The paper explores the distinction between two doctrines, both of which inform theory construction in much of modern cognitive psychology: the representational theory of mind and the computational theory of mind. According to the former, propositional attitudes are to be construed as relations that organisms bear to mental representations. According to the latter, mental processes have access only to formal (nonsemantic) properties of the mental representations over which they are defined.The following claims are defended: (1) That the traditional dispute between (...) “rational” and “naturalistic” psychology is plausibly viewed as an argument about the status of the computational theory of mind. Rational psychologists accept a formality condition on the specification of mental processes; naturalists do not. (2) That to accept the formality condition is to endorse a version of methodological solipsism. (3) That the acceptance of some such condition is warranted, at least for that part of psychology which concerns itself with theories of the mental causation of behavior. This is because: (4) such theories require nontransparent taxonomies of mental states; and (5) nontransparent taxonomies individuate mental states without reference to their semantic properties. Equivalently, (6) nontransparent taxonomies respect the way that the organism represents the object of its propositional attitudes toitself, and it is this representation which functions in the causation of behavior.The final section of the paper considers the prospect for a naturalistic psychology: one which defines its generalizations over relations between mental representations and their environmental causes, thus seeking to account for the semantic properties of propositional attitudes. Two related arguments are proposed, both leading to the conclusion that no such research strategy is likely to prove fruitful. (shrink)

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...) we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

A theory of analogy must describe how the meaning of an analogy is derived from the meanings of its parts. In the structure‐mapping theory, the interpretation rules are characterized as implicit rules for mapping knowledge about a base domain into a target domain. Two important features of the theory are (a) the rules depend only on syntactic properties of the knowledge representation, and not on the specific content of the domains; and (b) the theoretical framework allows analogies to be distinguished (...) cleanly from literal similarity statements, applications of abstractions, and other kinds of comparisons.Two mapping principles are described: (a) Relations between objects, rather than attributes of objects, are mapped from base to target; and (b) The particular relations mapped are determined by systematicity, as defined by the existence of higher‐order relations. (shrink)

This bold and brilliant book asks the ultimate question of the life sciences: How did the human mind acquire its incomparable power? In seeking the answer, Merlin Donald traces the evolution of human culture and cognition from primitive apes to the era of artificial intelligence, and presents an original theory of how the human mind evolved from its presymbolic form. In the emergence of modern human culture, Donald proposes, there were three radical transitions. During the first, our bipedal but still (...) apelike ancestors acquired "mimetic" skill—the ability to represent knowledge through voluntary motor acts—which made Homo erectus successful for over a million years. The second transition—to "mythic" culture —coincided with the development of spoken language. Speech allowed the large-brained Homo sapiens to evolve a complex preliterate culture that survives in many parts of the world today. In the third transition, when humans constructed elaborate symbolic systems ranging from cuneiforms, hieroglyphics, and ideograms to alphabetic languages and mathematics, human biological memory became an inadequate vehicle for storing and processing our collective knowledge. The modern mind is thus a hybrid structure built from vestiges of earlier biological stages as well as new external symbolic memory devices that have radically altered its organization. According to Donald, we are symbol-using creatures, more complex than any that went before us, and we may have not yet witnessed the final modular arrangement of the human mind. (shrink)

Anydomainofscientificresearchhasitssustainingorthodoxy. Thatis, research on a problem, whether in astronomy, physics, or biology, is con- ducted against a backdrop of broadly shared assumptions. It is these as- sumptionsthatguideinquiryandprovidethecanonofwhatisreasonable-- of what "makes sense." And it is these shared assumptions that constitute a framework for the interpretation of research results. Research on the problem of how we see is likewise sustained by broadly shared assump- tions, where the current orthodoxy embraces the very general idea that the business of the visual system is to (...) create a detailed replica of the visual world, and that it accomplishes its business via hierarchical organization and by operatingessentiallyindependently of other sensorymodalitiesas well as independently of previous learning, goals, motor planning, and motor execution. (shrink)

Elements of Episodic Memory is a classic text in the psychology literature. It had a significant influence on research in the area has been much sought after in recent years. Finally, it has now been made available again with this reissue, the text unchanged from the original.

Closely related to this is Mr. Bruner's "evolutionary instrumentalism," his conception of instruction as the means of transmitting the tools and skills of a ...

This revised edition includes a new Preface outlining developments in Relevance Theory since 1986, discussing the more serious criticisms of the theory, and ...

Language and Problems of Knowledge is sixteenth in the series Current Studies in Linguistics, edited by Jay Keyser.

A more extensive discussion of certain of the more technical notions appears in my paper "On Binding" (Chomsky,; henceforth, OB). ...

Introduction: Something on the State of the Art 1 I. Functionalism and Realism 1. Operationalism and Ordinary Language 35 2. The Appeal to Tacit Knowledge in Psychological Explanations 63 3. What Psychological States are Not 79 4. Three Cheers for Propositional Attitudes 100 II. Reduction and Unity of Science 5. Special Sciences 127 6. Computation and Reduction 146 III. Intensionality and Mental Representation 7. Propositional Attitudes 177 8. Tom Swift and His Procedural Grandmother 204 9. Methodological Solipsism Considered as a (...) Research Strategy in Cognitive Psychology 225 IV. Nativism 10. The Present Status of the Innateness Controversy 257 Notes 317. (shrink)

One of the cornerstone books of Western philosophy, Critique of Pure Reason is Kant's seminal treatise, where he seeks to define the nature of reason itself and builds his own unique system of philosophical thought with an approach known as transcendental idealism. He argues that human knowledge is limited by the capacity for perception and attempts a logical designation of two varieties of knowledge: a posteriori, the knowledge acquired through experience; and a priori, knowledge not derived through experience. This accurate (...) translation by J. M. D. Meiklejohn offers a simple and direct rendering of Kant's work that is suitable for readers at all levels. (shrink)

This book brings together Wollheim's broad and abiding concerns to illuminate human thought at its furthest reaches of introspection and expression.

In this book, Newell makes the case for unified theories by setting forth a candidate.

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...) derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

The author takes the reader on a tour that covers such topics as computers, evolution, Descartes, Schrodinger, and the nature of perception, language, and invididuality. He argues that biology provides the key to understanding the brain. Underlying his argument is the evolutionary view that the mind arose at a definite time in history. This book ponders connections between psychology and physics, medicine, philosophy, and more. Frequently contentious, Edelman attacks cognitive and behavioral approaches, which leave biology out of the picture, as (...) well as the currently fashionable view of the brain as a computer. (shrink)

A benchmark volume for an emerging field where mental disorders serve as the springboard for philosophical insights.

The Modularity of Mind proposes an alternative to the or view of cognitive architecture that has dominated several decades of cognitive science. Whereas interactionism stresses the continuity of perceptual and cognitive processes, modularity theory argues for their distinctness. It is argued, in particular, that the apparent plausibility of New Look theorizing derives from the failure to distinguish between the (correct) claim that perceptual processes are inferential and the (dubious) claim that they are unencapsidated, that is, that they are arbitrarily sensitive (...) to the organism's beliefs and desires. In fact, according to modularity theory, perceptual processes are computationally isolated from much of the background knowledge to which cognitive processes have access. The postulation of autonomous, domain-specific psychological mechanisms underlying perceptual integration connects modularity theory with the tradition of faculty psychology, in particular, with the work of Franz Joseph Call. Some of these historical affinities, and some of the relations between faculty psychology and Cartesianism, are discussed in the book. (shrink)

Most biologists and some cognitive scientists have independently reached the conclusion that there is no such thing as learning in the traditional “instructive‘ sense. This is, admittedly, a somewhat extreme thesis, but I defend it herein the light of data and theories jointly extracted from biology, especially from evolutionary theory and immunology, and from modern generative grammar. I also point out that the general demise of learning is uncontroversial in the biological sciences, while a similar consensus has not yet been (...) reached in psychology and in linguistics at large. Since many arguments presently offered in defense of learning and in defense of “general intelligence‘ are often based on a distorted picture of human biological evolution, I devote some sections of this paper to a critique of “adaptationism,‘ providing also a sketch of a better evolutionary theory. Moreover, since certain standard arguments presented today as “knock-down‘ in psychology, in linguistics and in artificial intelligence are a perfect replica of those once voiced by biologists in favor of instruction and against selection, I capitalize on these errors of the past to draw some lessons for the present and for the future. (shrink)

Parallel distributed processing is transforming the field of cognitive science. Microcognition provides a clear, readable guide to this emerging paradigm from a cognitive philosopher's point of view. It explains and explores the biological basis of PDP, its psychological importance, and its philosophical relevance.

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

As Ruben notes, the macrostrategy can allow that the distinction may also be drawn at some micro level, but it insists that descent to the micro level is ...

This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...) e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation. (shrink)

A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for (...) certain types of perception, learning, and control, or they make those processes computationally simpler. Once a creature has concepts its capacities are vastly multiplied. (shrink)

The concept of representation has become almost inextricably bound to the concept of symbol systems. the concepts is nowhere more prevalent than in descriptions of "internal representations." These representations are thought to occur in an internal symbol system that allows the brain to store and use information. In this paper we explore a different approach to understanding psychological processes, one that retains a commitment to representations and computations but that is not based on the idea that information must be stored (...) and manipulated in symbol systems. In particular, we suggest that the notion of a symbol system as currently understood construes psychological processes in terms of a specific type of computational system, in which a control function "reads," "interprets," and manipulates discrete entities called "symbols." We argue that other types of computational systems may provide a more appropriate characterization of psychological processes. One implication of our argument is the need to consider the constraints placed on computational theories in psychology by the nature of the computing device itself, the human brain. Perhaps surprisingly, this implication leads us to the conclusion that a "functionalist" conception of psychological processes (discussed below) does not entail that physiology is irrelevant to psychology, as has been maintained by prominent adherents of the symbol-systems approach. (shrink)