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- W2022755507 abstract "Observers completed a series of simulated medical diagnosis tasks that differed in category discriminability and base-rate/cost-benefit 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 (nonnegative cost) as opposed to a point loss (negative cost). These results support the flat-maxima and competition between reward and accuracy (COBRA) 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 nonnegative, and (c) by the end of training the importance placed on accuracy was equal for negative and nonnegative costs." @default.
- W2022755507 created "2016-06-24" @default.
- W2022755507 creator A5032781961 @default.
- W2022755507 creator A5077670142 @default.
- W2022755507 date "2001-01-01" @default.
- W2022755507 modified "2023-10-16" @default.
- W2022755507 title "On the relation between base-rate and cost-benefit learning in simulated medical diagnosis." @default.
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- W2022755507 doi "https://doi.org/10.1037//0278-7393.27.6.1367" @default.
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