Sensorimotor learning and decision-making in complex environments

Recent advances in movement neuroscience suggest that sensorimotor control can be considered as a continuous decision-making process in complex environments in which uncertainty and task variability play a key role. Leading theories of motor control assume that the motor system learns probabilistic models and that motor behavior can be explained as the optimization of payoff or cost criteria under the expectation of these models. Here we discuss how the motor system exploits task variability to build up efficient models through structural learning and compare human behavior to Bayes optimal models. In particular, we focus on deviations from these normative models due to effects of model uncertainty and we discuss in how far model uncertainty can be considered as a special case of a general decision-making framework that considers limited information-processing capabilities