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Response-based outcome predictions and confidence regulate feedback processing and learning.

Romy Frömer1,2, Matthew R Nassar2, Rasmus Bruckner3,4,5

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Learning is enhanced when surprise is calibrated using internal performance monitoring, including outcome predictions and confidence. This research shows how online predictions improve learning efficiency by adjusting neural error signals.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Learning Sciences

Background:

  • Learning theories highlight surprise and new information from feedback.
  • Internal monitoring of performance, including predictions and confidence, may influence learning.
  • The role of online performance evaluation in calibrating learning from feedback is underexplored.

Purpose of the Study:

  • To investigate how response-based performance monitoring (outcome predictions and confidence) controls learning from feedback.
  • To test the hypothesis that surprise, and thus learning, is modulated by online performance evaluation.
  • To examine the relationship between prediction accuracy, confidence calibration, and learning speed.

Main Methods:

  • Behavioral experiments measuring learning speed and confidence calibration.
  • Analysis of electroencephalography (EEG) signatures related to feedback processing.
  • Comparison of empirical findings with predictions from a Bayesian inference model.

Main Results:

  • Individuals better at calibrating confidence to outcome prediction precision learned more quickly.
  • EEG data revealed that feedback processing is sensitive to the accuracy and confidence of outcome predictions.
  • Results align with a Bayesian inference model of learning.

Conclusions:

  • Online predictions and confidence play a crucial role in calibrating learning.
  • Internal performance monitoring refines neural error signals, enhancing learning efficiency.
  • Surprise is not solely based on past experience but also on real-time performance evaluation.