In _Reconstructing the Cognitive World_, Michael Wheeler argues that we should turn away from the generically Cartesian philosophical foundations of much contemporary cognitive science research and proposes instead a Heideggerian approach. Wheeler begins with an interpretation of Descartes. He defines Cartesian psychology as a conceptual framework of explanatory principles and shows how each of these principles is part of the deep assumptions of orthodox cognitive science. Wheeler then turns to Heidegger's radically non-Cartesian account of everyday cognition, which, he argues, can (...) be used to articulate the philosophical foundations of a genuinely non-Cartesian cognitive science. Finding that Heidegger's critique of Cartesian thinking falls short, even when supported by Hubert Dreyfus's influential critique of orthodox artificial intelligence, Wheeler suggests a new Heideggerian approach. He points to recent research in "embodied-embedded" cognitive science and proposes a Heideggerian framework to identify, amplify, and clarify the underlying philosophical foundations of this new work. He focuses much of his investigation on recent work in artificial intelligence-oriented robotics, discussing, among other topics, the nature and status of representational explanation, and whether cognition is computation rather than a noncomputational phenomenon best described in the language of dynamical systems theory. Wheeler's argument draws on analytic philosophy, continental philosophy, and empirical work to "reconstruct" the philosophical foundations of cognitive science in a time of a fundamental shift away from a generically Cartesian approach. His analysis demonstrates that Heideggerian continental philosophy and naturalistic cognitive science need not be mutually exclusive and shows further that a Heideggerian framework can act as the "conceptual glue" for new work in cognitive science. (shrink)

"This book presents recent research efforts in Artificial Intelligence about building artificial systems capable of performing cognitive tasks. A fundamental issue addressed in this book is if these cognitive processes can have any meaningfulness to the artificial system being built"--Provided by publisher.

This volume is part one of a two-volume set. Volume I: From the Puritans through Transcendentalism. Volume I: From the St. Louis Hegelians through C. I. Lewis.

This book is the first to provide a comprehensive survey of the computational models and methodologies used for studying the evolution and origin of language and communication. Comprising contributions from the most influential figures in the field, it presents and summarises the state-of-the-art in computational approaches to language evolution, and highlights new lines of development. Essential reading for researchers and students in the fields of evolutionary and adaptive systems, language evolution modelling and linguistics, it will also be of interest to (...) researchers working on applications of neural networks to language problems. Furthermore, due to the fact that language evolution models use multi-agent methodologies, it will also be of great interest to computer scientists working on multi-agent systems, robotics and internet agents. (shrink)

Charles Sanders Peirce has been characterized as the greatest American philosophic genius. He is the creator of pragmatism and one of the founders of modern logic. James, Royce, Schroder, and Dewey have acknowledged their great indebtedness to him. A laboratory scientist, he made notable contributions to geodesy, astronomy, psychology, induction, probability, and scientific method. He introduced into modern philosophy the doctrine of scholastic realism, developed the concepts of chance, continuity, and objective law, and showed the philosophical significance of the theory (...) of signs and mathematical logic. The present series is the first published edition of his systematic works. (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.

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..

This highly acclaimed book is a major contribution to the philosophy of language as well as a systematic interpretation of Frege, indisputably the father of ...

In this book, T. L. Short corrects widespread misconceptions of Peirce's theory of signs and demonstrates its relevance to contemporary analytic philosophy of language, mind and science. Peirce's theory of mind, naturalistic but nonreductive, bears on debates of Fodor and Millikan, among others. His theory of inquiry avoids foundationalism and subjectivism, while his account of reference anticipated views of Kripke and Putnam. Peirce's realism falls between 'internal' and 'metaphysical' realism and is more satisfactory than either. His pragmatism is not verificationism; (...) rather, it identifies meaning with potential growth of knowledge. Short distinguishes Peirce's mature theory of signs from his better-known but paradoxical early theory. He develops the mature theory systematically on the basis of Peirce's phenomenological categories and concept of final causation. The latter is distinguished from recent and similar views, such as Brandon's, and is shown to be grounded in forms of explanation adopted in modern science. (shrink)

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.

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...

This volume represents an important contribution to Peirce’s work in mathematics and formal logic. An internationally recognized group of scholars explores and extends understandings of Peirce’s most advanced work. The stimulating depth and originality of Peirce’s thought and the continuing relevance of his ideas are brought out by this major book.

This is a copy of the Proceedings version of a paper presented at the Workshop on Computational Intelligence and Semiotics, organized by João Queiroz and Ricardo Gudwin, held at Itaú Cultural, São Paulo, Brazil, on 8-9 October, 2002. (This version contains material not actually delivered at the conference.) Queiroz and Gudwin will be releasing the Proceedings volume on a..

Using hypersets as an analytic tool, we compare traditionally Gibsonian (Chemero 2003; Turvey 1992) and representationalist (Sahin et al. this issue) understandings of the notion ‘affordance’. We show that representationalist understandings are incompatible with direct perception and erect barriers between animal and environment. They are, therefore, scarcely recognizable as understandings of ‘affordance’. In contrast, Gibsonian understandings are shown to treat animal-environment systems as unified complex systems and to be compatible with direct perception. We discuss the fruitful connections between Gibsonian affordances (...) and dynamical systems explanation in the behavioral sciences and point to prior fruitful application of Gibsonian affordances in robotics. We conclude that it is unnecessary to re-imagine affordances as representations in order to make them useful for researchers in robotics. (shrink)

Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be grounded (...) in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

The evolutionary emergence of biological processes in organisms with inner, qualitative aspects has not been explained in any sufficient way by neurobiology, nor by the traditional neo-Darwinian paradigm — natural selection would appear to work just as well on insentient zombies (with the right behavioral input-output relations) as on real sentient animals. In consciousness studies one talks about the ‘hard problem’ of qualia. In this paper I sketch a set of principles about sign action, causality and emergent evolution. On the (...) basis of these principles, I characterize a concept of cause that would allow for a naturalistic explanation of the origin of consciousness. The suggested account of causation also turns the ‘hard problem’ of qualia into the easier problem of relating experimental biology to experiential biology. (shrink)

Cognitive science has been dominated by the computational conception that cogniton is computation across representations. To the extent to which cognition is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from ther interpretation by human users. Strictly speaking, computers as such–apart from human users–are not only incapable (...) of cognition but even incapable of computation, properly construed. If we want to understand the nature of thought, we are going to have to study thinking, not computing, because they are not the same thing. (shrink)