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This classification is somewhat misleading, however, because many of the current members of the editorial board do research that crosses over into other disciplines, and several have appointments in departments of cognitive science. In the early years, as today, the journal and the proceedings of the annual conference consisted predominantly of articles that are psychological and computational, although papers oriented more toward linguistics, neuroscience, and philosophy occasionally appear for an insightful analysis, see Schunn, Crowley, and Okada, The Cognitive Science Society actually followed the journal, originating in , although the journal was later given to the Society by its publisher, Ablex.
The organization began with a meeting at the Dallas airport initiated by Allan Collins, Donald Norman who did not want to travel to the East coast , and Roger Schank who did not want to travel to the West coast. Edward Feigenbaum, AI, Stanford. Charles Fillmore, linguistics, University of California, Berkeley. Jerry Fodor, philosophy and psychology, MIT.
Walter Kintsch, psychology, University of Colorado. Zenon Pylyshyn, psychology, University of Western Ontario. Eleanor Rosch, psychology, University of California, Berkeley. Roger Schank, AI, Yale. It is interesting that the twelve founding members of the executive committee included five artificial intelligence researchers, five psychologists, a philosopher, and a linguist.
Since then, the society executive committee now called the governing board has tilted more toward psychology and away from artificial intelligence, reflecting the evolution of the society. The thirteen members of the governing board, include eight psychologists, three AI researchers, a philosopher and a linguist. It is notable, however, that the philosopher Thagard , the linguist and one of the psychologists each works with computational models. According to the minutes of the meeting recorded by Eugene Charniak and Donald Norman, the two main issues discussed were the nature of the membership of the organization and the role of AI in it.
The main reason for making this distinction seems to have been to eliminate the need for refereeing papers at the projected annual conference, following a model used by the Psychonomic Society. Some members of the first executive committee thought that the Cognitive Science Society should be an artificial intelligence society and should try to host an annual AI conference. But such close identification was resisted by other members of the committee, and in the American Association for Artificial Intelligence was formed and began its own annual meeting.
Typically people attend, out of the approximately people who belong to the Society, and the conference proceedings include hundreds of papers and abstracts. A standard feature of the conference is a set of symposia that have speakers from more than one discipline. The content of the conference can vary greatly from year to year, reflecting the different interests of the organizers, who are largely drawn from the host institution. It would be very difficult for any one conference to cover the multitude of topics of interest to the highly diverse membership of the Cognitive Science Society, but substantial diversity is assured over the course of successive meetings.
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One problem for the society is that involvement by artificial intelligence researchers has dropped off somewhat over the past two decades, reflecting the trend in AI toward engineering rather than cognitive modeling approaches. On the other hand, involvement by philosophers seems to be increasing, but linguists and neuroscientists attend their own disciplinary meetings.
The journal Cognitive Science has far fewer participants than the conference, since only about fifteen articles appear in it annually. It is, however, not the only interdisciplinary journal in cognitive science, as the following partial list demonstrates: Behavioral and Brain Sciences, Cognition, Computational Linguistics, Mind and Language.
Moreover, in addition to the annual meetings of the Cognitive Science Society, there are other conferences where researchers can pursue questions at the intersection of such fields as linguistics and computation, philosophy and psychology, cognition and neuroscience, and so on. The Society for Philosophy and Psychology and Cognitive Neuroscience Society are two of the organizations that serve to forge links at a more local level than the entire field of cognitive science. In addition, every year there are special-topic conferences on particular aspects of the mind that are geared toward interdisciplinary participation, on topics such as text processing, computer-human interaction, and AI and education.
Conferences are probably the closest analog to intercultural trading zones, as people from various disciplines and countries gather to exchange ideas. One would get, however, a feeble anthropological understanding of trading zones if one concentrated only on the people and places where they meet. Just as the point of economic trading zones is the exchange of goods, so the point of intellectual trading zones is the exchange of ideas, and I have said little so far about the ideas and methods that make interdisciplinary work in cognitive science possible and desirable.
Understanding the interdisciplinary character of cognitive science requires much more than biography and sociology, so I now turn to a discussion of the intellectual content of cognitive science. Ideas For an interdisciplinary field to have an intellectual purpose, it must involve ideas that cut across disciplinary boundaries. For cognitive science, the most important ideas have been mental representation, computational procedures, and the brain as a representational-computational engine.
My aim here is to describe how each of these has helped to make possible trading zones in cognitive zones; fuller accounts of the history and content of these ideas can be found in other sources such as Johnson-Laird , Churchland and Sejnowski , and Thagard The concept of mental representation is ancient, evident in the writings of philosophers such as Plato, Locke, and Kant. But in the early s, especially in American psychological circles, the concept of mind had become suspect, a metaphysical construction incompatible with the positivist and behaviorist prescriptions of the time.
From its beginnings, artificial intelligence was representational, writing programs using computer structures assumed to be analogous to ones that underlie human thought. Cognitive theorizing has postulated various kinds of mental representation in order to explain intelligent behavior, including sentences expressed in logical formalism, rules, concepts, analogs, visual images, and distributed representations in artificial neural networks see Thagard, , for a survey.
Discussion of these representations has been at the center of interdisciplinary debates involving psychologists, AI researchers, philosophers, linguists, neuroscientists, and anthropologists. Although there is by no means general agreement on which kinds of representation are most important for explaining mental capacities, it is striking that the discussion of representation is at the core of interdisciplinary discourse.
Heideggerians and social constructivists who completely reject the concept of mental representation operate only at the fringes of cognitive science. Trading zones do not require complete agreement or a universal vocabulary, but they do require an overlapping conceptual core among the cultures or disciplines that participate in them.
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For cognitive science, the idea of mental representation is a crucial part of that core. Although cognitive science merely revived and enriched the idea of mental representations, it also had from the start a core idea that was much more original. In order to explain intelligent functioning, it is necessary to postulate not only mental representations, but procedures that operate on them to produce performance.
Before computational ideas came along in the s, philosophers and psychologists were limited in the kinds of processes they could discuss, for example association of ideas and logical inference. Moreover, it was not at all evident how such processes could be understand mechanistically, or how the brain could carry them out. By the early s, however, the first computers were in use, and computation was becoming understood both theoretically and practically.
Although Chomsky has never embraced the computational view of mind, since he contends that linguistics need only explain competence and can ignore performance, the view of thinking as analogous to or even as a kind of computation has united many other linguists, most psychologists, some philosophers, and even cognitive neuroscientists who understand the brain as a computational device.
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It is not an exaggeration to see cognitive science as a spin-off from a technological development — the invention of digital computers in the s. In particular, the rapid growth of cognitive psychology in the s and s employed a view of thinking as information processing that heavily employed computational ideas and metaphors.
The major development in cognitive science in the s was the growth of connectionist models using artificial neural networks, and the most striking expansion of the s has been in work on cognitive neuroscience using brain scanning methods discussed in the next section.
Through this work, the computational approach to thinking has been enriched by thinking of the brain as a representational-computational machine and using what is known about the brain to enhance ideas about representation and computation. The result has been a new set of ideas that cross disciplinary boundaries, including distributed representations and parallel processes.
Increasingly, the brain and what is rapidly becoming known about it are furnishing topics for interdisciplinary discourse.
Although concepts involving representation, computation, and the brain are at the center of the cognitive science trading zone, there are other more local concepts that provide intersections for particular pairs of disciplines. For example, psychology, philosophy, and AI share a concern with inference, although philosophy and AI are often concerned more with normative issues of how people and machines should infer than with descriptive psychological issues about how people actually do make inferences.
It would be interesting to compile a complete list of ideas at the intersection of two or more of the six disciplines that constitute cognitive science.
There is no reason to suppose that an interdisciplinary field such as cognitive science should be limited to a fixed set of contributing disciplines. Just as new cultures can arrive to contribute to an anthropological trading zone, so new disciplines can emerge as relevant to an interdisciplinary field. At its inception in the s, cognitive science was mostly a mixture of psychology, artificial intelligence, and linguistics, and only later was the strong relevance of neuroscience, philosophy, and anthropology recognized.
The early emphasis on mental representation led to neglect of matters that have received more attention in recent cognitive science, such as the role of the human body in cognition and the importance of the physical and social environments in which cognition takes place. However, the embodiment and situatedness of cognition do not provide reasons for abandoning the representational-computational theory of mind, only for expanding and supplementing it Thagard, My guess is that the next major addition to the interdisciplinary mix of cognitive science will be molecular biology, as knowledge increases dramatically about the genetic and chemical basis of neurological processes.
Bruer this volume discusses some of the potential interconnections between genetic studies and cognitive science. The ebb and flow of contributions of different disciplines to an interdisciplinary field can not be managed by any central body such as the Cognitive Science Society, but depends on the unpredictable course of theoretical and experimental developments.
The journal Cognitive Science currently lists education as one of the areas of cognitive science, in addition to the six disciplines that I have been discussing. Education is an extremely important area of application of cognitive science, but is not a contributing discipline in itself. But education is primarily a borrower of ideas and methods rather than a disciplinary contributor to understanding of how the mind works. Methods A discipline is constituted not only by its ideas but by its methods. Typically, for example, psychologists run experiments, AI researchers write computer programs, linguists analyze languages, and neuroscientists record brain operations.
An interdisciplinary field requires methods that cross disciplinary boundaries, and there are two such methods that have had the greatest impact on work in cognitive science: computer simulation and brain scanning. I shall briefly describe the nature of these two methods in order to show how the cognitive science trading zone involves not only ideas but also activities of an interdisciplinary nature. When computers began to become available in the s, scientists quickly realized their potential for investigating physical processes.
Even when a physical system has a mathematical description, it is often not possible to work out its behavior in any detail, because the equations that describe it may have no tractable solution.
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However, if programmable equations can be written that approximate its behavior, then running a computer program can provide predictions about behaviors too complex to be worked out by direct mathematical methods. Gallison describes how computer simulations became a standard part of the practice of physics in the s, and today computer simulations are widely used in disciplines as diverse as economics and evolutionary biology.
I have already described how cognitive science pioneers such Newell, Simon, Miller and Minsky recognized in the s the potential for computational simulation of human thought, and such simulations have been at the core of theoretical developments in cognitive science ever since. Computer simulation not only offers cognitive science the benefit of complex calculation found in computer modeling in such disciplines as physics, economics, and biology, it also provides a major theoretical impetus. The structures and procedures in the computer model of mind are hypothesized to be analogous to the mental representations and procedures that underlie human thinking.
As in other disciplines in which computer models are useful, one of the merits of computational models of cognition is that they serve to draw out the unforeseen empirical consequences of cognitive theories and display their limitations. The assessment of cognitive models should address questions such as the following Thagard, : 1. Is the model a genuine instantiation of the theoretical ideas about the structure and growth of knowledge, and is the program a genuine implementation of the model? Breadth of application.
Does the model apply to lots of different examples, not just a few that have been cooked up to make the program work? Does the model scale up to examples that are considerably larger and more complex than the ones to which it has been applied? Qualitative fit. Does the computational model perform the same kinds of tasks that people do in approximately the same way? Quantitative fit. Can the computational model simulate quantitative aspects of psychological experiments, e. Does the computational model simulate representations and processes that are compatible with those found in theoretical accounts and computational models of other kinds of cognition?
Computer simulation is an interdisciplinary method for two reasons. First, computational modeling is not normally part of the training of psychologists, philosophers, neuroscientists, linguists, or anthropologists, and second, it usually draws on ideas about structures and algorithms that are part of the branch of computer science called artificial intelligence. But computer simulation is obviously not just part of computer science and artificial intelligence, since knowledge of psychology, philosophy, language, or neuroscience is crucial for determining what to simulate.
Being Interdisciplinary: Trading Zones in Cognitive Science
The method of computer simulation requires either 1 interdisciplinary collaboration between computer scientists and members of other interdisciplinary fields or 2 the acquisition by individuals from a particular discipline of ideas and skills from the other. A great deal of cognitive modeling has been accomplished by psychologists who have stepped outside the typically empirical orientation of their discipline to acquire computational skills in order to perform computational simulations.
More rare are AI researchers who have acquired sufficient knowledge of psychology or linguistics to produce computational models in these areas, and rarer still are philosophers who have adopted computational modeling as a methodology. Another interdisciplinary method has become important to cognitive science in recent decades.