Reward Rate Optimization in Two-Alternative Decision Making: Empirical Tests of Theoretical Predictions

Patrick Simen, David Contreras, Cara Buck, Peter Hu, Philip Holmes, Jonathan D. Cohen

Research output: Contribution to journalArticlepeer-review

161 Scopus citations


The drift-diffusion model (DDM) implements an optimal decision procedure for stationary, 2-alternative forced-choice tasks. The height of a decision threshold applied to accumulating information on each trial determines a speed-accuracy tradeoff (SAT) for the DDM, thereby accounting for a ubiquitous feature of human performance in speeded response tasks. However, little is known about how participants settle on particular tradeoffs. One possibility is that they select SATs that maximize a subjective rate of reward earned for performance. For the DDM, there exist unique, reward-rate-maximizing values for its threshold and starting point parameters in free-response tasks that reward correct responses (R. Bogacz, E. Brown, J. Moehlis, P. Holmes, & J. D. Cohen, 2006). These optimal values vary as a function of response-stimulus interval, prior stimulus probability, and relative reward magnitude for correct responses. We tested the resulting quantitative predictions regarding response time, accuracy, and response bias under these task manipulations and found that grouped data conformed well to the predictions of an optimally parameterized DDM.

Original languageEnglish (US)
Pages (from-to)1865-1897
Number of pages33
JournalJournal of Experimental Psychology: Human Perception and Performance
Issue number6
StatePublished - Dec 2009

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)
  • Behavioral Neuroscience


  • decision making
  • diffusion
  • optimization
  • response time
  • reward rate


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