creators_name: Harnad, Stevan
creators_name: Hanson, S.J.
creators_name: Lubin, J.
editors_name: Powers, D. W.
editors_name: Reeker, L.
type: bookchapter
datestamp: 2001-06-18
lastmod: 2011-03-11 08:54:40
metadata_visibility: show
title: Categorical Perception and the Evolution of Supervised Learning in Neural Nets
ispublished: pub
subjects: comp-sci-neural-nets
subjects: percep-cog-psy
full_text_status: public
keywords: categorical perception, neural nets, learning 
abstract: Some of the features of animal and human categorical perception
(CP) for color, pitch and speech are exhibited by neural net simulations of CP with
one-dimensional inputs: When a backprop net is trained to discriminate and then
categorize a set of stimuli, the second task is accomplished by "warping" the
similarity space (compressing within-category distances and expanding
between-category distances). This natural side-effect also occurs in humans and
animals. Such CP categories, consisting of named, bounded regions of similarity
space, may be the ground level out of which higher-order categories are
constructed; nets are one possible candidate for the mechanism that learns the
sensorimotor invariants that connect arbitrary names (elementary symbols?) to the
nonarbitrary shapes of objects. This paper examines how and why such
compression/expansion effects occur in neural nets. 
date: 1991
date_type: published
publication: Working Papers of the AAAI Spring Symposium on Machine Learning of Natural Language and Ontology
publisher: Symposium on Symbol Grounding: Problems and Practice, Stanford University
pagerange: 65-74
refereed: FALSE
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Harnad, S. (ed.) (1987) "Categorical Perception: The Groundwork of Cognition" .
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citation:   Harnad, Stevan and Hanson, S.J. and Lubin, J.  (1991) Categorical Perception and the Evolution of Supervised Learning in Neural Nets.  [Book Chapter]     
document_url: http://cogprints.org/1579/1/harnad91.cpnets.html