Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain
Open accessAbstract
Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric–psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms.
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Individual risk sensitivity can be assessed by using different learning rates for positive and negative RPEs when risk or outcome variance is not explicitly available.
“In a seminal paper by Niv et al. (2012), the authors showed that tasks, where risk or outcome variance is not explicitly available, individual risk sensitivity can be assessed by using different learning rates for positive and negative RPEs.”
From Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development (2015) · cites this paper as Niv et al., 2012
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- Authors
- Niv, Y., Edlund, J. A., Dayan, P., & O'Doherty, J. P.
- Affiliations
- Princeton University (US)
- California Institute of Technology (US)
- Gatsby Computational Neuroscience Unit (GB)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2012-01-11
- Volume
- 32
- Issue
- 2
- Pages
- 551-562
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6621075
- OpenAlex
- W2033271727
- Cited by
- 356
- References
- 71
- Citations by year
- 2026: 19 · 2025: 33 · 2024: 24 · 2023: 31 · 2022: 32 · 2021: 40 · 2020: 24 · 2019: 24 · 2018: 24 · 2017: 23 · 2016: 16 · 2015: 19 · 2014: 21 · 2013: 20 · 2012: 6
- FWCI
- 10.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc-sa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Gordon and Betty Moore Foundation
- Gatsby Charitable Foundation
- Hebrew University of Jerusalem
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- risk sensitivity, reinforcement learning, reward prediction error, nucleus accumbens, BOLD signal, risk aversion, decision making under risk, affective learning
- MeSH terms
- Adolescent, Brain, Choice Behavior, Conditioning, Classical, Female, Humans, Learning, Male, Models, Psychological, Nucleus Accumbens, Predictive Value of Tests, Reinforcement, Psychology, Reward, Risk-Taking, Young Adult
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