This is an introductory survey to the philosophy of science suitable for beginners and nonspecialists. Its point of departure is the question: why should we believe what science tells us about the world? In this attempt to justify the claims of science the book treats such topics as observation data, confirmation of theories, and the explanation of phenomena. The writing is clear and concrete with detailed examples drawn from contemporary science: solar neutrinos, the gravitational bending of light, and the creation/evolution (...) debate, for example. What emerges is a view of science in which observation relies on theory to give it meaning and credibility, while theory relies on observation for its motivation and validation. (shrink)

This paper explores a new reason for preferring a model-theoretic approach to understanding the nature of scientific theories. Identifying the models in philosophers' model-theoretic accounts of theories with the concepts in cognitive scientists' accounts of categorization suggests a structure to families of models far richer than has commonly been assumed. Using classical mechanics as an example, it is argued that families of models may be "mapped" as an array with "horizontal" graded structures, multiply hierarchical "vertical" structures, and local "radial" structures. (...) These structures promise important implications for how scientific theories are learned and used in actual scientific practice. (shrink)

The third edition of a work that defines the field of cognitive neuroscience, with extensive new material including new chapters and new contributors.

This long-awaited work by prominent Harvard psychologist Stephen Kosslyn integrates a twenty-year research program on the nature of high-level vision and mental ...

A Neurocomputationial Perspective illustrates the fertility of the concepts and data drawn from the study of the brain and of artificial networks that model the...

On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so (...) defined is internal to this concept, so it becomes a hypothesis that this notion of symbols includes the symbols that we humans use every day of our lives. In this paper we attempt systematically, but plainly, to lay out the nature of physical symbol systems. Such a review is in ways familiar, but not thereby useless. Restatement of fundamentals is an important exercise. (shrink)

For the uninitiated, there are two major tendencies in the modeling of human cognition. The older, tradtional school believes, in essence, that full human cognition can be modeled by dividing the world up into distinct entities -- called __symbol s__-- such as “dog”, “cat”, “run”, “bite”, “happy”, “tumbleweed”, and so on, and then manipulating this vast set of symbols by a very complex and very subtle set of rules. The opposing school claims that this system, while it might be good (...) at concluding that Paris is the capital of France or that there must be blood flowing in the left-rear leg of a cow, can never capture the full measure -- indeed, the essence -- of human cognition. For them, the essential features of cognition emerge from the combined effects of myriad, tiny actions far below the surface of consciousness. This is the camp to which Paul Churchland belongs. (shrink)

In Connectionism and the Philosophy of Psychology, Horgan and Tienson articulate and defend a new view of cognition.

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.

Choice Outstanding Academic Title, 1996. In hundreds of articles by experts from around the world, and in overviews and "road maps" prepared by the editor, The Handbook of Brain Theory and Neural Networks charts the immense progress made in recent years in many specific areas related to great questions: How does the brain work? How can we build intelligent machines? While many books discuss limited aspects of one subfield or another of brain theory and neural networks, the Handbook covers the (...) entire sweep of topics—from detailed models of single neurons, analyses of a wide variety of biological neural networks, and connectionist studies of psychology and language, to mathematical analyses of a variety of abstract neural networks, and technological applications of adaptive, artificial neural networks. Expository material makes the book accessible to readers with varied backgrounds while still offering a clear view of the recent, specialized research on specific topics. (shrink)

By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics (...) as the nature of concepts, hypothesis formation, analogy, and theory justification. (shrink)

The first comprehensive presentation of the dynamical approach to cognition. It contains a representative sampling of original, current research on topics such as perception, motor control, speech and language, decision making, and development.

All of the chapters have been written especially for the book by the leading scholars in the field.Michael I. Posner is Professor of Psychology at the ...