Decodability of Reward Learning Signals Predicts Mood Fluctuations

Eran Eldar*, Charlotte Roth, Peter Dayan, Raymond J. Dolan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Our mood often fluctuates without warning. Recent accounts propose that these fluctuations might be preceded by changes in how we process reward. According to this view, the degree to which reward improves our mood reflects not only characteristics of the reward itself (e.g., its magnitude) but also how receptive to reward we happen to be. Differences in receptivity to reward have been suggested to play an important role in the emergence of mood episodes in psychiatric disorders [1–16]. However, despite substantial theory, the relationship between reward processing and daily fluctuations of mood has yet to be tested directly. In particular, it is unclear whether the extent to which people respond to reward changes from day to day and whether such changes are followed by corresponding shifts in mood. Here, we use a novel mobile-phone platform with dense data sampling and wearable heart-rate and electroencephalographic sensors to examine mood and reward processing over an extended period of one week. Subjects regularly performed a trial-and-error choice task in which different choices were probabilistically rewarded. Subjects’ choices revealed two complementary learning processes, one fast and one slow. Reward prediction errors [17, 18] indicative of these two processes were decodable from subjects’ physiological responses. Strikingly, more accurate decodability of prediction-error signals reflective of the fast process predicted improvement in subjects’ mood several hours later, whereas more accurate decodability of the slow process’ signals predicted better mood a whole day later. We conclude that real-life mood fluctuations follow changes in responsivity to reward at multiple timescales. In a week-long smartphone experiment, Eldar et al. show that reward-prediction errors indicative of fast and slow reward-learning processes can be decoded from EEG and heart-rate signals. Moreover, fast and slow mood fluctuations are predicted by how well fast and slow learning can be decoded—positive mood changes follow greater decodabilities.

Original languageAmerican English
Pages (from-to)1433-1439.e7
JournalCurrent Biology
Volume28
Issue number9
DOIs
StatePublished - 7 May 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 The Author(s)

Keywords

  • ecological momentary assessment
  • mood
  • prediction errors
  • reinforcement learning
  • reward
  • wearable sensors

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