their actions are to have the best chance of success. The consequence is the frequently noted paradoxical tendency of apologists for science to claim both that current theory is demonstrated truth, and that current theory will inevitably be modified and revised in the future. There are two implications of the claim that the mechanisms of imposition and enjoinment are the central devices by which a group seeks to generate consistency and coherence in the beliefs and actions of its members. One is that imposition and enjoinment are important determinants of between-group cognitive differences. The other is that what is enjoined and imposed within a group is crucial to an understanding of the cognitive processes of group members. Explanation of cognitive differences between cultures and sub-cultural groups has not in general been an area in which Psychology has been conspicuously successful. Early approaches assigned a constitutional origin to any observed differences; later, linguistic differences were invoked, for example by Whorf (1956) for cross-cultural differences and by Bernstein (1964, 1971), for differences between subcultures. More recently attention has turned to ecological variables. Irvine and Berry (1988), for example, propose an ecological interpretation of the “Law of Cultural differentiation” which asserts that “all populations have the same perceptual and cognitive processes and the same potential for cognitive and perceptual development, but ecological and cultural factors “prescribe what shall be learned and at what age; consequently different cultural environments lead to the development of different patterns of ability (Ferguson, 1956, p. 121)”” (Kagitcibasi and Berry, 1989, p. 498). The mechanisms by which differences in ability arise are to say the least, underspecified. The theoretical approach developed here and elsewhere attempts to be much more specific about what these mechanisms are.

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One important factor is what we have called the “fabricated world” effect (see Gorayska and Lindsay, 1989a,
b and further discussion in Section 6 below) that can massively amplify intrinsic ecological differences. For example, instead of investing time in remembering a complex and arbitrary environment, people may instead choose to construct cities laid out according to simple algorithms: the world is adapted to the limitations of memory rather than vice versa. Similarly buildings are frequently organised around goal satisfaction, with specific rooms to eat, sleep and cook, or specific buildings to drink, dance, or borrow a book. In this way, commonly sought goals in a culture become realised as physical structures, and goal-oriented planning is conditioned by the physical possibilities that exist. A second way in which cultural differences in cognition can be induced, is by the use of imposition and enjoinment to constrain knowledge and planning processes. Initially the locus of imposed and enjoined constraints is the symbolic planning system, but as we have suggested earlier, once successful plans are formulated they are likely to be downloaded to an automatic, non-symbolic, action generation system, which means that the constraints initially imposed via ethical and imperative forms of language eventually become unconscious and habitual. If this account is correct, then human planning activities are, at least in part, shaped and controlled by enjoinment, through the use of ethical language. As particular ethical exhortations are usually, if not necessarily, derived from more general ethical values and principles, it should follow that the goals, plans, and actions that arise as a result of the enjoinment process are systematically related to the ethical values and principles that underlie them. This in turn suggests that ethical values and principles can be used by outside observers to explain regularities in the behaviour of members of a group within which those values are current, and that they can be employed by group members themselves, to assist in understanding the behaviour of their fellows. Some of the ways in which ethical principles and maxims can contribute to the cognitive processes underlying action planning are summarised in Figure 3 below. Ethical values and principles operate to reduce the cognitive demands of goal selection and planning, to enable cooperative goal-seeking, and to facilitate the construction of mental models of other agents, derived from their actions, which permit cognitive recovery of their goals. The cognitive benefits of ethical constraints on reducing the number of possible plans to be evaluated in actionplanning contexts are closely analogous to the effects of subjective preference, discussed earlier (see p. 82). It is now widely accepted that AI models that seek to understand human text about the social and physical world require access to

Relevance, goal management and cognitive technology

Cognitive Functions of Ethical Values 1. 2. 3. 4. 5. 6. 7.

Facilitating choice of goals Reducing the range of acceptable plans and plan elements to be considered Conveying plans not validatable within a single lifetime experience. Eg.: ‘rules for a good life’ Providing a framework for cooperative goal-seeking Enabling socialisation via “enjoinment” Offering a source of principles to explain the behaviour of other agents Allowing the credibility assessment of agents attempting to enjoin knowledge

Figure 3.Some cognitive functions of ethical values.

a rich set of physical principles and social conventions. An example is given by Charniak (1972): Jane was invited to Jack’s birthday party. She wondered if he would like a kite. She went to her room and shook her piggy bank. It made no sound. Charniak argues that a text comprehension system would need to know at least, that: – – – – –

Guests are expected to take gifts to birthday parties. A kite is a suitable gift for a child. Purchasing a gift requires money. Money is often kept in piggy banks, particularly by children. Piggy banks containing coinage emit sound when shaken.

In the absence of ethical principles, the actions, including dialogue, of artificial cognitive systems cannot be comprehensible as human action is; nor without access to ethical values as a source of hypotheses can artificial systems understand the behaviour of humans, learn from them, or cooperate with them. If machines are to be treated as agents, or even to understand the behaviour of agents, they must first be given ethical values. It might be admitted that ethical values and principles have some connection with action and action planning, and yet be denied that this confers any particular plausibility on the model we have proposed which assigns a fundamental role to the processing of relevance information. Is there any good reason to assert that ethical reasoning and relevance processing are closely connected? We believe that there is. If ethical knowledge is represented in memory as abstract rules and general principles and heuristics, much of ethical reasoning must be concerned with determining which principles and heuristics are

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relevant to particular contexts in which action is required. If we are correct, then the notion of relevance must play a central, though unacknowledged part in ethical debate. In support of our contention, we quote from two philosophical texts concerned with ethics, these texts were chosen simply because they were readily available in the circumstances under which are writing: any of a wide range of others would no doubt serve as well. Singer’s (1963) book is concerned with the problems of generalisation associated with the Kantian “categorical imperative”, crudely, the claim that an agent ought to carry out only those acts which they are prepared to “will as universal law”, that is to advocate that any agent ought to carry out under similar circumstances. We take Singer’s book as an example because it is centrally concerned with those judgements generally regarded as uniquely ethical. Singer notes that there are serious problems involved in deciding who is similar to an agent making an ethical judgement, and when their circumstances are similar, and when not. He argues that “while some similarities, and differences, can always be specified, not all of them will be relevant ones. The generalisation principle must be understood in the sense that what is right for one person must be right for every relevantly similar person in relevantly similar circumstances” (Singer, 1963, p. 19, author’s italics). In discussing specific moral principles, Singer declares: “though they hold in all circumstances in the sense that violations of them are always wrong, they are not relevant in all circumstances. On the contrary they are relevant only where the corresponding rule is relevant. The principle that it is always wrong to kill for the sake of killing is not relevant where killing is not involved” (ibid. p. 109). By contrast with Singer, Gauthier (1963) denies that there are uniquely “categorical” (or ethical) judgements. He believes that all ethical reasoning is “practical”, that is rooted in the specific context in which action is necessary. His dependence on the notion of relevance is however, no less: “a person need not consider all wants in determining what to do. Only a minute fraction of all wants have any possible relevance to the situation in which he is to act, or to what he may do. Indeed, most of the wants in each person’s practical basis never do nor can enter into his reasoning, although it is impossible to provide criteria to determine, in advance, just which wants may possess practical relevance” (Gauthier, 1963: 86). The quotations provided above only illustrate the point we have argued, they cannot prove its truth. Nonetheless, the considerations to which we have drawn attention add up to a powerful case for reconsidering the role of ethics in cognition. We have argued that ethical knowledge plays an important part in

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action planning; that ethical language is used to shape the cognitive processes of members of social and cultural groups; that ethical values and principles are the main dimensions along which social actions are interpreted and understood; that artificially intelligent systems must act upon and have access to ethical values and principles if they are to participate in dialogue with humans and act in a manner which is considered meaningful; and finally, that because of the part played by ethical knowledge in action planning, relevance information is central to the use of ethical knowledge and though never acknowledged, has consistently been invoked as a prominent element of ethical discussion.

6. The fabricated world hypothesis This hypothesis was first proposed by Gorayska and Lindsay (1989a,
b). The name assigned to the hypothesis was intended to reveal its debt to the earlier “carpentered world hypothesis” of Segall, Campbell and Herskovits (1966). In the 1950s and 60s a large body of evidence was accumulated indicating that preliterate, rural tribespeople were less susceptible than urban dwellers to a range of visual illusions. Campbell, Segal and Herskovits suggested that this might be because constant exposure to regular rectilinear structures in the “carpentered world” of modern cities predisposed the perceptual systems of city folk to interpret two dimensional arrays of straight lines as geometrically regular figures in 3-dimensional space. People from a rural environment in which rectilinearity is almost never encountered were less likely to use the same visual processing heuristic, and hence experienced the illusion to a lesser extent. Lindsay and Gorayska suggested that cognitive effects created or amplified by the environment were not confined to visual perception. Built environments, or “fabricated worlds”, have very different information processing properties from natural environments. The human cognitive system is only able to control motor vehicles travelling at scores or hundreds of miles per hour because highways afford an artificially predictable context in which relevant change is relatively rare and usually signalled well in advance, for example by road signs. Millions of people a year find their way through unfamiliar airports, often without assistance, because of the informational structure of airport organisation. Strangers can navigate abroad, because engineers have constructed an environment that embodies informational structures already in the head of the traveller. If we know what people know, we can design an environment that fits their preexisting knowledge structures like a glove. Novel environments can be mastered

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without new learning because they have been engineered to fit old learning. The idea that the human cognitive system is not a stand-alone device, but part of a larger unit which incorporates elements of the external environment (the parts we have learned about) has arisen in several contexts. Andy Clark (2001) uses the term wideware to capture the fact that memory is outside in the world as well as in the head. Taking the same idea, that human cognition must be viewed as part of a larger whole that incorporates the learning environment: O’Regan and Nöe (2002) have sought to explain unexpected perceptual phenomena such as change blindness. This term is used to refer to the discovery that for example, when a visual image is modified even as it is being inspected, if the introduction of the change is masked by a flicker, the change to the image even though perceptually gross, is often not noticed. Similarly, people frequently fail to notice when a receptionist interacting with them bends below a desk, and is replaced by a different person who continues seamlessly with the interaction. O’Regan and Nöe argue that change blindness occurs, because people do not have access to a “direct” representation of the perceptual environment in memory, that can be compared with current input to detect change. But it seems that we do: phenomenology gives us a false impression of the nature of our own consciousness. Instead of a cognitive representation of external reality inside our heads, O’Regan and Nöe suggest that the world is its own memory — perceptual memory doesn’t need to remember everything, only the “motor coordinates” of where information not currently relevant can be found if needed. An AI system built on similar assumptions, that directly uses relevance information to control what parts of a visual array are seen was described by Ip, Gorayska and Lok (1994). O’Regan and Nöe however, draw much more far-reaching implications from the idea that the environment is embedded in human cognitions: “we suggest that the basic thing people do when they see is that they exercise mastery of the sensorimotor contingencies governing visual exploration. Thus, visual sensation and visual perception are different aspects of a person’s skillful exploratory activity”. The existence of sensory modalities can readily be explained by this approach: “the difference between seeing and hearing is to be explained in terms of the different things that we do when we see and hear”, and there are implications for understanding consciousness itself: “way of thinking about the neural bases of perception and action is needed that does not rest on the false assumption that the brain is the seat of consciousness”. These recent suggestions that mind and the environment are an integrated whole seem to considerably increase the salience of a relevance-based theory of

Relevance, goal management and cognitive technology

cognition. Relevance-based processing provides a mechanism that is sufficient to explain how the mind-environment duplex operates: action planning confers relevance, because only the set of perceptual features that can form part of action plans currently being developed or executed is available within the symbolic problem space. Associative relationships developed within the connectionist component of the cognitive system provide at least the initial set of features to which the problem space has access. This arrangement will naturally cause the development of cognitive modules and sensory modalities as a collateral outcome. When two problem space generators use disjoint operator sets they are functionally independent processing systems: cognitive goals constrain the set of plans that will work; the set of plans that will work constrains the set of plan elements that can be used in implementation. When operator sets are disjoint, relevance discontinuity exists between the two systems: operators used by one problem-space generator are never relevant to the other. Whilst many theorists (e.g., Fodor, 1983, and most present-day neuropsychologists) have proposed that the mind is modular in organisation, it has proved impossible to locate a neurophysiological basis for this organisation. Damage to the brain can sometimes cause specific deficits in language, perception or memory, but damage to identical parts of the brain in other patients does not invariably lead to similar neuropsychological impairments. Modules seem to exist in the mind, but not in the brain. A GMS processing relevance information appears capable of showing how this can occur. The Fabricated World Hypothesis goes beyond the bare assertion of the environmental embodiment of mind, to draw attention to the technological possibilities of manipulating mind by engineering the environment.

7. The cognitive technology of goal management Let us suppose that the theory of relevance processing and goal management that we have described is true. What are the pragmatic implications? How can this knowledge be put to technological use? a. Controlling problem spaces via connectionist learning and modifying connectionist models via problem spaces The theory of cognitive goal management via relevance relationships proposes that the unconscious and automatic control of actions is handled by a connectionist learning system that provides the base inputs to a conscious symbolic hypothesis testing module that operates by developing explicit propositional

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models of the behaviour resulting from lower-level connectionist control processes. A symbol-based cognitive system that is sufficient to learn by imitating the behaviour of others, must also be sufficient to learn by symbolically modelling the system’s own behaviour. However, the set of operators available to the symbolic processor is restricted to the set of operators designated as relevant by connectionist learning processes. The more powerful hypothesis testing paradigm available within the symbol-based learning system may require the use of operators not designated as relevant by connectionist processes, and hence cognitively unavailable, or available only in propositional form as intellectual beliefs that seem “contrary to intuitions” or unconnected with how a person acts. One practical implication of the goal-management theory is that action-planning at the symbolic level can be facilitated, and actions can be brought into line with beliefs by providing learning experiences that add new operators into the problem space. For example, the beliefs developed by theoretical physicists about the relationship between matter and gravity are symbolically coherent, but are usually found to be counter-intuitive and cognitively complex, presumably because they can only be handled by a serial operation working-memory with restricted processing capacity. However, the mass-gravity relationship can be modelled by using a rubber membrane distorted by objects of varying mass that are placed upon its surface. Experimenting with a model of this kind is directly training the connectionist actioncontrol system to operate with an extended set of action possibilities. But the utility of this particular training procedure could only be established by working through the higher-level symbolic system — it could not have been arrived at directly by observing the effect of action contingencies. In a way, there is nothing new here: every teacher knows that if intellectually abstract and complex relationships can be experienced as physically instantiated by some model, learning is facilitated. What we are providing is an explanation of why this occurs, and an insight into the mechanisms underlying the process so that these processes become easier to manipulate in a principled way. b. Using shared learning to develop cognitively efficient, fabricated environments (eliminating relevance discontinuities) It is old news that operating environments can be made user-friendly by ensuring that they meet user requirements. It probably follows from Norman and Shallice’s (1986) model of action control that user requirements have more to do with habitual patterns of action controlled by the unconscious implementation of schemas in the contention scheduling process, than with consciously controlled behaviour. These familiar principles are surely given a new spin

Relevance, goal management and cognitive technology

when we suspect that the reason schema-driven action is unconscious is that it results from connectionist processes operating in a biologically primitive action control system. Further, modification of this system cannot occur directly through top down influences from the symbolic processor because connectionist systems cannot be symbolically programmed. Changing someone’s beliefs about effective actions in a given domain will only change their behaviour when slow serial voluntary control is exercised, or when as a result of changed beliefs, an agent systematically practices new stimulus response contingencies to bring actions and beliefs into line. Think about a concert pianist learning a new finger movement: knowing how the action is to be executed at the level of belief, may provide the incentive for practice and the feedback by which practice is guided, but the belief is no substitute for the practice. Now it becomes quite clear that if what we mean by meeting user needs is enabling the direct transfer of schema-controlled actions, we cannot establish user needs by interviewing users, or by asking them to complete questionnaires. Cognitive tools built on the basis of such data, generated by what are probably the most commonly used methodologies in interface engineering, should match performer beliefs, but this provides no assurance that they will support skilled action. The new model suggests that we should first seek discrepancies between the two control systems, then, when discrepancies exist, decide which system it is most important that a new tool interfaces with. Ramachandran (1996) has demonstrated how cognitive control of sensory feedback from (phantom) limbs can be established by modulating visual input (using mirrors to fool the connectionist system into acting as if an amputated limb is s