Reconstructing Physical Symbol Systems
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In this paper, the authors present another view of representation in connectionist networks and respond to statements about ALVINN by both Vera and Simon and Greeno and Moore (1993).About:
This article is published in Cognitive Science.The article was published on 1994-04-01 and is currently open access. It has received 22 citations till now. The article focuses on the topics: Physical symbol system & Symbol.read more
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Principles of Neural Science
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Book
Being There: Putting Brain, Body, and World Together Again
TL;DR: Clark as mentioned in this paper argues that the mental has been treated as a realm that is distinct from the body and the world, and argues that a key to understanding brains is to see them as controllers of embodied activity.
Book
Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution
Mark H. Bickhard,Loren Terveen +1 more
TL;DR: In this paper, the authors present a general critique of the problem of representation and the consequences of encodings in AI and Cognitive Science, and present an alternative approach to representation in the Interactive Model.
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Towards a cognitive robotics
Andrew G. Clark,Rick Grush +1 more
TL;DR: The strategy will be to argue from within a general framework that accepts many of the basic premises of the work (in new robotics and in dynamical systems theory) that motivates such scepticism in the first place.
The Proper Treatment of Symbols in a Connectionist Architecture
Keith J. Holyoak,John E. Hummel +1 more
TL;DR: Ezinative connectionism offers a direct challenge to the PSS hypothesis, thereby transforming the latter from an axiom of cognitive science into a controversial theoretical position, which has been vigorously Regardless of whether models based on distributed representations provide genuine alternatives to physical symbol systems, it is apparent that they have attractive properties as possible algorithmic accounts of cognition.
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Book
Principles of Neural Science
TL;DR: The principles of neural science as mentioned in this paper have been used in neural networks for the purpose of neural network engineering and neural networks have been applied in the field of neural networks, such as:
Journal ArticleDOI
Principles of Neural Science
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Journal ArticleDOI
Connectionism and cognitive architecture: a critical analysis
Jerry A. Fodor,Zenon W. Pylyshyn +1 more
TL;DR: Differences between Connectionist proposals for cognitive architecture and the sorts of models that have traditionally been assumed in cognitive science are explored and the possibility that Connectionism may provide an account of the neural structures in which Classical cognitive architecture is implemented is considered.
Journal ArticleDOI
The symbol grounding problem
TL;DR: In this paper, the problem of grounding symbolic representations in nonsymbolic representations of two kinds, i.e., "iconic representations" and "categorical representations" is addressed.
Journal ArticleDOI
On the proper treatment of connectionism
TL;DR: In this article, a set of hypotheses is formulated for a connectionist approach to cognitive modeling, and these hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models, which are considered are massively parallel numerical computational systems that are a kind of continuous dynamical system.