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 eventrelated 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. CONFIDENCE IN PREDICTIONS AND FEEDBACK regulation of the decision process both within and between individuals (Bahrami et al., 2010; Shea et al., 2014). Both of these forms of performance evaluationerror detection and confidence (Yeung & Summerfield, 2012)require sufficient knowledge about the task at hand.However, little is currently known about how people learn to make these evaluations as they master new skills, and this is particularly true in the context of tasks involving continuous response parameters-as in motor control, e.g., when throwing at a target-where errors are inevitable and graded, as compared with the simpler case of a binary, categorical choice.
CONFIDENCE IN PREDICTIONS AND FEEDBACK 4In the present study we used this framework of confidence and error detection as a form of outcome prediction to investigate how individuals learn to accurately evaluate responses along two key dimensions: 1) predicting action outcomesi.e., how people learn to accurately predict the direction and magnitude of a potential error, and 2) accurately indicating confidencei.e., how people learn to calibrate their confidence judgments such that higher confidence correlates with more accurate predictions. We further studied how these internal evaluations affect the encoding of feedback information as it is reflected in event-related potentials (ERPs) of the EEG.