Key result
Accurate decoding of reward prediction-error signals predicts mood improvement hours to a day later.
Why the study?
The relationship between reward processing and daily fluctuations of mood has yet to be tested directly, including whether changes in reward responsivity precede mood shifts.
Does the decodability of reward learning signals from physiological responses predict daily mood fluctuations?
Observational
Does the decodability of reward learning signals from physiological responses predict daily mood fluctuations?
Real-life mood fluctuations follow changes in responsivity to reward at multiple timescales, which can be decoded from physiological responses.
Answers are grounded in this paper and cite their sources.
May inform early mood episode detection; leaves open whether targeting reward receptivity alters psychiatric trajectories.
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.
No takes yet. Share an insight, caveat, or question.
Eldar et al. (2018) conducted an observational in Mood fluctuations. Reward prediction-error signals (fast and slow processes) was evaluated on Mood fluctuations. More accurate decodability of fast reward prediction-error signals predicted mood improvement several hours later, while slow process signals predicted better mood a day later.
Synapse has enriched 10 related papers. Consider them for comparative context: