Abstract not found

Observers completed a series of simulated medical diagnosis tasks that differed in category discriminability and base-rate/costbenefit ratio. Point, accuracy, and decision criterion estimates were closer to optimal (a) for category d' = 2.2 than for category d' = 1.0 or 3.2, (b) when base-rates, as opposed to cost-benefits were manipulated, and (c) when the cost of an incorrect response resulted in no point loss (non-negative cost) as opposed to a point loss (negative cost). These results support the "flat-maxima" (von Winterfeldt & Edwards, 1982) and COmpetition Between Reward and Accuracy (COBRA; Maddox & Bohil, 1998a) hypotheses. A hybrid model that instantiated simultaneously both hypotheses was applied to the data. The model parameters indicated that (a) the reward-maximizing decision criterion quickly approached the optimal criterion, (b) the importance placed on accuracy maximization early in learning was larger when the cost of an incorrect response was negative as opposed to non-negative, and (c) by the end of training the importance placed on accuracy was equal for negative and non-negative costs.

this article. We also thank Lofilei Carderhas and Robert F. zwin for help with data collection