Ellsberg's paradox in sensorimotor learning

Both sensorimotor and economic behavior in humans can be understood as optimal decisionmaking under uncertainty specified by probabilistic models. In many important everyday situations, however, such models might not be available or be ambiguous due to lack of familiarity with the environment. Deviations from optimal decisionmaking in the face of ambiguity have first been reported by Ellsberg in economic choices between urns of known and unknown composition. Here we designed an urn task similar to Ellsberg's task and an equivalent motor task, where subjects choose between hitting partially occluded targets with differing degree of ambiguity. In both experiments subjects had to choose between a risky and an ambiguous option in every trial. The risky option provided full information about the probabilities of the possible outcomes. The ambiguous option was always characterized by a lack of information with respect to the probabilities. We could manipulate the degree of ambiguity by varying the amount of information revealed about the ambiguous option. In the motor task, we manipulated the extent to which an ambiguous target was occluded that subjects aimed to hit, whereas in the urn task we varied the number of balls drawn from the ambiguous urn before subjects made their decision. This way, we could test the more general hypothesis that decisionmakers gradually switch from ambiguity to risk when more information becomes available. Ellsberg's paradox then arises in the limit case in which the ambiguous option gives away no information. We found that subjects tended to avoid ambiguous urns in line with Ellsberg's results, however, the same subjects tended to be ambiguityloving or neutral in the motor task. One of the most important points of Ellsberg's original experiment was to show that expected utility models—that is models that only care about maximizing expected success—cannot explain subjects' choice behavior under ambiguity. Since then a number of models for decisionmaking under ambiguity have been proposed. However, few of them are able to dynamically change the degree of ambiguity as new information arrives. Here we employ a multiplier preference model, that is a type of variational preference model for decisionmaking under ambiguity, and use it under a Bayesian update procedure to integrate novel information. We show that the deviations from optimal decisionmaking can be explained by such a robust Bayesian decisionmaking model. Our results suggest that ambiguity is a ubiquitous phenomenon, not only to understand economic choice behavior, but also sensorimotor learning and control