title: Probability matching, accuracy maximization, and a test of the optimal classifier's independence assumption in perceptual categorization.
abstract: Observers completed perceptual categorization tasks that included 25 base-rate/payoff conditions constructed from the factorial combination of five base-rate ratios (1:3, 1:2, 1:1, 2:1, and 3:1) with five payoff ratios (1:3, 1:2, 1:1, 2:1, and 3:1). This large database allowed an initial comparison of the competition between reward and accuracy maximization (COBRA) hypothesis with a competition between reward maximization and probability matching (COBRM) hypothesis, and an extensive and critical comparison of the flat-maxima hypothesis with the independence assumption of the optimal classifier. Model-based instantiations of the COBRA and COBRM hypotheses provided good accounts of the data, but there was a consistent advantage for the COBRM instantiation early in learning and for the COBRA instantiation later in learning. This pattern held in the present study and in a reanalysis of Bohil and Maddox (2003). Strong support was obtained for the flat-maxima hypothesis over the independence assumption, especially as the observers gained experience with the task. Model parameters indicated that observers' reward-maximizing decision criterion rapidly approaches the optimal value and that more weight is placed on accuracy maximization in separate base-rate/payoff conditions than in simultaneous base-rate/payoff conditions. The superiority of the flat-maxima hypothesis suggests that violations of the independence assumption are to be expected, and are well captured by the flat-maxima hypothesis, with no need for any additional assumptions.
authors.affiliation: Department of Psychology, University of Texas, Austin, Texas 78712, USA. maddox@psy.utexas.edu
authors.forename: W Todd
authors.initials: WT
authors.lastname: Maddox
authors.forename: Corey J
authors.initials: CJ
authors.lastname: Bohil
mesh_terms: D003657:Decision Making; D004193:Discrimination Learning; D006801:Humans; D008962:Models, Theoretical; D009042:Motivation; D010364:Pattern Recognition, Visual; D011337:Probability Learning; D011340:Problem Solving; D011601:Psychophysics; D012201:Reward; D017603:Signal Detection, Psychological