The drift-diffusion model (DDM) describes decision making in simple, two-alternative forced choice (2AFC) tasks. It accurately fits response-time distributions and implements an optimal decision procedure for stationary 2AFC tasks: for a given accuracy, no other model achieves faster average response times. The value of a decision threshold applied to accumulated information also 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 the rate of earned rewards. 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 (). 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.
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