Computational Models of Performance Monitoring and Cognitive Control
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The medial prefrontal cortex (mPFC) has been the subject of intense interest as a locus of cognitive control. Several computational models have been proposed to account for a range of effects, including error detection, conflict monitoring, error likelihood prediction, and numerous other effects observed with single-unit neurophysiology, fMRI, and lesion studies. Here, we review the state of computational models of cognitive control and offer a new theoretical synthesis of the mPFC as signaling response–outcome predictions. This new synthesis has two interacting components. The first component learns to predict the various possible outcomes of a planned action, and the second component detects discrepancies between the actual and intended responses; the detected discrepancies in turn update the outcome predictions. This single construct is consistent with a wide array of performance monitoring effects in mPFC and suggests a unifying account of the cognitive role of medial PFC in performance monitoring.
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The right inferior frontal gyrus, insula, and MCG may comprise the core neural network of the supervisory attentional control system needed to implement a nondominant, context-dependent behavior against a competing behavioral alternative.
“Overall, we propose that the right inferior frontal gyrus, insula, and MCG may comprise the core neural network of the supervisory attentional control system needed to implement a nondominant, context-dependent behavior against a competing behavioral alternative (Alexander and Brown 2010; Cieslik et al. 2015).”
From Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis (2017) · cites this paper as Alexander and Brown 2010
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Alexander, W. H., & Brown, J. W. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.065 · Cited by 50 · see citing works
Self-citationBrown, J. W. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.034 · Cited by 49 · see citing works
Self-citationSteinhauser, M., Maier, M. E., & Hübner, R. (2008) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.34.1.158 · Cited by 48 · see citing works
Chadderdon, G. L., & Sporns, O. (2006) · Journal of Cognitive Neuroscience · doi:10.1162/089892906775783624 · Cited by 42 · see citing works
Anterior cingulate dysfunction during choice anticipation in schizophrenia
Quintana, J., Wong, T., Ortiz-Portillo, E., Marder, S. R., & Mazziotta, J. C. (2004) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2004.06.005 · Cited by 39 · see citing works
Neuronal activity related to rule and conflict in macaque supplementary eye field
OLSON, C., & Gettner, S. N. (2002) · Physiology & Behavior · doi:10.1016/s0031-9384(02)00945-9 · Cited by 38 · see citing works
Chadderdon, G. L., & Sporns, O. (2006) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2006.18.2.242 · Cited by 37 · see citing works
Computational and neural mechanisms of task switching
Reynolds, J. R., Braver, T. S., Brown, J. W., & der Stigchel, S. V. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.102 · Cited by 34 · see citing works
Self-citationTask switching and the pied homunculus: where are we being led?
Altmann, E. M. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(03)00169-4 · Cited by 28 · see citing works
Shifting Attention Using a Temporal Difference Prediction Error and High-Dimensional Input
Alexander, W. H. (2007) · Adaptive Behavior · doi:10.1177/1059712307078663 · Cited by 9 · see citing works
Self-citationRey, H. G., Lew, S. E., & Zanutto, B. S. (2007) · Springer eBooks · doi:10.1007/978-0-387-72256-6_17 · Cited by 5 · see citing works
Flagged first, then by citations
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The integration of negative affect, pain and cognitive control in the cingulate cortex
Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J., & Davidson, R. J. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn2994 · Cited by 2,082 · see citing works
Reasoning, Learning, and Creativity: Frontal Lobe Function and Human Decision-Making
Collins, A., & Koechlin, E. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001293 · Cited by 558 · see citing works
An Integrative Model of the Maturation of Cognitive Control
Luna, B., Marek, S., Larsen, B., Tervo-Clemmens, B., & Chahal, R. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-034054 · Cited by 550 · see citing works
Neurons in the frontal lobe encode the value of multiple decision variables
Kennerley, S. W., Dahmubed, A. F., Lara, A. H., & Wallis, J. D. (2009) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21100 · Cited by 453 · see citing works
Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis
Zhang, R., Geng, X., & Lee, T. M. C. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1443-x · Cited by 398 · see citing works
Three key regions for supervisory attentional control: Evidence from neuroimaging meta-analyses
Cieslik, E. C., Mueller, V. I., Eickhoff, C. R., Langner, R., & Eickhoff, S. B. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.11.003 · Cited by 322 · see citing works
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
Self-citationNee, D. E., Kastner, S., & Brown, J. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.027 · Cited by 139 · see citing works
Self-citationVahid, A., Mückschel, M., Stober, S., Stock, A.-K., & Beste, C. (2020) · Communications Biology · doi:10.1038/s42003-020-0846-z · Cited by 87 · see citing works
Sun, S., Yu, H., Yu, R., & Wang, S. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02625-w · Cited by 69 · see citing works
Butler, K., Klaus, K., Edwards, L., & Pennington, K. (2017) · Hormones and Behavior · doi:10.1016/j.yhbeh.2017.07.013 · Cited by 43 · see citing works
Hochman, E. Y., Orr, J. M., & Gehring, W. J. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs323 · Cited by 11 · see citing works
- Authors
- Alexander, W. H., & Brown, J. W.
- Affiliations
- Indiana University Bloomington (US)
- Corresponding author
- Brown, Joshua W.
- ORCID
- Brown, Joshua W.
- Venue
- Topics in Cognitive Science
- Publisher
- Wiley
- Published
- 2010-03-08
- Volume
- 2
- Issue
- 4
- Pages
- 658-677
- ISSN
- 1756-8757 (ISSN-L)
- PMC
- PMC3044326
- OpenAlex
- W2107220076
- Cited by
- 193
- References
- 125
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 7 · 2023: 14 · 2022: 12 · 2021: 9 · 2020: 9 · 2019: 13 · 2018: 13 · 2017: 20 · 2016: 13 · 2015: 14 · 2014: 19 · 2013: 12 · 2012: 12
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Topics in Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Drug Abuse (R01DA026457, R03DA023462)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- medial prefrontal cortex, cognitive control, performance monitoring, computational model, error detection, conflict monitoring, outcome prediction, discrepancy detection, prediction updating
- MeSH terms
- Attention, Goals, Humans, Models, Psychological, Psychomotor Performance, Prefrontal Cortex, Executive Function
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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