The DOI matches its Crossref record; 307 of the 307 references it cites were found in a registry or an index.
Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing
Open accessAbstract
Most reward-related electroencephalogram (EEG) studies focus exclusively on the feedback-related negativity (FRN, also known as feedback negativity or FN, medial-frontal negativity or MFN, feedback error-related negativity or fERN, and reward positivity or RewP). This component is usually measured approximately 200-300 ms post-feedback at a single electrode in the frontal-central area (e.g., Fz or FCz). The present review argues that this singular focus on the FRN fails to leverage EEG's greatest strength, its temporal resolution, by underutilizing the rich variety of event-related potential (ERP) and EEG time-frequency components encompassing the wider temporal heterogeneity of reward processing. The primary objective of this review is to provide a comprehensive understanding of often overlooked ERP and EEG correlates beyond the FRN in the context of reward processing with the secondary goal of guiding future research toward multistage experimental designs and multicomponent analyses that leverage the temporal power of EEG. We comprehensively review reward-related ERPs (including the FRN, readiness potential or RP, stimulus-preceding negativity or SPN, contingent-negative variation or CNV, cue-related N2 and P3, Feedback-P3, and late-positive potential or LPP/slow-wave), and reward-related EEG time-frequency components (changes in power at alpha, beta, theta, and delta bands). These electrophysiological signatures display distinct time-courses, scalp topographies, and reflect independent psychological processes during anticipatory and/or outcome stages of reward processing. Special consideration is given to the time-course of each component and factors that significantly contribute to component variation. Concluding remarks identify current limitations along with recommendations for potential important future directions.
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.
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
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Updating P300: An integrative theory of P3a and P3b
Polich, J. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.04.019 · Cited by 7,919
Carver, C. S., & White, T. L. (1994) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.67.2.319 · Cited by 7,306
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., & Turner, R. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5675 · Cited by 4,212
Holroyd, C. B., & Coles, M. G. H. (2002) · Psychological Review · doi:10.1037/0033-295x.109.4.679 · Cited by 4,027
Neural systems of reinforcement for drug addiction: from actions to habits to compulsion
Everitt, B. J., & Robbins, T. W. (2005) · Nature Neuroscience · doi:10.1038/nn1579 · Cited by 3,785
The reward circuit: linking primate anatomy and human imaging
Haber, S. N., & Knutson, B. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2009.129 · Cited by 3,718
Cited by
Pace-Schott, E. F., Amole, M. C., Aue, T., Balconi, M., Bylsma, L. M., Critchley, H., Demaree, H. A., Friedman, B. H., Gooding, A. E. K., Gosseries, O., et al. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.05.002 · Cited by 221
“Frequency based measures of EEG activity have not been used as extensively to study emotional responses, but there has been increasing interest in the utility of these measures, particularly in the area of emotional responses to rewarding stimuli (see (Glazer et al., 2018) for review).”
I feel what I do: Relating interoceptive processes and reward-related behavior
Marshall, A. C., Gentsch, A., Blum, A.-L., Broering, C., & Schütz-Bosbach, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.032 · Cited by 28
Allostatic-interoceptive anticipation of social rejection
Migeot, J., Hesse, E., Fittipaldi, S., Mejía, J., Fraile, M., García, A. M., García, M. D. C., Ortega, R., Lawlor, B., Lopez, V., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120200 · Cited by 15
Metadata
- Authors
- Glazer, J. E., Kelley, N. J., Pornpattananangkul, N., Mittal, V. A., & Nusslock, R.
- Venue
- International Journal of Psychophysiology
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 132
- Issue
- Pt B
- Pages
- 184-202
- ISSN
- 0167-8760 (ISSN-L)
- OpenAlex
- W2790096095
- Cited by
- 361
- References
- 307
- FWCI
- 13.14
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing. https://app.cochat.ai/papers/10.1016/j.ijpsycho.2018.02.002
- The paper
- Glazer, J. E., Kelley, N. J., Pornpattananangkul, N., Mittal, V. A., & Nusslock, R. (2018). Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing. International Journal of Psychophysiology. https://doi.org/10.1016/j.ijpsycho.2018.02.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.