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1 Center for Neurobiology & Behavior, Columbia University, New York, New York, United States
2 Department of Neurology, Columbia University, New York, New York, United States
3 Center for Neurobiology & Behavior, Columbia University, New York, New York, United States; Department of Physiology & Cellular Biophysics, Columbia University, New York, New York, United States
* To whom correspondence should be addressed. E-mail: nq6{at}columbia.edu.
Movement duration is an integral component of motor control, but nearly all extant optimization models of motor planning pre-fix duration instead of explaining it. We propose a new optimization principle that predicts movement duration. The model assumes that the brain attempts to minimize duration under the constraint of meeting an accuracy criterion that is task- and context-dependent. The model determines a unique duration as a tradeoff between speed (time optimality) and accuracy (acceptable end-point scatter). We analyzed the model for a linear motor plant, and obtained a closed-form equation for determining movement duration. By solving the equation numerically with specific plant-parameters for the eye and arm, we found that the model can reproduce saccade duration as a function of amplitude (the main sequence), and arm-movement duration as a function of the target-distance-to-size ratio (Fitts' law). In addition, it explains the dependence of peak saccadic speed on amplitude, and the dependence of saccadic duration on initial eye position. Furthermore, for arm movements, the model predicts a scaling relationship between peak velocity and distance, and a reduction in movement duration with a moderate increase in viscosity. Finally, for a linear plant, our model predicts a control signal identical to that of the minimum-variance model set to the same duration. This control signal is a smooth function of time, in contrast to the discontinuous bang-bang control found in the time-optimal-control literature. We suggest that one aspect of movement planning may be to assign an end-point accuracy criterion for a given task and context
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