A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages
Open accessAbstract
Economic approaches to decision making assume that people attach values to prospective goods and act to maximize their obtained value. Neuroeconomics strives to observe these values directly in the brain. A widely used valuation term in formal learning and decision-making models is the reward prediction error: the value of an outcome relative to its expected value. An influential theory (Holroyd & Coles, 2002) claims that an electrophysiological component, feedback related negativity (FRN), codes a reward prediction error in the human brain. Such a component should be sensitive to both the prior likelihood of reward and its magnitude on receipt. A number of studies have found the FRN to be insensitive to reward magnitude, thus questioning the Holroyd and Coles account. However, because of marked inconsistencies in how the FRN is measured, a meaningful synthesis of this evidence is highly problematic. We conducted a meta-analysis of the FRN's response to both reward magnitude and likelihood using a novel method in which published effect sizes were disregarded in favor of direct measurement of the published waveforms themselves, with these waveforms then averaged to produce "great grand averages." Under this standardized measure, the meta-analysis revealed strong effects of magnitude and likelihood on the FRN, consistent with it encoding a reward prediction error. In addition, it revealed strong main effects of reward magnitude and likelihood across much of the waveform, indicating sensitivity to unsigned prediction errors or "salience." The great grand average technique is proposed as a general method for meta-analysis of event-related potential (ERP).
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ERP studies in healthy participants show that FRN/RewP amplitudes are modulated by reward expectancy and prediction error, and that learning-related changes in reward expectancy are reflected in the feedback-locked medial-frontal signal.
“ERP studies in healthy participants show that FRN/RewP amplitudes are modulated by reward expectancy and prediction error, and that learning-related changes in reward expectancy are reflected in the feedback-locked medial-frontal signal [128,130].”
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Luo, Q., & Qu, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071186 · Cited by 13 · see citing works
Electrophysiological Responses to Feedback during the Application of Abstract Rules
Walsh, M. M., & Anderson, J. R. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00454 · Cited by 9 · see citing works
Asymmetrical effects of frequent gains and frequent losses in a gambling task
Nittono, H., Otsuka, Y., & Ullsperger, P. (2008) · Neuroreport · doi:10.1097/wnr.0b013e32830c21a8 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Glimcher, P. W. (2009). Choice: Towards a standard back-pocket model. San Diego, CA: Elsevier Academic Press.
- Sutton, R. S., & Barto, A. G. (1998). Introduction to reinforcement learn- ing. Cambridge, MA: MIT Press.
Cited by
Sorted by citations
Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort
Vassena, E., Holroyd, C. B., & Alexander, W. H. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00316 · Cited by 140 · see citing works
Fradkin, I., Adams, R. A., Parr, T., Roiser, J. P., & Huppert, J. D. (2020) · Psychological Review · doi:10.1037/rev0000188 · Cited by 89 · see citing works
Vahedi, J., Bahic, A., Chaliani, I., Schilbach, L., Ciupka‐Schön, B., Bellebaum, C., Pietrowsky, R., & Peterburs, J. (2025) · Psychophysiology · doi:10.1111/psyp.70142 · Cited by 3 · see citing works
Chmiel, J., & Kładna, A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15155994
- Authors
- Sambrook, T. D., & Goslin, J.
- Affiliations
- University of Plymouth (GB)
- ORCID
- Sambrook, Thomas D.
- Venue
- Psychological Bulletin
- Published
- 2015
- Volume
- 141
- Issue
- 1
- Pages
- 213-235
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2012241920
- Cited by
- 411
- References
- 115
- Citations by year
- 2026: 37 · 2025: 51 · 2024: 34 · 2023: 41 · 2022: 33 · 2021: 41 · 2020: 37 · 2019: 34 · 2018: 36 · 2017: 29 · 2016: 26 · 2015: 12
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Psychological Bulletin (publishedVersion)
- PubMed (publishedVersion)
- PEARL (University of Plymouth) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- 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
- reward prediction error, feedback-related negativity, event-related potentials, reward magnitude, neuroeconomics, decision-making, salience, waveform averaging
- MeSH terms
- Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Humans, Reward, Feedback, Psychological
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 ›
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