Modulation of motor performance by a monetary incentive: A pilot study.

It is commonly acknowledged that movement performance is determined by a trade-off between accuracy requirements and energetic expenditure. However, their relative weights are subjective and depend on the perceived benefit (or cost) associated to successful movement completion. A deeper knowledge on how this trade-off affects motor behavior may suggest ways to manipulate it in pathologies, like Parkinson's disease, in which the mechanisms underlying the selection of motor response are believed to be defective.

Reward-based learning of a redundant task.

Motor skill learning has different components. When we acquire a new motor skill we have both to learn a reliable action-value map to select a highly rewarded action (task model) and to develop an internal representation of the novel dynamics of the task environment, in order to execute properly the action previously selected (internal model). Here we focus on a 'pure' motor skill learning task, in which adaptation to a novel dynamical environment is negligible and the problem is reduced to the acquisition of an action-value map, only based on knowledge of results.

Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level.

Computational models of neuromotor recovery after a stroke might help to unveil the underlying physiological mechanisms and might suggest how to make recovery faster and more effective. At least in principle, these models could serve: (i) To provide testable hypotheses on the nature of recovery; (ii) To predict the recovery of individual patients; (iii) To design patient-specific "optimal" therapy, by setting the treatment variables for maximizing the amount of recovery or for achieving a better generalization of the learned abilities across different tasks. Here we review the state of the art of computational models for neuromotor recovery through exercise, and their implications for treatment.