Optimizing learning for control of human-machine interfaces.

Human-machine interfaces, such as augmented reality devices, hand prostheses, and brain-computer interfaces, are increasingly being used in neurorehabilitation. In this issue of Neuron, Vassiliadis et al.1 examine the interaction between closed-loop continuous reinforcement and sensory feedback in learning of human-machine interface control.