On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization

Abstract

Biased category payoff matrices engender separate reward-and accuracy-maximizingdecision criteria. Although instructed to maximize reward, observers use suboptimal decision criteria that place greater emphasis on accuracy than is optimal. In this study, objective classifier feedback (the objectively correct response) was compared with optimal classifier feedback (the optimal classifier' s response) at two levels of category discriminability when zero or negative costs accompanied incorrect responses for two payoff matrix multiplication factors. Performance was superior for optimal classifier feedback relative to objective classifier feedback for both zero-and negative-cost conditions, especially when category discriminability was low, but the magnitude of the optimal classifier advantage was approximately equal for zero-and negative-cost conditions. The optimal classifier feedback performance advantage did not interact with the payoff matrix multiplication factor. Model-based analyses suggested that the weight placed on accuracy was reduced for optimal classifierfeedback relative to objective classifier feedback and for high category discriminability relative to low category discriminability. In addition, the weight placed on accuracy declined with training when feedback was based on the optimal classifier and remained relativelystable when feedback was based on the objective classifier.These results suggest that feedback based on the optimal classifier leads to superior decision criterion learning across a wide range of experimental conditions.

Implicit learning mediates base rate acquisition in perceptual categorization

“…Based on previous work (e.g., Bohil & Maddox 2003a, b), we suspected that the influence of base-rates might most clearly be seen when category discriminability is low ( d ’1 conditions) and when the base-rate differential was most pronounced (3:1 conditions). As a result, we conducted another ANOVA looking at only the d ’1, 3:1 base-rate conditions (including those with and without baseline training).…”

“…During this phase, category d ’ matched the ensuing experimental phase, and participants gave a categorization response followed by feedback on each trial. This procedure has been used in previous studies to isolate base-rate influence from category learning (Bohil & Maddox, 2003a, b). Two groups also completed conditions without first completing baseline category training.…”

Irrational time allocation in decision-making

“…R. Soc. B 283: 20151439 performance from blocks 2-5 on both a dummy variable for the intervention blocks, as well as an integer variable (2)(3)(4)(5) that encoded the block number. The mixed-effects regression results (see the electronic supplementary material , table S2) show that, all else being equal, with each additional block, earnings increased by 61.86 points (z ¼ 5.90, p , 0.0001) in the twinkling-stars task.…”

AN INTEGRATIVE THEORY OF LOCUS COERULEUS-NOREPINEPHRINE FUNCTION: Adaptive Gain and Optimal Performance

“…In principle, we can imagine utility functions that differentially weigh types of outcomes under varying circumstances (e.g., speed over accuracy, or vice versa;Bohil & Maddox 2003, Mozer et al 2002, Wald 1947. However, it seems reasonable to assume that under many (if not most) circumstances, a critical objective is to maximize the rewards accrued by performance.…”