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Abstract:
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 evidence that humans deviate from Bayes optimal behavior in their movements, because they are sensitive to model
uncertainty. Furthermore, we discuss in how far model uncertainty can be incorporated in optimality models of sensorimotor behavior.
