Neural network models of categorical perception.

Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues 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 are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

Categorical perception effects induced by category learning.

The authors report a series of studies designed to determine whether effects similar to those observed in the innate categorical perception of color and phonemes are induced during the learning of simple unidimensional categories and more complex multidimensional ones. In Experiment 1 no evidence was found for such effects when stimuli varied on 1 dimension. Experiments 2 and 3 demonstrated a within-category compression effect but no between category expansion effect for stimuli varying in 2 dimensions. Compression only was also shown in Experiment 4, which used pictures of actual objects. Multidimensional scaling analyses illustrate how within-category compression without expansion was sufficient to produce categorical clustering of items in the similarity space. These analyses also show that learning changed the dimensional structure of similarity space. Results are compared with those from other studies exploring similar phenomena and with neural network simulations.

Explaining consciousness: the hard problem.

edited by Jonathan Shear, Bradford/MIT Press 1997. £33.95/$40.00 (vii+422 pages) ISBN 0 262 19388 4.