Localist Connectionist Approaches To Human Cognition

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Affect Model Fit
algorithmic cognition
BIA Model
bilingual
bilingual language processing
Category=JMA
Category=JMR
cognitive model selection in neuroscience
Context Independent Properties
Context Objects
Control Node
Data Sets
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Feature Matches
Gating Nodes
gradient
IAC Model
Increase Mapping Accuracy
inhibition
lateral
Lateral Inhibition
Localist Connectionist Approach
Localist Connectionist Models
Model Fit
Model Selection Methods
models
neural computation
nodes
orthographic processing
phoneme
Phoneme Nodes
Phonological Encoding
Phonological Similarity Effect
primacy
Primacy Gradient
psychological modeling
recognition
Serial Position Curve
Serial Recall
serial recall processes
Size Adjective
Speech Errors
Symbol Processing Models
word
Word Node

Product details

  • ISBN 9781138002753
  • Weight: 498g
  • Dimensions: 152 x 229mm
  • Publication Date: 12 Aug 2014
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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This volume provides an overview of a relatively neglected branch of connectionism known as localist connectionism. The singling out of localist connectionism is motivated by the fact that some critical modeling strategies have been more readily applied in the development and testing of localist as opposed to distributed connectionist models (models using distributed hidden-unit representations and trained with a particular learning algorithm, typically back-propagation). One major theme emerging from this book is that localist connectionism currently provides an interesting means of evolving from verbal-boxological models of human cognition to computer-implemented algorithmic models. The other central messages conveyed are that the highly delicate issue of model testing, evaluation, and selection must be taken seriously, and that model-builders of the localist connectionist family have already shown exemplary steps in this direction.

Arthur Jacobs, Jonathan Grainger, Arthur M. Jacobs