Linear transformations of the payoff matrix and decision criterion learning in perceptual categorization

The effects of payoff-matrix multiplication, payoff-matrix addition, the presence of long-run gains versus long-run losses, category discriminability, and base rate on decision criterion learning were examined in 2 perceptual categorization experiments. Observers were found to be sensitive to the effects of payoff-matrix multiplication (and category discriminability) on the steepness of the objective reward function in line with predictions from the flat-maxima hypothesis and contrary to the predictions from the payoff-variance hypothesis. Decision criterion learning was best in base-rate conditions, was worst when losses were associated with incorrect responding, and was intermediate when no losses were associated with incorrect responding. This performance profile was well captured by the competition between reward and accuracy (COBRA) hypothesis. A hybrid model framework that instantiates both the flat-maxima and COBRA hypotheses was necessary to account for the data from both experiments.