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.
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