Influential theories emphasize the importance of predictions in learning: we learn from feedback to the extent that it is surprising, and thus conveys new information. Here we explore the hypothesis that surprise depends not only on comparing current events to past experience, but also on online evaluation of performance via internal monitoring. Specifically, we propose that people leverage insights from response-based performance monitoring – outcome predictions and confidence – to control learning from feedback. In line with predictions from a Bayesian inference model, we find that people who are better at calibrating their confidence to the precision of their outcome predictions learn more quickly. Further in line with our proposal, EEG signatures of feedback processing are sensitive to the accuracy of, and confidence in, post-response outcome predictions. Taken together, our results suggest that online predictions and confidence serve to calibrate neural error signals to improve the efficiency of learning.
Scripts and source data for all analyses are available under https://github.com/froemero/Outcome-Predictions-and-Confidence-Regulate-Learning.
- Matthew R Nassar
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: The study was performed following the guidelines of the ethics committee of the department of Psychology at Humboldt University. Participants gave informed consent to the experiment and were remunerated with course credits or 8 € per hour.
- Tadeusz Wladyslaw Kononowicz, Cognitive Neuroimaging Unit, CEA DRF/Joliot, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, France
- Received: September 4, 2020
- Accepted: April 30, 2021
- Accepted Manuscript published: April 30, 2021 (version 1)
© 2021, Frömer et al.
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