Laurence T. Maloney

Department of Psychology and Center for Neural Science, New York University

In executing any speeded movement, there is uncertainty about the outcome due to motor
variability. I’ll present a Bayesian decision theoretic model of ideal movement planning that
takes into account a subject’s own spatial and temporal motor uncertainty. I’ll briefly 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. Subjects’ performance came close to
maximizing expected gain.

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 Allais paradox) into motor form and directly compare decision
making under risk to movement planning under risk in the same subjects. The results suggest
that, while individuals value rewards identically in planning movement and in making economic
decisions, their use of probability is markedly different.