Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.Author Summary: In economic decision-making it is well-known that when decision-makers have several options, each associated with uncertain outcomes, their decision is not purely determined by the average payoff, but also takes into account the risk (that is, variability of the payoff) associated with each option. Some actions have a highly variable payoff, such as betting money on a horse, whereas others are much less variable, such as the return from a savings account. Whether an individual favors one action over the other depends on their risk-attitude. In contrast to economic decision-making, models of human motor control have exclusively focussed on models that maximize average rewards (minimize average cost). Here, we consider a computational model (an optimal feedback controller) that takes the variance of the cost into account when calculating the best movement strategy. We compare the model with the performance of human subjects in a sensorimotor task and find that the subjects' behavior is consistent with the predictions of a risk-sensitive optimal feedback controller with most subjects being risk-averse.