Symbolic and Connectionist Paradigms

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Anomalous Combinations
artificial intelligence theory
boltzmann
Boltzmann Machine
Category=JMR
cognitive science interdisciplinary research
Connectionist Models
Connectionist Systems
Deterministic Parsing
Distributed Connectionist Networks
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
hidden
Hidden Layer
Hidden Layer Activations
Hidden Unit
Hidden Unit Activations
hybrid computational models
interactive
Interactive Activation Model
layer
Localist Connectionist
Localist Connectionist Model
Localist Connectionist Networks
machine
Marker Passing Systems
mental representation systems
models
neural network modeling
output
Physical Symbol System Hypothesis
Plural Morpheme
Propositional Attitude
reductionism in cognition
Rule Templates
Short Term Sequential Memory
Simple Recurrent Network
subsymbolic processing
Symbolic Models
systems
turing
Turing Machine
unit
Variable Bindings
Vice Versa

Product details

  • ISBN 9780805810806
  • Weight: 520g
  • Dimensions: 152 x 229mm
  • Publication Date: 01 Jul 1992
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Paperback
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The modern study of cognition finds itself with two widely endorsed but seemingly incongruous theoretical paradigms. The first of these, inspired by formal logic and the digital computer, sees reasoning in the principled manipulation of structured symbolic representations. The second, inspired by the physiology of the brain, sees reasoning as the behavior that emerges from the direct interactions found in large networks of simple processing components. Each paradigm has its own accomplishments, problems, methodology, proponents, and agenda.

This book records the thoughts of researchers -- from both computer science and philosophy -- on resolving the debate between the symbolic and connectionist paradigms. It addresses theoretical and methodological issues throughout, but at the same time exhibits the current attempts of practicing cognitive scientists to solve real problems.