COMPARING DECISION BOUND AND EXEMPLAR MODELS OF CATEGORIZATION

The performance of a decision bound model of categorization (Ashby, 19 92a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., No sofsky, 1986, 1992) and the second is a recently proposed deterministi c exemplar model (Ashby & Maddox, in press), which contains the genera lized context model as a special case. When the exemplars from each ca tegory were normally distributed and the optimal decision bound was li near, the deterministic exemplar model and the decision bound model pr ovided roughly equivalent accounts of the data. When the optimal decis ion bound was nonlinear, the decision bound model provided a more accu rate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986, 1989), in which th e category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significa ntly outperforming the exemplar models. The decision bound model was f ound to be especially successful when (1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subject's performance was characterized by complex subopti malities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable altern ative to the currently popular exemplar models of categorization.