Institute for Mathematical Behavioral Sciences

In executing any speeded movement, there is uncertainty about the outcome due to motor variability. I’ll present a statistical decision theoretic (SDT) model of ideal movement planning that takes into account a subject’s own spatial and temporal motor uncertainty. I’ll then describe recent experiments in which subjects carried out speeded motor tasks. The outcome of each movement earned an explicit monetary reward or penalty. In one game, for example, subjects attempted to reach out and touch briefly presented reward disks while avoiding nearby, overlapping penalty disks. The task for the subject was to trade off the risk of missing the reward disk against the risk of hitting the penalty disk. The optimum tradeoff depended on the magnitudes of penalty and reward and the subject’s own motor error. For each subject, in each game, we could estimate the ideal movement strategy and maximum expected gain possible using the SDT model. We compared subjects’ movements and winnings to this ideal. In many of these experiments, subjects consistently chose movements that were close to optimal. This outcome is surprising: these motor tasks are formally equivalent to decision making under risk and subjects making decisions under risk typically do not maximize expected gain. I’ll describe very recent work in which we set out to translate classical decision making experiments (concerning the independence axiom of expected utility theory) into motor form and compare decision making under risk to movement planning under risk in the same subjects.