Ranging across both standard philosophical territory and the landscape of cutting-edge cognitive science, Mindware: An Introduction to the Philosophy of Cognitive Science, Second Edition, is a vivid and engaging introduction to key issues, research, and opportunities in the field.Starting with the vision of mindware as software and debates between realists, instrumentalists, and eliminativists, Andy Clark takes students on a no-holds-barred journey through connectionism, dynamical systems, and real-world robotics before moving on to the frontiers of cognitive technologies, enactivism, predictive coding, and (...) the extended mind. Throughout, he highlights challenging issues in an effort to engage students in active debate. Each chapter opens with a brief sketch of a major research tradition or perspective, followed by concise critical discussions dealing with key topics and problems. (shrink)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

Mindware: An Introduction to the Philosophy of Cognitive Science invites readers to join in up-to-the-minute conceptual discussions of the fundamental issues, problems, and opportunities in cognitive science. Written by one of the most renowned scholars in the field, this vivid and engaging introductory text relates the story of the search for a cognitive scientific understanding of mind. This search is presented as a no-holds-barred journey from early work in artificial intelligence, through connectionist (artificial neural network) counter-visions, and on to neuroscience, (...) artificial life, dynamics, and robotics. The journey ends with some wide-ranging and provocative speculation about the complex coadaptive dance between mind, culture, and technology. Each chapter opens with a brief sketch of a major research tradition or perspective, followed by short yet substantial critical discussions dealing with key topics and problems. Ranging across both standard philosophical territory and the landscape of cutting-edge cognitive science, Clark highlights challenging issues in an effort to engage readers in active debate. Topics covered include mental causation; machine intelligence; the nature and status of folk psychology; the hardware/software distinction; emergence; relations between life and mind; the nature of perception, cognition, and action; and the continuity (or otherwise) of high-level human intelligence with other forms of adaptive response. Numerous illustrations, text boxes, and extensive suggestions for further reading enhance the text's utility. Helpful appendices provide background information on dualism, behaviorism, identity theory, consciousness, and more. An exceptional text for introductory and more advanced courses in cognitive science and the philosophy of mind, Mindware is also essential reading for anyone interested in these fascinating and ever-changing fields. (shrink)

Eliminative materialism is the thesis that our common-sense conception of psychological phenomena constitutes a radically false theory, a theory so fundamentally defective that both the principles and the ontology of that theory will eventually be displaced, rather than smoothly reduced, by completed neuroscience. Our mutual understanding and even our introspection may then be reconstituted within the conceptual framework of completed neuroscience, a theory we may expect to be more powerful by far than the common-sense psychology it displaces, and more substantially (...) integrated within physical science generally. My purpose in this paper is to explore these projections, especially as they bear on (1) the principal elements of common-sense psychology: the propositional attitudes (beliefs, desires, etc.), and (2) the conception of rationality in which these elements figure. (shrink)

INTRODUCTION: TWO KINDS OF RLDUCTIONISM The man who laughs is the one who has not yet heard the terrible news. BERTHOLD BRECHT I propose, in this book, ...

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

A great deal of philosophy of mind in the modern era has been driven by an intense aversion to Cartesian dualism. In the 1950s, materialists claimed to have succeeded once and for all in exorcising the Cartesian ghost by identifying the mind with the brain. In subsequent decades, cognitive science put scientific meat on this metaphysical skeleton by explicating mental processes as digital computation implemented in the brain's hardware.

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...) and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates (...) category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates (...) category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)

This collection of 17 essays by the author offers a comprehensive theory of mind, encompassing traditional issues of consciousness and free will.

Embodied agents use bodily actions and environmental interventions to make the world a better place to think in. Where does language fit into this emerging picture of the embodied, ecologically efficient agent? One useful way to approach this question is to consider language itself as a cognition-enhancing animal-built structure. To take this perspective is to view language as a kind of self-constructed cognitive niche: a persisting though never stationary material scaffolding whose critical role in promoting thought and reason remains surprisingly (...) ill-understood. It is the very materiality of this linguistic scaffolding, I suggest, that is responsible for some key benefits. By materializing thought in words, we create structures that are themselves proper objects of perception, manipulation, and thought. (shrink)

The idea that the language we speak influences the way we think has evoked perennial fascination and intense controversy. According to the strong version of this hypothesis, called the Sapir-Whorf hypothesis after the American linguists who propounded it, languages vary in their semantic partitioning of the world, and the structure of one’s language influences how one understands the world. Thus speakers of different languages perceive the world differently. Although the last two decades have been marked by extreme skepticism concerning the (...) possible effects of language on thought, recent theoretical and methodological advances in cognitive science have given the question new life. Research in linguistics and linguistic anthropology has revealed striking differences in cross-linguistic semantic patterns, and cognitive psychology has developed subtle techniques for studying how people represent and remember experience. It is now possible to test predictions about how a given language influences the thinking of its speakers. Language in Mind includes contributions from both skeptics and believers and from a range of fields. It contains work in cognitive psychology, cognitive development, linguistics, anthropology, and animal cognition. The topics discussed include space, number, motion, gender, theory of mind, thematic roles, and the ontological distinction between objects and substances. I Introduction 1 Whither Whorf 3 Dedre Gentner and Susan Goldin-Meadow II Position Statements 15 1 Languages and Representations 17 Eve V. Clark 2 Language and Mind: Let’s Get the Issues Straight! 25 Stephen C. Levinson 3 The Key Is Social Cognition 47 Michael Tomasello III Language as Lens: Does the Language We Acquire Influence How We See the World? 59 4 Sex, Syntax, and Semantics 61 Lera Boroditsky, Lauren A. Schmidt, and Webb Phillips 5 Speaking versus Thinking about Objects and Actions 81 Barbara C. Malt, Steven A. Sloman, and Silvia P. Gennari 6 The Effects of Spatial Language on Spatial Representation: Setting Some Boundaries 113 Edward Munnich and Barbara Landau 7 Language and Thought Online: Cognitive Consequences of Linguistic Relativity 157 Dan I. Slobin IV Language as Tool Kit: Does the Language We Acquire Augment Our Capacity for Higher-Order Representation and Reasoning? 193 8 Why We’re So Smart 195 Dedre Gentner 9 Does Language Help Animals Think? 237 Stan A. Kuczaj, II, and Jennifer L. Hendry 10 What Makes Us Smart? Core Knowledge and Natural Language 277 Elizabeth S. Spelke 11 Conceptual and Linguistic Factors in Inductive Projection: How Do Young Children Recognize Commonalities between Animals and Plants? 313 Kayoko Inagaki and Giyoo Hatano 12 Language for Thought: Coming to Understand False Beliefs 335 Jill G. de Villiers and Peter A. de Villiers V Language as Category Maker: Does the Language We Acquire Influence Where We Make Our Category Distinctions? 385 13 Space under Construction: Language-Specific Spatial Categorization in First Language Acquisition 387 Melissa Bowerman and Soonja Choi 14 Reevaluating Linguistic Relativity: Language-Specific Categories and the Role of Universal Ontological Knowledge in the Construal of Individuation 429 Mutsumi Imai and Reiko Mazuka 15 Interaction of Language Type and Referent Type in the Development of Nonverbal Classification Preferences 465 John A. Lucy and Suzanne Gaskins 16 Thought before Language: Do We Think Ergative? 493 Susan Goldin-Meadow. (shrink)

Part I: The Life of Cognitive Science:. William Bechtel, Adele Abrahamsen, and George Graham. Part II: Areas of Study in Cognitive Science:. 1. Analogy: Dedre Gentner. 2. Animal Cognition: Herbert L. Roitblat. 3. Attention: A.H.C. Van Der Heijden. 4. Brain Mapping: Jennifer Mundale. 5. Cognitive Anthropology: Charles W. Nuckolls. 6. Cognitive and Linguistic Development: Adele Abrahamsen. 7. Conceptual Change: Nancy J. Nersessian. 8. Conceptual Organization: Douglas Medin and Sandra R. Waxman. 9. Consciousness: Owen Flanagan. 10. Decision Making: J. Frank Yates (...) and Paul A. Estin. 11. Emotions: Paul E. Griffiths. 12. Imagery and Spatial Representation: Rita E. Anderson. 13. Language Evolution and Neuromechanisms: Terrence W. Deacon. 14. Language Processing: Kathryn Bock and Susan M. Garnsey. 15. Linguistics Theory: D. Terence Langendoen. 16. Machine Learning: Paul Thagard. 17. Memory: Henry L. Roediger III and Lyn M. Goff. 18. Perception: Cees Van Leeuwen. 19. Perception: Color: Austen Clark. 20. Problem Solving: Kevin Dunbar. 21. Reasoning: Lance J. Rips. 22. Social Cognition: Alan J. Lambert and Alison L. Chasteen. 23. Unconscious Intelligence: Rhianon Allen and Arthur S. Reber. 24. Understanding Texts: Art Graesser and Pam Tipping. 25. Word Meaning: Barbara C. Malt. Part III: Methodologies of Cognitive Science:. 26. Artificial Intelligence: Ron Sun. 27. Behavioral Experimentation: Alexander Pollatsek and Keith Rayner. 28. Cognitive Ethology: Marc Bekoff. 29. Deficits and Pathologies: Christopher D. Frith. 30. Ethnomethodology: Barry Saferstein. 31. Functional Analysis: Brian Macwhinney. 32. Neuroimaging: Randy L. Buckner and Steven E. Petersen. 33. Protocal Analysis: K. Anders Ericsson. 34. Single Neuron Electrophysiology: B. E. Stein, M.T. Wallace, and T.R. Stanford. 35. Structural Analysis: Robert Frank. Part IV: Stances in Cognitive Science:. 36. Case-based Reasoning: David B. Leake. 37. Cognitive Linguistics: Michael Tomasello. 38. Connectionism, Artificial Life, and Dynamical Systems: Jeffrey L. Elman. 39. Embodied, Situated, and Distributed Cognition: Andy Clark. 40. Mediated Action: James V. Wertsch. 41. Neurobiological Modeling: P. Read Montague and Peter Dayan. 42. Production Systems: Christian D. Schunn and David Klahr. Part V: Controversies in Cognitive Science:. 43. The Binding Problem: Valerie Gray Hardcastle. 44. Heuristics and Satisficing: Robert C. Richardson. 45. Innate Knowledge: Barbara Landau. 46. Innateness and Emergentism: Elizabeth Bates, Jeffrey L. Elman, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi, and Kim Plunkett. 47. Intentionality: Gilbert Harman. 48. Levels of Explanation and Cognition Architectures: Robert N. McCauley. 49. Modularity: Irene Appelbaum. 50. Representation and Computation: Robert S. Stufflebeam. 51. Representations: Dorrit Billman. 52. Rules: Terence Horgan and John Tienson. 53. Stage Theories Refuted: Donald G. Mackay. Part VI: Cognitive Science in the Real World:. 54. Education: John T. Bruer. 55. Ethics: Mark L. Johnson. 56. Everyday Life Environments: Alex Kirlik. 57. Institutions and Economics: Douglass C. North. 58. Legal Reasoning: Edwina L. Rissland. 59. Mental Retardation: Norman W. Bray, Kevin D. Reilly, Lisa F. Huffman, Lisa A. Grupe, Mark F. Villa, Kathryn L. Fletcher, and Vivek Anumolu. 60. Science: William F. Brewer and Punyashloke Mishra. Selective Biographies of Major Contributors to Cognitive Science: William Bechtel and Tadeusz Zawidzki. (shrink)

This volume is the direct result of a conference in which a number of leading researchers from the fields of artificial intelligence and biology gathered to ...

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.

In Being There, Andy Clark weaves these several threads into a pleasing whole and goes on to address foundational questions concerning the new tools and..

In treating cognition as problem solving, Andy Clark suggests, we may often abstract too far from the very body and world in which our brains evolved to guide...

What is the place of language in human cognition? Do we sometimes think in natural language? Or is language for purposes of interpersonal communication only? Although these questions have been much debated in the past, they have almost dropped from sight in recent decades amongst those interested in the cognitive sciences. Language and Thought is intended to persuade such people to think again. It brings together essays by a distinguished interdisciplinary team of philosophers and psychologists, who discuss various ways in (...) which language may be implicated in human cognition. The editors have provided an introduction which lays out the basic terms and history of the debate, and a consolidated bibliography which will provide a valuable reference resource for all those interested in this area. The volume will be of great interest to all researchers and students interested in language and its place in cognition. (shrink)

Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low­level cognitive phenomena like sensory­motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high­level human cognition. In this paper we argue that (...) a promising way to do that is to merge current ideas and methods of 'embodied cognition' with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan Cognitive Robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high­level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life. (shrink)

Of course, words aren’t magic. Neither are sextants, compasses, maps, slide rules and all the other paraphenelia which have accreted around the basic biological brains of homo sapiens. In the case of these other tools and props, however, it is transparently clear that they function so as to either carry out or to facilitate computational operations important to various human projects. The slide rule transforms complex mathematical problems (ones that would baffle or tax the unaided subject) into simple tasks of (...) perceptual recognition. The map provides geographical information in a format well-suited to aid complex planning and strategic military operations. The compass gathers and displays a kind of information that (most) unaided human subjects do not seem to command. These various tools and props thus act to generate information, or to store it, or to transform it, or some combination of the three. In so doing, they impact our individual and collective problem- solving capacities in much the same dramatic ways as various software packages impact the performance of a simple pc. (shrink)

This chapter takes up, and sketches an answer to, the main challenge facing massively modular theories of the architecture of the human mind. This is to account for the distinctively flexible, non-domain-specific, character of much human thinking. I shall show how the appearance of a modular language faculty within an evolving modular architecture might have led to these distinctive features of human thinking with only minor further additions and non-domain-specific adaptations.