The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
The Logic of Scientific Discovery.
Hutchison, T. W., & Popper, K. R. (1959) · Economica · doi:10.2307/2550489 · Cited by 7,629
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience?
Berridge, K. C., & Robinson, T. E. (1998) · Brain Research Reviews · doi:10.1016/s0165-0173(98)00019-8 · Cited by 4,127
The Logic of Scientific Discovery
Popper, K. R., & Weiss, G. (1959) · Physics Today · doi:10.1063/1.3060577 · Cited by 2,717
Wald, A. (1943) · Transactions of the American Mathematical Society · doi:10.1090/s0002-9947-1943-0012401-3 · Cited by 2,522
Dissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning
O'Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004) · Science · doi:10.1126/science.1094285 · Cited by 2,088
Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006) · Nature · doi:10.1038/nature05051 · Cited by 1,584
Temporal difference models and reward-related learning in the human brain
O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003) · Neuron · doi:10.1016/s0896-6273(03)00169-7 · Cited by 1,522
Two types of dopamine neuron distinctly convey positive and negative motivational signals
Matsumoto, M., & Hikosaka, O. (2009) · Nature · doi:10.1038/nature08028 · Cited by 1,378
Research works printed but not linked: 1
- Sutton RS Barto AG (1990) in Learning and computational neuroscience: foundations of adaptive networks, Time-derivative models of Pavlovian reinforcement, eds Gabriel M Moore J (MIT, Cambridge, MA), pp 497–537.
Cited by
Sorted by citations
Sescousse, G., Caldú, X., Segura, B., & Dreher, J.-C. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.02.002 · Cited by 782
Significance of the insula for the evolution of human awareness of feelings from the body
(Bud) Craig, A. D. (2011) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2011.05990.x · Cited by 646
Neurophysiology of performance monitoring and adaptive behavior
Ullsperger, M., Danielmeier, C., & Jocham, G. (2014) · Physiological Reviews · doi:10.1152/physrev.00041.2012 · Cited by 634
Inflammation Effects on Motivation and Motor Activity: Role of Dopamine
Felger, J. C., & Treadway, M. T. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.143 · Cited by 474
A computational and neural model of momentary subjective well-being
Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1407535111 · Cited by 444
Self-citation
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
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
