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

• Published in: PLoS computational biology Vol. 6; no. 7; p. e1000857
• Main Authors: Nagengast, Arne J., Braun, Daniel A., Wolpert, Daniel M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2010; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Analysis of Variance; Biological control systems; Computer Science/Systems and Control Theory; Computer Simulation; Cost control; Decision making; Expected values; Experiments; Feedback, Sensory - physiology; Female; Humans; Linear Models; Male; Models, Biological; Motor ability; Neuroscience/Motor Systems; Neuroscience/Psychology; Neuroscience/Theoretical Neuroscience; Noise; Risk aversion; Risk-Taking; Studies; Uncertainty; United Kingdom; Uncertainty; Algorithms; Analysis of Variance; Models, Biological; Computer Simulation; Humans; Risk-Taking; Adult; Female; Linear Models; Male; Feedback, Sensory
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000857

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.