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Studying Food Reward and Motivation in Humans
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When theory and biology differ: The relationship between reward prediction errors and expectancy.

Chad C Williams1, Cameron D Hassall1, Robert Trska1

  • 1Centre for Biomedical Research, University of Victoria, Victoria, British Columbia, V8W 2Y2, Canada.

Biological Psychology
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Summary

This study reveals how the brain processes unexpected rewards using electroencephalography. The reward positivity brain signal shows a sigmoidal relationship with reward expectancy, impacting reinforcement learning theories.

Keywords:
DopamineFeedback-related negativityPrediction errorReinforcement learningRescorla-Wagner learning ruleReward positivity

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Reinforcement Learning

Background:

  • Learning relies on comparing expectations with outcomes, known as prediction errors.
  • Prediction errors are crucial for modifying value and guiding behavior.

Purpose of the Study:

  • To investigate how varying levels of expectancy violation affect neural signals during feedback processing.
  • To examine the relationship between reward expectancy and the reward positivity component of event-related potentials.

Main Methods:

  • Participants performed a time estimation task while electroencephalogram (EEG) was recorded.
  • Task difficulty was manipulated to create five levels of reward expectancy: very expected, expected, 50/50, unexpected, and very unexpected.
  • The amplitude of the reward positivity was measured in response to feedback.

Main Results:

  • The reward positivity amplitude scaled inversely with expectancy, with larger amplitudes for more unexpected feedback.
  • The relationship between reward expectancy and reward positivity amplitude was not linear.
  • Reward positivity amplitude was similar for very expected and expected feedback, and for very unexpected and unexpected feedback, demonstrating a sigmoidal pattern.

Conclusions:

  • The findings demonstrate a sigmoidal relationship between reward expectancy and reward positivity.
  • This sigmoidal pattern has significant implications for understanding reinforcement learning models.
  • The study highlights the nuanced neural processing of prediction errors.