Base-rate and payoff effects in multidimensional perceptual categorization.

Abstract
The optimality of multidimensional perceptual categorization performance with unequal base rates and payoffs was examined. In Experiment 1, observers learned simultaneously the category structures and base rates or payoffs. Observers showed conservative cutoff placement when payoffs were unequal and extreme cutoff placement when base rates were unequal. In Experiment 2, observers were trained on the category structures before the base-rate or payoff manipulation. Simultaneous base-rate and payoff manipulations tested the hypothesis that base-rate information and payoff information are combined independently. Observers showed (a) small suboptimalities in base-rate and payoff estimation, (b) no qualitative differences across base-rate and payoff conditions, and (c) support for the hypothesis that base-rate and payoff information is combined independently. Implications for current theories of base-rate and payoff learning are discussed. Categorization is a primary component of many behaviors of all organisms. Rats categorize bits of food as "large" or "small," with small pieces being eaten immediately and large pieces being hoarded (Wishaw, 1990; Wishaw & Tomie, 1989). The red-bellied stickleback categorizes prey by color and pattern, with certain patterns being pursued and others being avoided (Alcock, 1989). Humans categorize speech sounds and handwritten characters to facilitate communication. Medical doctors categorize X-rays to deter- mine whether a tumor is present or absent and to make diagnoses by examining patterns of symptoms or test results. All organisms divide objects and events into separate categories. If they did not perform these tasks with some measure of success, they would die and their species would become extinct. In light of this fact, it is reasonable to hypothesize that in many domains, human (and other organisms') categorization performance is very nearly opti- mal (Ashby & Maddox, 1998). Although optimality can be defined in many ways, a common definition is performance that maximizes long-run reward (Green & Swets, 1966). To examine rigorously the optimality of categorization performance, one must identify the basic properties of everyday categorization problems. First, the stimulus can be decomposed into a set of values along multiple basic

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