Real-time reinforcement for human-machine interface control

A major challenge involved in human-machine interfaces is developing feedback strategies that improve control and benefit patients with motor disabilities. Here, we propose, validate, and mechanistically characterize a personalized, closed-loop strategy that delivers reinforcement feedback in real time during human-machine interface control. Across five experiments involving 106 participants and two control interfaces, fewer than 20 reinforcement trials produced immediate improvements in force control and lasting retention gains. These effects were strongest when visual and/or somatosensory feedback was limited, a finding that suggests translational relevance for tasks, technologies, and pathologies with limited sensory feedback. In chronic stroke patients, real-time reinforcement likewise improved online force control under limited visual feedback, although short training did not yield retention gains. Information-theoretic analyses further revealed that reinforcement compensates for reduced feedback control when sensory feedback is sparse and promotes motor exploitation of successful actions. Overall, these findings identify real-time reinforcement as a promising strategy for enhancing human-machine interface control.