The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks

Rafal Bogacz, Eric Brown, Jeff Moehlis, Philip Holmes, Jonathan D. Cohen

Research output: Contribution to journalArticle

877 Scopus citations

Abstract

In this article, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks. They begin by analyzing 6 models of TAFC decision making and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm (most accurate for a given speed or fastest for a given accuracy). They prove further that there is always an optimal trade-off between speed and accuracy that maximizes various reward functions, including reward rate (percentage of correct responses per unit time), as well as several other objective functions, including ones weighted for accuracy. They use these findings to address empirical data and make novel predictions about performance under optimality. (PsycINFO Database Record (c) 2006 APA, all rights reserved).

Original languageEnglish (US)
Pages (from-to)700-765
Number of pages66
JournalPsychological Review
Volume113
Issue number4
DOIs
StatePublished - Oct 2006

All Science Journal Classification (ASJC) codes

  • Psychology(all)

Keywords

  • Drift diffusion model
  • Optimal performance
  • Perceptual choice
  • Reward rate
  • Speed-accuracy trade-off

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