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Neural Network Models of Categorical Perception

By R.I. Damper and S.R. Harnad

Abstract

Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan et al. introduced the use of signal detection theory to CP studies. Anderson et al. simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms is capable of gen-erating the characteristics of categorical perception. Hence, CP may not be a special mode of perception but an emergent property of any sufficiently powerful general learning system

Topics: Neural Nets, Perceptual Cognitive Psychology
Year: 2000
DOI identifier: 10.3758/bf03206927
OAI identifier: oai:cogprints.org:1620

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