Neural network models of categorical perception

Author(s): Harnad, S. R.

Journal/Book: Percept Psychophys. 2000; 62: 1710 Fortview Rd, Austin, TX 78704, USA. Psychonomic Soc Inc. 843-867.

Abstract: 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 mode of perception but an emergent property of any sufficiently powerful general learning system.

Note: Review Damper RI, Univ Southampton, Dept Elect & Comp Sci, Image Sppech & Intelligent Syst, Res Grp, Bldg 1, Southampton SO17 1BJ, Hants, ENGLAND

Keyword(s): VOICE-ONSET TIME; INITIAL STOP CONSONANTS; SPEECH-PERCEPTION; ADAPTIVE RESONANCE; ENHANCED DISCRIMINABILITY; COMPUTATIONAL MODEL; PHONETIC BOUNDARIES; INTERNAL STRUCTURE; MOTOR THEORY; CATEGORIZATION

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