Cited by 966Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology StudiesReinforcement Learning in Robotics

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Computational roles for dopamine in behavioural control

Montague, P. Read, Hyman, Steven E., Cohen, Jonathan D.·2004·Nature

DOI 10.1038/nature03015

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Abstract

Neuromodulators such as dopamine have a central role in cognitive disorders. In the past decade, biological findings on dopamine function have been infused with concepts taken from computational theories of reinforcement learning. These more abstract approaches have now been applied to describe the biological algorithms at play in our brains when we form value judgements and make choices. The application of such quantitative models has opened up new fields, ripe for attack by young synthesizers and theoreticians.

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An Integrative Theory of Prefrontal Cortex Function
Miller, E. K., & Cohen, J. D. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.167 · Cited by 12,803
Self-citation
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
Self-citation
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
The Psychology of Learning and Motivation: Advances in Research and Theory
(2010) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/c2009-0-62209-1 · Cited by 6,035
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Drugs abused by humans preferentially increase synaptic dopamine concentrations in the mesolimbic system of freely moving rats.
Di Chiara, G., & Imperato, A. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.14.5274 · Cited by 4,733
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
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
From Neuropsychology to Mental Structure
Shallice, T. (1988) · doi:10.1017/cbo9780511526817 · Cited by 4,072
The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity.
Holroyd, C. B., & Coles, M. G. H. (2002) · Psychological Review · doi:10.1037/0033-295x.109.4.679 · Cited by 4,027

Show all 77 references

Research works printed but not linked: 2

Rescorla, R. A. & Wagner A. R. in Classical Conditioning 2: Current Research and Theory (eds Black, A. H. & Prokasy, W. F.) 64–69 (Appleton Century-Crofts, New York, 1972).
Bayer, H. M. & Glimcher, P. W. Subjective estimates of objective rewards: using economic discounting to link behavior and brain. Soc. Neurosci. Abstr. 28, 358.6 (2002).

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Sailer, U., Robinson, S., Fischmeister, F. P. S., Moser, E., Kryspin‐Exner, I., & Bauer, H. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.012 · Cited by 24
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Feedback-related negativity reflects omission of monetary gains: Evidence from ERP gambling study
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Authors: Montague, P. R., Hyman, S. E., & Cohen, J. D.
Affiliations: Baylor College of Medicine (US)
Harvard University (US)
University of Pittsburgh (US)
ORCID: Montague, P. Read
Hyman, Steven E.
Cohen, Jonathan D.
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2004-10
Volume: 431
Issue: 7010
Pages: 760-767
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature03015
OpenAlex: W2165368112
Semantic Scholar: 16cd7a24d117f61da645fba1b4cc31839dff2805
Cited by: 966
References: 77
Citations by year: 2026: 6 · 2025: 13 · 2024: 19 · 2023: 36 · 2022: 21 · 2021: 26 · 2020: 32 · 2019: 31 · 2018: 39 · 2017: 40 · 2016: 42 · 2015: 49 · 2014: 51 · 2013: 56 · 2012: 82
FWCI: 4.14
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
UCL Discovery (University College London) (submittedVersion)
Topics: Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords: dopamine, neuromodulation, reinforcement learning, computational model, value judgments, decision-making, behavioral control, cognitive disorders
MeSH terms: Animals, Choice Behavior, Cognition, Dopamine, Humans, Learning, Models, Neurological, Schizophrenia, Substance-Related Disorders

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Computational roles for dopamine in behavioural control

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