Computational models to understand sensorimotor control and adaptation performance

How the incoming sensory information and the knowledge of body dynamics are combined by the nervous system to control movement execution? One way to investigate this is to design experiments in which movements are perturbed by either an unfamiliar dynamic environment, or by alterations in the available sensory information. Then we may look at the resulting errors patterns, and at whether and how the motor system adapts to them. Experiments may involve healthy as well as neurologically impaired subjects, so that it may be possible to characterize at least some pathologies in terms of abnormal sensorimotor control. Interpretation of these experimental results requires a solid theoretical framework, involving Bayesian statistics and control theory. These are the main ingredients of modern theories of motor control. As an example of this approach, we review two experiments in which we studied (i) the effects of vibration of the biceps muscle on the planning and execution of planar reaching movements of the upper limb; and (ii) the adaptation to speed-dependent force fields in multiple sclerosis patients