Statistical decision theory and trade-offs in the control of motor response

We present a novel approach to the modeling of motor responses based on statistical decision theory. We begin with the hypothesis that subjects are ideal motion planners who choose movement trajectories to minimize expected loss. We derive predictions of the hypothesis for movement in environments where contact with specified regions carries rewards or penalties. The model predicts shifts in a subject's aiming point in response to changes in the reward and penalty structure of the environment and with changes in the subject's uncertainty in carrying out planned movements. We tested some of these predictions in an experiment where subjects were rewarded if they succeeded in touching a target region on a computer screen within a specified time limit. Near the target was a penalty region which, if touched, resulted in a penalty. We varied distance between the penalty region and the target and the cost of hitting the penalty region. Subjects shift their mean points of contact with the computer screen in response to changes in penalties and location of the penalty region relative to the target region in qualitative agreement with the predictions of the hypothesis. Thus, movement planning takes into account extrinsic costs and the subject's own motor uncertainty.