Abstract
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.
Highlights
Risk-attitudes are an important determinant of human decisionmaking that expresses itself, for example, with individuals who are risk-seeking investing in highly volatile stocks and those who are risk-averse choosing governments bonds
Random forces acted on the ball which caused the ball to drift under Brownian motion
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, and takes into account the risk associated with each option
Summary
Risk-attitudes are an important determinant of human decisionmaking that expresses itself, for example, with individuals who are risk-seeking investing in highly volatile stocks and those who are risk-averse choosing governments bonds. When subjects are given a choice of either a risky but high-average reward (a 50-50 chance of winning £100 or nothing) and a sure-bet with lower average reward (£45 for sure), the majority of people choose the sure option – for example see [1,2] This effect is called risk-aversion because people are willing to accept a lower average payoff in order to reduce the variability of the payoff. Optimal feedback control has been proposed as a model for continuous optimal decision-making and has successfully explained a wide range of movement phenomena such as variability patterns [4], the response of bimanual movements to perturbations [5,6], adaptation to novel tasks [7,8,9] and complex object manipulation [10] This model computes the optimal strategy given a cost function that penalizes a combination of error and effort. We show that subjects’ behavior is consistent with risk-sensitive optimal control models with most subjects being risk-averse
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