Processing graded feedback: Electrophysiological correlates of learning from small and large errors

Di Luft, Caroline Bernardi; Takase, Emilio and Bhattacharya, Joydeep. 2014. Processing graded feedback: Electrophysiological correlates of learning from small and large errors. Journal of Cognitive Neuroscience, 26(5), pp. 1180-1193. ISSN 0898-929X [Article]

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Abstract or Description

Feedback processing is important for learning and therefore may affect the consolidation of skills. Considerable research demonstrates electrophysiological differences between correct and incorrect feedback, but how we learn from small versus large errors is usually overlooked. This study investigated electrophysiological differences when processing small or large error feedback during a time estimation task. Data from high-learners and low-learners were analyzed separately. In both high- and low-learners, large error feedback was associated with higher feedback-related negativity (FRN) and small error feedback was associated with a larger P300 and increased amplitude over the motor related areas of the left hemisphere. In addition, small error feedback induced larger desynchronization in the alpha and beta bands with distinctly different topographies between the two learning groups: The high-learners showed a more localized decrease in beta power over the left frontocentral areas, and the low-learners showed a widespread reduction in the alpha power following small error feedback. Furthermore, only the high-learners showed an increase in phase synchronization between the midfrontal and left central areas. Importantly, this synchronization was correlated to how well the participants consolidated the estimation of the time interval. Thus, although large errors were associated with higher FRN, small errors were associated with larger oscillatory responses, which was more evident in the high-learners. Altogether, our results suggest an important role of the motor areas in the processing of error feedback for skill consolidation.

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© 2014 Massachusetts Institute of Technology

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May 2014Published
31 March 2014Published Online

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16 Jul 2015 09:12

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31 Mar 2020 10:22

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Yes, this version has been peer-reviewed.


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