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

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 errorrelated 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. This article is part of a Special Issue entitled: Error Processing (c) 2021 IBRO. Published by Elsevier Ltd. All rights reserved.

Automated summary

The human brain has the ability to predict the sensory consequences of physical movements through a forward model that generates predictions based on sensory and efferent information. This experiment studied the neural processes of error-related negativity (ERN) during the learning process and found that as task performance improved, the amplitude of ERN increased while the feedback-related negativity (FRN) attenuated. This suggests that in the early stages of learning, the motor control system relies more on external feedback, and as task performance improves, the forward model generates more accurate predictions, which contributes to error processing. The data also indicates a complementary relationship between ERN and FRN in motor learning.