Free energy and sensorimotor information processing

Daniel Braun
Max Planck Institute for Biological Cybernetics, Tübingen

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 considered as a special case of a general decision-making 
framework inspired by statistical physics and thermodynamics.