Relevance of Predictive and Postdictive Error Information in the Course of Motor Learning

Abstract

The prediction of the sensory consequences of physical movements is a fundamental feature of the human brain. This function is attributed to a forward model, which generates predictions based on sensory and efferent information. The neural processes underlying such predictions have been studied using the error-related negativity (ERN) as a fronto-central event-related potential in electroencephalogram (EEG) tracings. In this experiment, 16 participants practiced a novel motor task for 4000 trials over ten sessions. Neural correlates of error processing were recorded in sessions one, five, and ten. Along with significant improvements in task performance, the ERN amplitude increased over the sessions. Simultaneously, the feedback-related negativity (FRN), a neural marker corresponding to the processing of movement-outcome feedback, attenuated with learning. The findings suggest that early in learning, the motor control system relies more on information from external feedback about terminal outcome. With increasing task performance, the forward model is able to generate more accurate outcome predictions, which, as a result, increasingly contributes to error processing. The data also suggests a complementary relationship between the ERN and the FRN over motor learning.