Risk-Sensitivity in Bayesian Sensorimotor Integration

Jordi Grau-Moya, Pedro A. Ortega, Daniel A. Braun

Abstract: Author Summary Statistically optimal decision-makers use probabilistic predictive models of their environment to achieve their goals. However, in real life such probabilistic models can be wrong or only approximately true, in which case basing decisions exclusively on the statistics of such models can constitute a problematic decision criterion. In contrast, risk-sensitive decision-makers can take model uncertainty into account. They allow deviations from their probabilistic model depending on the quality of the predictions of the model. In particular, they trust their model less if it makes imprecise predictions and bias their decisions towards worst-case or best-case outcomes. Here we designed a sensorimotor task where subjects exhibit Bayesian information integration when they infer the hidden location of a target and they had to decide to make a more or less costly movement. We found that subjects exhibited a bias with respect to the statistically optimal movement towards less costly outcomes, the higher the uncertainty about the target location was. This interplay between estimation uncertainty and movement cost is consistent with a risk-sensitive decision criterion that takes model uncertainty into account.