Complex Decision Rules in Categorization: Contrasting Novice and Experienced Performance

The ability of novice and experienced Ss to learn complex decision rules was tested with 3 categorization tasks. Each task involved 2 categories with exemplars that were normally distributed on 2 stimulus dimensions. 3 separate sets of stimuli were used, and in each task the decision rule that maximized categorization accuracy was a highly nonlinear function of the stimulus dimension values. In the 3 tasks, all experienced Ss used highly nonlinear decision rules. Quadratic rules were supported over bilinear rules, and in many cases, Ss used nearly optimal decision rules. These findings did not depend on whether the stimulus components were integral or separable. Novice Ss also did not use simple linear rules. A model that assumed Ss tried a succession of different linear rules was also rejected. Instead, novices appeared to use quadratic rules, although less consistently than experienced Ss.