TY - JOUR T1 - Optimal Compensation for Temporal Uncertainty in Movement Planning A1 - Hudson, Todd E. A1 - Maloney, Laurence T. A1 - Landy, Michael S. Y1 - 2008/07/25 N2 - Author Summary Many recent models of motor planning are based on the idea that the CNS plans movements to minimize “costs” intrinsic to motor performance. A minimum variance model would predict that the motor system plans movements that minimize motor error (as measured by the variance in movement) subject to the constraint that the movement be completed within a specified time limit. A complementary model would predict that the motor system minimizes movement time subject to the constraint that movement variance not exceed a certain fixed threshold. But neither of these models is adequate to predict performance in everyday tasks that include external costs imposed by the environment where good performance requires that the motor system select a tradeoff between speed and accuracy. In driving to the airport to catch a plane, for example, there are very real costs associated with driving too fast and also with being just a bit too late. But the “optimal” tradeoff depends on road conditions and also on how important it is to catch the plane. We examine motor performance in analogous experimental tasks where we impose arbitrary monetary costs on movements that are “late” or “early” and show that humans systematically trade off risk and reward so as to maximize their expected monetary gain. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 4 IS - 7 UR - https://doi.org/10.1371/journal.pcbi.1000130 SP - e1000130 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000130 ER -