Costs and benefits in perceptual categorization.

Observers categorized perceptual stimuli when the category costs and benefits were manipulated across conditions, and costs were either zero or nonzero. The cost-benefit structures were selected so that performance across conditions was equivalent with respect to the optimal classifier. Each observer completed several blocks of trials in each of the experimental conditions, and a series of nested models was applied to the individual observer data from all conditions. In general, performance became more nearly optimal as observers gained experience with the cost-benefit structures, but performance reached asymptote at a suboptimal level. Observers behaved differently in the zero- and nonzero-cost conditions, performing consistently worse when costs were nonzero. A test of the hypothesis that observers weight costs more heavily than benefits was inconclusive. Some aspects of the data supported this differential weighting hypothesis, but others did not. Implications for current theories of cost-benefit learning are discussed.