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 for deviations from Bayes optimal behavior in human sensorimotor control that can be explained by informationprocessing constraints captured by a free energy variational principle. We discuss in how far such deviations can be considered as a special case of a general decision-making framework for bounded rationality inspired by statistical physics and thermodynamics.