Reward expectation modulates feedback-related negativity and EEG spectra

Michael X. Cohen, Christian E. Elger, Charan Ranganath

Research output: Contribution to journalArticle

334 Scopus citations

Abstract

The ability to evaluate outcomes of previous decisions is critical to adaptive decision-making. The feedback-related negativity (FRN) is an event-related potential (ERP) modulation that distinguishes losses from wins, but little is known about the effects of outcome probability on these ERP responses. Further, little is known about the frequency characteristics of feedback processing, for example, event-related oscillations and phase synchronizations. Here, we report an EEG experiment designed to address these issues. Subjects engaged in a probabilistic reinforcement learning task in which we manipulated, across blocks, the probability of winning and losing to each of two possible decision options. Behaviorally, all subjects quickly adapted their decision-making to maximize rewards. ERP analyses revealed that the probability of reward modulated neural responses to wins, but not to losses. This was seen both across blocks as well as within blocks, as learning progressed. Frequency decomposition via complex wavelets revealed that EEG responses to losses, compared to wins, were associated with enhanced power and phase coherence in the theta frequency band. As in the ERP analyses, power and phase coherence values following wins but not losses were modulated by reward probability. Some findings between ERP and frequency analyses diverged, suggesting that these analytic approaches provide complementary insights into neural processing. These findings suggest that the neural mechanisms of feedback processing may differ between wins and losses.

Original languageEnglish (US)
Pages (from-to)968-978
Number of pages11
JournalNeuroImage
Volume35
Issue number2
DOIs
StatePublished - Apr 1 2007

Keywords

  • Decision-making
  • EEG oscillations
  • ERP
  • Reinforcement learning
  • Reward prediction error

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

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