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Maddox WT, Bohil CJ. Optimal classifier feedback improves cost-benefit but not base-rate decision criterion learning in perceptual categorization. Mem Cognit. 2005;33(2):303-19doi: 10.3758/bf03195319.
Maddox, W. T., & Bohil, C. J. (2005). Optimal classifier feedback improves cost-benefit but not base-rate decision criterion learning in perceptual categorization. Memory & cognition, 33(2), 303-19. https://doi.org/10.3758/bf03195319
Maddox, W Todd, and Bohil, Corey J. "Optimal classifier feedback improves cost-benefit but not base-rate decision criterion learning in perceptual categorization." Memory & cognition vol. 33,2 (2005): 303-19. doi: https://doi.org/10.3758/bf03195319
Maddox WT, Bohil CJ. Optimal classifier feedback improves cost-benefit but not base-rate decision criterion learning in perceptual categorization. Mem Cognit. 2005 Mar;33(2):303-19. doi: 10.3758/bf03195319. PMID: 16028585.
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Mem Cognit. 2005 Mar;33(2):303-19. doi: 10.3758/bf03195319.

Optimal classifier feedback improves cost-benefit but not base-rate decision criterion learning in perceptual categorization.

Memory & cognition

W Todd Maddox, Corey J Bohil

Affiliations

  1. Department of Psychology, 1 University Station A8000, University of Texas, Austin, TX 78712, USA. [email protected]

PMID: 16028585 DOI: 10.3758/bf03195319

Abstract

Unequal payoffs engender separate reward- and accuracy-maximizing decision criteria; unequal base rates do not. When payoffs are unequal, observers place greater emphasis on accuracy than is optimal. This study compares objective classifier (the objectively correct response) with optimal classifier feedback (the optimal classifier's response) when payoffs or base rates are unequal. It provides a critical test of Maddox and Bohil's (1998) competition between reward and accuracy maximization (COBRA) hypothesis, comparing it with a competition between reward and probability matching (COBRM) and a competition between reward and equal response frequencies (COBRE) hypothesis. The COBRA prediction that optimal classifier feedback leads to better decision criterion leaning relative to objective classifier feedback when payoffs are unequal, but not when base rates are unequal, was supported. Model-based analyses suggested that the weight placed on accuracy was reduced for optimal classifier feedback relative to objective classifier feedback. In addition, delayed feedback affected learning of the reward-maximizing decision criterion.

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