Neural correlates of risk prediction error during reinforcement learning in humans
Open accessAbstract
Behavioral studies have shown for decades that humans are sensitive to risk when making decisions. More recently, brain activities have been shown to be correlated with risky choices. But an important gap needs to be filled: How does the human brain learn which decisions are risky? In cognitive neuroscience, reinforcement learning has never been used to estimate reward variance, a common measure of risk in economics and psychology. It is thus unknown which brain regions are involved in risk learning. To address this question, participants completed a decision-making task during fMRI. They chose repetitively from four decks of cards and each selection was followed by a stochastic payoff. Expected reward and risk differed among the decks. Participants' aim was to maximize payoffs. Risk and reward prediction errors were calculated after each payoff based on a novel reinforcement learning model. For reward prediction error, the strongest correlation was found with the BOLD response in the striatum. For risk prediction error, the strongest correlation was found with the BOLD responses in the insula and inferior frontal gyrus. We conclude that risk and reward prediction errors are processed by distinct neural circuits during reinforcement learning. Additional analyses revealed that the BOLD response in the inferior frontal gyrus was more pronounced for risk aversive participants, suggesting that this region also serves to inhibit risky choices.
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During performance of the Iowa gambling task, AIC responses reflect risk prediction error while striatal responses reflect reward prediction errors.
“during performance of the Iowa gambling task, AIC responses reflect risk prediction error while striatal responses reflect reward prediction errors”
From An Interoceptive Predictive Coding Model of Conscious Presence (2012) · cites this paper as d'Acremont et al., 2009
The available passages from the indexed copy are publication metadata, references, and a ‘Recent citations’ section, not this paper’s task description or results. Searches for the Iowa gambling task, anterior insular cortex (AIC) risk prediction error, and striatal reward prediction error found no passage establishing the attributed findings; the Iowa gambling task appears only in a cited reference. (LLM, conf=0.72)
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- Authors
- d'Acremont, M., Lu, Z.-L., Li, X., Van der Linden, M., & Bechara, A.
- Affiliations
- University of Southern California (US)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-10
- Volume
- 47
- Issue
- 4
- Pages
- 1929-1939
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2076705444
- Cited by
- 94
- References
- 67
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 6 · 2022: 3 · 2021: 6 · 2020: 1 · 2019: 3 · 2018: 6 · 2017: 5 · 2016: 7 · 2015: 9 · 2014: 5 · 2013: 10 · 2012: 8
- FWCI
- 4.02
- Type
- journal article
- Language
- en
- Open access
- green
- orbi.uliege.be
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- Open Repository and Bibliography (University of Liège) (submittedVersion, other-oa)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- National Institute of Neurological Disorders and Stroke (P01 NS019632)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- reward prediction error, reinforcement learning, risk aversion, risky decision making, striatum, insula, inferior frontal gyrus, reward variance, BOLD signal
- MeSH terms
- Brain, Choice Behavior, Female, Humans, Magnetic Resonance Imaging, Male, Reinforcement, Psychology, Risk-Taking, Task Performance and Analysis, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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