Neural correlates of individual variability in reward-based motor learning

Individual differences in motor learning are linked to variability in the neural processes associated with feedback processing and motor memory retention. This study investigated the neural correlates of reward-based motor learning and focused on two electrophysiological markers: event-related desynchronization (ERD) in the sensorimotor cortex and feedback-related negativity (FRN) from frontal EEG channels. Sixty-four healthy adults performed a visuomotor rotation task with monetary reward or punishment feedback. Learning amount, defined as the degree of error compensation, and retention amount, defined as the maintenance of adapted motor behavior in a no-vision condition, were quantified. Scalp EEG was recorded to assess alpha (8–13 Hz) and beta (15–30 Hz) ERD over the primary motor cortex (M1) during movement preparation and FRN amplitude following feedback presentation. Stepwise regression revealed that FRN amplitude in the late adaptation phase was associated with learning amount, but only among participants with robust event-related potential (ERP) responses (amplitude > ±5 µV). This finding suggests that greater neural sensitivity to feedback supports better learning. In contrast, alpha ERD magnitude in the late adaptation phase, along with reinforcement conditions, was a significant predictor of motor skill retention in the full sample, although no interaction effect was observed. These findings suggest that FRN and alpha ERD reflect dissociable neural mechanisms underlying learning and retention, respectively, with FRN indexing feedback sensitivity and alpha ERD reflecting cortical excitability related to memory consolidation. The absence of interaction effects between neural markers and reinforcement conditions further supports the idea that these processes contribute independently to motor adaptation. Practically, combining reward-based training with interventions that increase M1 excitability may enhance retention, while tailoring feedback strategies to maximize FRN responses may improve learning efficiency. These findings provide novel insights into the neural basis of individual variability in motor learning and suggest avenues for personalized approaches in rehabilitation and skill learning.