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Computation and cognition: Toward a foundation for cognitive science
415
ARTIFICIAL INTELLIGENCE
Book Review Z. Pylyshyn, Computation and Cognition: Toward a Foundation for Cognitive Science (MIT Press, Cambridge, MA, 1984); 292 pages, $33.75 (hardcover), $9.95 (paperback) Reviewed by: Nigel Ward
Computer Science Division, University of California, Berkeley, CA 94720, U.S.A. Pylyshyn wrote this book to clarify the "foundational assumptions" of cognitive science. He feels "a need to e x p o s e . . , some stable intuitions which cognitive science researchers share, and which to some extent guide their work." The book is directed to those who share his interest in such questions as "what is the nature of members of the class of cognizers?" including "higher vertebrates and certain computer systems." Pylyshyn aims to address such issues in a "suggestive" not "rigorous" way. The book seems to be directed to people with broad backgrounds in cognitive science and philosophy of mind. It can be summarized as follows: The goal of cognitive science is to "explain" behavior, not merely to describe, simulate, analyze, or understand it. Explanations in terms of neurons and the like are unacceptable; real explanations must refer to the goals and beliefs of the subject. Hence "representations" will play a key role in any account of behavior. In particular, explanations must be represented in a formalism with a "systematic" account of the relation between semantics and symbols. The only available such formalism is symbol structures, with Tarskian semantics. We can say that a machine and a person do the same thing if they follow the same algorithm. Specifically, "two programs can be thought of a s . . . different realizations of the same cognitive process if they can be represented by the same program in some theoretically specified virtual machine." It is hard to pin down the idea of "same algorithm," but the "intuitive notion" is compelling. The notion of algorithm allows us to say that "cognition is a type of computation," not just metaphorically, but as an "empirical hypothesis." The distinction between algorithm and virtual machine means that any phenomenon can be explained in either of two ways. There are explanations
Artificial Intelligence 33 (1987) 415-417 0004-3702/87/$3.50 © 1987, Elsevier Science Publishers B.V. (North-Holland)
416
BOOK REVIEW
which refer to the functional architecture, or special facts about the hardware of the brain; and those which refer to algorithms, or "representations and the processes operating on them." To decide which aspect of mind is elucidated by any particular experiment, we can apply the "cognitive penetrability" test. For example, the fact that we can change someone's response to an alarm by changing his beliefs about the fire drill schedule means that the reaction to loud noises is cognitively penetrable, and hence not ascribable to the nature of the functional architecture. The above considerations aid the analysis of various cognitive science paradigms. First, behaviorism is bad, since it provides no real explanations at all. Second, the realization that perception results in symbol structures constrains theories of perception. One can conclude that any "transducer" which maps physical entities-in-the-world to cognitive entities must have certain specific characteristics. In particular, Gibson's claims about "direct perception" do not meet these constraints, and so need to be presented with caveats. Third, Kosslyn's interpretation of the mental imagery results is questionable. It is not necessary to explain imagery in terms of an analog medium, since the experimental results can be explained in terms of the subject's beliefs about what he would see. Moreover, it is an error to explain things in terms of mental architecture if there is an explanation in terms of knowledge, beliefs, and representations. Previous discussion notwithstanding, perhaps the set of cognitive phenomena is not identical with the set of things explainable in terms of representations and algorithms. For example, learning seems cognitive, but is hard to explain in terms of symbolic encoding. Indeed, cognition-as-defined-here may not be a "natural scientific domain" study. Computation and Cognition is only marginally relevant to AI, largely because Pylyshyn's conception of science is alien in many ways. He thinks it important to "constrain" theorizing. This leads him for example, to criticize the analog-medium model of imagery because it makes the explanation of new results too easy. He considers a science to be a set of principles, rather than a set of questions or a methodology. He states that usefulness is not an important quality in a theory. In general, his discussion of AI and the theory, design, and programming of computers is shallow. It is present not for its own sake, but merely to advance his philosophical arguments. The book contains faults of omission and commission. Pylyshyn fails to mention such relevant issues as: the classic philosophical problems of fixing reference, relating form and content, and isolating knowledge from skills; the recurring doubts on the importance of logic in AI; and the implications of his assertions for research. His claims are often too vague to decide whether they are absurd or tautological, irrelevant or interesting. The book itself is hard to read: the sentences tend to be long and tortuous, many ideas are mentioned but not explained, and the digressions and repetition make it hard to tell where
BOOK REVIEW
417
a train of thought is leading, or to find specific information. Pylyshyn has chosen an important topic, the relation of artificial intelligence to cognitive science, which has not yet been well discussed. Occasionally he does consider interesting points, such as of the difficulty of knowing what people do when you tell them to "imagine." On the whole, however, I cannot recommend Computation and Cognition. Those who do read it should know two things about the book: principles and constraints whose introduction is apparently unmotivated will in fact be used to criticize theories, and some of the material is included to reject or acknowledge criticisms directed at an earlier version of the b o o k )
1Open Peer Commentary on "Cognition and computation", Behavioral and Brain Sciences3(1)
(1980) 133-153.
ARTIFICIAL INTELLIGENCE
Book Review Z. Pylyshyn, Computation and Cognition: Toward a Foundation for Cognitive Science (MIT Press, Cambridge, MA, 1984); 292 pages, $33.75 (hardcover), $9.95 (paperback) Reviewed by: Nigel Ward
Computer Science Division, University of California, Berkeley, CA 94720, U.S.A. Pylyshyn wrote this book to clarify the "foundational assumptions" of cognitive science. He feels "a need to e x p o s e . . , some stable intuitions which cognitive science researchers share, and which to some extent guide their work." The book is directed to those who share his interest in such questions as "what is the nature of members of the class of cognizers?" including "higher vertebrates and certain computer systems." Pylyshyn aims to address such issues in a "suggestive" not "rigorous" way. The book seems to be directed to people with broad backgrounds in cognitive science and philosophy of mind. It can be summarized as follows: The goal of cognitive science is to "explain" behavior, not merely to describe, simulate, analyze, or understand it. Explanations in terms of neurons and the like are unacceptable; real explanations must refer to the goals and beliefs of the subject. Hence "representations" will play a key role in any account of behavior. In particular, explanations must be represented in a formalism with a "systematic" account of the relation between semantics and symbols. The only available such formalism is symbol structures, with Tarskian semantics. We can say that a machine and a person do the same thing if they follow the same algorithm. Specifically, "two programs can be thought of a s . . . different realizations of the same cognitive process if they can be represented by the same program in some theoretically specified virtual machine." It is hard to pin down the idea of "same algorithm," but the "intuitive notion" is compelling. The notion of algorithm allows us to say that "cognition is a type of computation," not just metaphorically, but as an "empirical hypothesis." The distinction between algorithm and virtual machine means that any phenomenon can be explained in either of two ways. There are explanations
Artificial Intelligence 33 (1987) 415-417 0004-3702/87/$3.50 © 1987, Elsevier Science Publishers B.V. (North-Holland)
416
BOOK REVIEW
which refer to the functional architecture, or special facts about the hardware of the brain; and those which refer to algorithms, or "representations and the processes operating on them." To decide which aspect of mind is elucidated by any particular experiment, we can apply the "cognitive penetrability" test. For example, the fact that we can change someone's response to an alarm by changing his beliefs about the fire drill schedule means that the reaction to loud noises is cognitively penetrable, and hence not ascribable to the nature of the functional architecture. The above considerations aid the analysis of various cognitive science paradigms. First, behaviorism is bad, since it provides no real explanations at all. Second, the realization that perception results in symbol structures constrains theories of perception. One can conclude that any "transducer" which maps physical entities-in-the-world to cognitive entities must have certain specific characteristics. In particular, Gibson's claims about "direct perception" do not meet these constraints, and so need to be presented with caveats. Third, Kosslyn's interpretation of the mental imagery results is questionable. It is not necessary to explain imagery in terms of an analog medium, since the experimental results can be explained in terms of the subject's beliefs about what he would see. Moreover, it is an error to explain things in terms of mental architecture if there is an explanation in terms of knowledge, beliefs, and representations. Previous discussion notwithstanding, perhaps the set of cognitive phenomena is not identical with the set of things explainable in terms of representations and algorithms. For example, learning seems cognitive, but is hard to explain in terms of symbolic encoding. Indeed, cognition-as-defined-here may not be a "natural scientific domain" study. Computation and Cognition is only marginally relevant to AI, largely because Pylyshyn's conception of science is alien in many ways. He thinks it important to "constrain" theorizing. This leads him for example, to criticize the analog-medium model of imagery because it makes the explanation of new results too easy. He considers a science to be a set of principles, rather than a set of questions or a methodology. He states that usefulness is not an important quality in a theory. In general, his discussion of AI and the theory, design, and programming of computers is shallow. It is present not for its own sake, but merely to advance his philosophical arguments. The book contains faults of omission and commission. Pylyshyn fails to mention such relevant issues as: the classic philosophical problems of fixing reference, relating form and content, and isolating knowledge from skills; the recurring doubts on the importance of logic in AI; and the implications of his assertions for research. His claims are often too vague to decide whether they are absurd or tautological, irrelevant or interesting. The book itself is hard to read: the sentences tend to be long and tortuous, many ideas are mentioned but not explained, and the digressions and repetition make it hard to tell where
BOOK REVIEW
417
a train of thought is leading, or to find specific information. Pylyshyn has chosen an important topic, the relation of artificial intelligence to cognitive science, which has not yet been well discussed. Occasionally he does consider interesting points, such as of the difficulty of knowing what people do when you tell them to "imagine." On the whole, however, I cannot recommend Computation and Cognition. Those who do read it should know two things about the book: principles and constraints whose introduction is apparently unmotivated will in fact be used to criticize theories, and some of the material is included to reject or acknowledge criticisms directed at an earlier version of the b o o k )
1Open Peer Commentary on "Cognition and computation", Behavioral and Brain Sciences3(1)
(1980) 133-153.