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<jats:title>Abstract</jats:title><jats:p>Influential theories emphasize the importance of predictions in learning: We learn from response outcomes and feedback to the extent that they are surprising, and thus convey new information. Here we investigated how individuals learn to predict response outcomes based on the subjective confidence and objective accuracy with which these predictions are made. We hypothesized that both of these aspects modulate how feedback is processed and that they are reflected in event-related potentials (ERPs) as measured using EEG. Participants performed a time estimation task with graded, performance-contingent feedback. With this design we could distinguish reward prediction errors (RPE), indexing outcome valence with regard to the goal, and output prediction errors (OPE), indexing the absolute mismatch between predicted motor outcome and actual performance. As we expected, predictions made with higher confidence were more accurate (smaller OPE), and more so as learning progressed. Further, individuals with a better correspondence between confidence judgments and prediction accuracy learned more quickly. Outcome valence, as indexed by RPE was reflected in the feedback-related negativity (FRN). In contrast, P3a amplitude increased with OPE and confidence, that is with the degree of surprise about the outcome. Finally, performance-relevant information converged in the P3b component with confidence modulating RPE effects in early trials while learning took place. Taken together, the results underline the significance of different aspects of predictions and suggest a role of confidence in learning.</jats:p>

Original publication

DOI

10.1101/442822

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

14/10/2018