Model uncertainty and risk-sensitivity in sensorimotor learning and decision-making

Recent advances in theoretical neuroscience suggest that sensorimotor control can be considered as a continuous decision-making process in which uncertainty plays a key role. Decision-makers can be risk-sensitive with respect to this uncertainty, first by not only considering the average payoff of an outcome, but also the variability of the payoffs, and second by taking into account model uncertainty in the presence of latent variables. Although such risk-sensitivity is a well-established phenomenon in psychology and economics, it has been much less studied in motor control. In fact, leading theories of motor control, such as optimal feedback control, assume that motor behaviors can be explained as the optimization of a given expected payoff or cost. Here we discuss evidence that humans exhibit risk-sensitivity and model uncertainty in their motor behaviors. Furthermore, we discuss how risk-sensitivity and model uncertainty can be considered as a special case of a general decision-making framework inspired by statistical physics and thermodynamics