Response-based outcome predictions and confidence regulate feedback processing and learning.
Response-based outcome predictions and confidence regulate feedback processing and learning.
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DOI：
10.7554/elife.62825
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发表时间：
2021-04-30
期刊：
影响因子：
7.7
通讯作者：
Yeung N
中科院分区：
文献类型：
--
作者：
Frömer R;Nassar MR;Bruckner R;Stürmer B;Sommer W;Yeung N
有影响力的理论强调预测在学习中的重要性：我们从反馈中学习到令人惊讶的程度，从而传达新信息。在这里，我们探讨了这样一个假设：惊喜不仅取决于当前事件与过去经验的比较，还取决于通过内部监控对绩效进行在线评估。具体来说，我们建议人们利用基于响应的绩效监控的见解（结果预测和置信度）来控制从反馈中学习。根据贝叶斯推理模型的预测，我们发现那些能够更好地根据结果预测的准确性调整信心的人学得更快。进一步根据我们的建议，反馈处理的脑电图签名对响应后结果预测的准确性和置信度敏感。综上所述，我们的结果表明，在线预测和置信度有助于校准神经误差信号，从而提高学习效率。
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.