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Perception & Psychophysics 1993, 53 (1), 49-70
 

Summary: Perception & Psychophysics
1993, 53 (1), 49-70
Comparing decision bound and exemplar
models of categorization
W. TODD MADDOX and F. GREGORY ASHBY
University of California, Santa Barbara, California
The performance of a decision bound model of categorization(Ashliy,J992a;Ashhy & Maddox,
in press) is compared with the performance oftwo exemplar models. The first is the generalized
context model (e.g., Nosofsky, 1986, 1992) and the second is a recently proposed deterministic
exemplar model (Ashby & Maddox, in press), which contains the generalized context model as
a special case. When the exemplars from each category were normally distributed and the op-
timal decision bound was linear, the deterministic exemplar model and the decision bound model
provided roughly equivalent accounts of the data. When the optimal decision bound was non-
linear, the decision bound model provided a more accurate account of the data than did either
exemplar model. When applied to categorization data collected by Nosofsky (1986, 1989), in which
the category exemplars are not normally distributed, the decision bound model provided excel-
lent accounts of the data, in many cases significantly outperforming the exemplar models. The
decision bound model wasfound to be especiallysuccessful when(1) single subject analyses were
performed, (2) each subject was given relatively extensive training, and (3) the subject's perfor-
mance was characterized by complex suboptimalities. These results support the hypothesis that

  

Source: Ashby, F. Gregory - Department of Psychology, University of California at Santa Barbara

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences