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Testing the Reward Prediction Error Hypothesis with an Axiomatic Model
Open accessAbstract
Neuroimaging studies typically identify neural activity correlated with the predictions of highly parameterized models, like the many reward prediction error (RPE) models used to study reinforcement learning. Identified brain areas might encode RPEs or, alternatively, only have activity correlated with RPE model predictions. Here, we use an alternate axiomatic approach rooted in economic theory to formally test the entire class of RPE models on neural data. We show that measurements of human neural activity from the striatum, medial prefrontal cortex, amygdala, and posterior cingulate cortex satisfy necessary and sufficient conditions for the entire class of RPE models. However, activity measured from the anterior insula falsifies the axiomatic model, and therefore no RPE model can account for measured activity. Further analysis suggests the anterior insula might instead encode something related to the salience of an outcome. As cognitive neuroscience matures and models proliferate, formal approaches of this kind that assess entire model classes rather than specific model exemplars may take on increased significance.
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“When participants attributed outcomes to the neutral committee (their own performance), MBD activity was, as has been previously reported,50”
From Two prefrontal-subcortical circuits estimate controllability and reflect its impact on learning (2026) · cites this paper as [51]
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“The VS ROI was based on Rutledge et al. (2010)9 nucleus accumbens ROI and was comprised of three 5 mm spheres for each hemisphere ([8 13 –3; 12 13 –8; 9 13 –7; –8 13 –3; –12 13 –8; –9 13 –7 ]).”
From Representation of aversive prediction errors in the human periaqueductal gray (2014) · cites this paper as Rutledge et al. (2010)
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Metadata
- Authors
- Rutledge, R. B., Dean, M., Caplin, A., & Glimcher, P. W.
- Affiliations
- New York University (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2010-10-06
- Volume
- 30
- Issue
- 40
- Pages
- 13525-13536
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC2957369
- OpenAlex
- W2035219495
- Cited by
- 244
- References
- 51
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 9 · 2023: 23 · 2022: 11 · 2021: 11 · 2020: 40 · 2019: 10 · 2018: 16 · 2017: 25 · 2016: 15 · 2015: 12 · 2014: 23 · 2013: 13 · 2012: 9
- FWCI
- 5.61
- 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)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institutes of Health (5r01ns054775-05)
- National Defense Science and Engineering Graduate
- National Institute on Aging (F31AG031656)
- National Institute of Neurological Disorders and Stroke (R01-NS-054775)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- reward prediction error, axiomatic model, reinforcement learning, model falsifiability, striatum, medial prefrontal cortex, amygdala, posterior cingulate cortex, anterior insula, neural coding, model comparison
- MeSH terms
- Adult, Algorithms, Behavior, Brain Mapping, Cognition, Female, Forecasting, Humans, Image Processing, Computer-Assisted, Functional Laterality, Learning, Male, Models, Neurological, Models, Psychological, Predictive Value of Tests, Regression Analysis, Reinforcement, Psychology, Reward, Prosencephalon
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