The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Adaptive Coding of Reward Value by Dopamine Neurons
Abstract
It is important for animals to estimate the value of rewards as accurately as possible. Because the number of potential reward values is very large, it is necessary that the brain's limited resources be allocated so as to discriminate better among more likely reward outcomes at the expense of less likely outcomes. We found that midbrain dopamine neurons rapidly adapted to the information provided by reward-predicting stimuli. Responses shifted relative to the expected reward value, and the gain adjusted to the variance of reward value. In this way, dopamine neurons maintained their reward sensitivity over a large range of reward values.
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In the citing paper
“Neurons in MBD nuclei are believed to encode reward prediction errors (RPEs).45”
From Two prefrontal-subcortical circuits estimate controllability and reflect its impact on learning (2026) · cites this paper as [45]
In the citing paper
“As is the case for dopamine neurons, 46 reward-related BOLD signals should maintain their sensitivity over a large range of reward values.”
From Increased neural responses to reward in adolescents and young adults with attention-deficit/hyperactivity disorder and their unaffected siblings (2015) · cites this paper as [46]
In the citing paper
“Indeed, reward-related responses adapt to the context in which a reward is presented 46, in the sense that they depend on a combination of the overall expected value and their variance rather than the absolute magnitude of reward value.”
From Increased neural responses to reward in adolescents and young adults with attention-deficit/hyperactivity disorder and their unaffected siblings (2015) · cites this paper as [46]
In the citing paper
“Yet, both reward magnitude and reward probability are coded relatively rather than absolutely. 46”
From Increased neural responses to reward in adolescents and young adults with attention-deficit/hyperactivity disorder and their unaffected siblings (2015) · cites this paper as [46]
In the citing paper
“Activity in individual neurons in both habenula and VTA reflects both the value of a stimulus in terms of the reward that it predicts58–60 as well as VoI21,22,27,61”
From Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering (2026) · cites this paper as [59]
In the citing paper
“For example, the gain of RPE signals produced by dopamine neurons in the ventral tegmental area is modulated by the variance of reward value.100,101”
From Serotonin neurons modulate learning rate through uncertainty (2022) · cites this paper as [101]
In the citing paper
“Consistent with a prediction error signal, spiking activity of dopamine neurons increases with both reward magnitude and probability (Fiorillo et al., 2003; Tobler et al., 2005; Roesch et al., 2007).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Tobler et al., 2005
In the citing paper
“Research in non-human primates, confirmed in humans, indicates that differences the probability with which a cue predicts a reward produce a quantitative change in the engagement of dopaminergic systems.46,47”
From Striatal sensitivity during reward processing in attention-deficit/hyperactivity disorder (2012) · cites this paper as [46]
Citations
Cites
Plum, F. (1960) · Archives of Neurology · doi:10.1001/archneur.1960.03840090124020 · Cited by 10,388
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-citationThe neural basis of drug craving: An incentive-sensitization theory of addiction
Robinson, T. (1993) · Brain Research Reviews · doi:10.1016/0165-0173(93)90013-p · Cited by 7,346
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
Self-citationClassical conditioning II: Current research and theory.
Black, A. H., & Prokasy, W. F. (1972) · Appleton-Century-Crofts eBooks · Cited by 4,449
Taussig, M. (1991) · Routledge eBooks · doi:10.4324/9780203397244 · Cited by 2,403
Discrete coding of reward probability and uncertainty by dopamine neurons
Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003) · Science · doi:10.1126/science.1077349 · Cited by 2,085
Self-citationNeurobehavioural mechanisms of reward and motivation
Robbins, T. W., & Everitt, B. J. (1996) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(96)80077-8 · Cited by 1,187
Animal intelligence; experimental studies
Thorndike, E. L. (1911) · doi:10.5962/bhl.title.55072 · Cited by 1,100
Predictive coding: a fresh view of inhibition in the retina
Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1982.0085 · Cited by 1,042
Research works printed but not linked: 3
- I. P. Pavlov Conditional Reflexes (Oxford Univ. Press Oxford 1927).
- H. B. Barlow, in Sensory Communication, W. A. Rosenblith, Ed. (MIT Press, Cambridge, MA, 1961), pp. 217–234.
- S. B. Laughlin, Z. Naturforsch. Teil C Biochem. Biophys. Biol. Virol.36, 910 (1981).
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BackgroundResultInfluentialSchiltz, C. A., Kelley, A. E., & Landry, C. F. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.03.002 · Cited by 18
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BackgroundCoppola, V. J., Spencer, J. M., Peterson, R. M., & Bingman, V. P. (2014) · Behavioural Brain Research · doi:10.1016/j.bbr.2013.11.038 · Cited by 20
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Zhang, X., Wang, M., Zhang, Y., Zhao, P., Jiamei, C., Yao, Y., & Liang, J. (2023) · Molecules · doi:10.3390/molecules28093646 · Cited by 17
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BackgroundMikuš, N., Eisenegger, C., Mathys, C. D., Clark, L., Müller, U., Robbins, T. W., Lamm, C., & Naef, M. (2023) · Nature Communications · doi:10.1038/s41467-023-39823-5 · Cited by 17
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Bertram, C., Dahan, L., Boorman, L. W., Harris, S., Vautrelle, N., Leriche, M., Redgrave, P., & Overton, P. G. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00329.2013 · Cited by 18
Siciliano, C. A., McIntosh, J. M., Jones, S. R., & Ferris, M. J. (2017) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.06.028 · Cited by 21
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Timmer, M. H. M., Aarts, E., Esselink, R. A. J., & Cools, R. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.14137 · Cited by 16
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Hori, Y., Nagai, Y., Mimura, K., Suhara, T., Higuchi, M., Bouret, S., & Minamimoto, T. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001055 · Cited by 16
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Shah, A. (2012) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-27645-3_16 · Cited by 17
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Rotermund, D., Ernst, U. A., & Pawelzik, K. R. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0083-7 · Cited by 17
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Schneider, R., & Kühl, J. (2012) · Medical Hypotheses · doi:10.1016/j.mehy.2012.02.022 · Cited by 18
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Burke, M. V., & Small, D. M. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.02.036 · Cited by 17
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Donahoe, J. W. (2012) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.2012.97-249 · Cited by 20
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BackgroundMethodResultInfluentialTian, Q., She, Y., Zhu, Y., Dai, D., Shi, M., Chu, W., Cai, T., Tsai, H., Li, H., Jiang, N., et al. (2023) · Sensors · doi:10.3390/s23052870 · Cited by 16
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BackgroundNeural correlates of recursive thinking during interpersonal strategic interactions
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Katz, A. C., Sarapas, C., Bishop, J. R., Patel, S., & Shankman, S. A. (2014) · Cognition & Emotion · doi:10.1080/02699931.2014.951030 · Cited by 15
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Noritake, A., & Nakamura, K. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00247.2018 · Cited by 15
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Aberg, K. C., Müller, J., & Schwartz, S. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00056 · Cited by 16
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Rose, J., Schiffer, A., & Güntürkün, O. (2012) · Neuroscience · doi:10.1016/j.neuroscience.2012.10.064 · Cited by 14
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Hong, S. Z., Kim, H. R., & Fiorillo, C. D. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00098 · Cited by 13
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Preuschoff, K., Quartz, S. R., & Bossaerts, P. L. (2008) · Revue d économie politique · doi:10.3917/redp.181.0075 · Cited by 14
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“In contrast to the increased dopaminergic transmission in the NAc during acute exposure to drugs, chronic drug use is linked with less rewarding effects which result from reduced DA levels (16, 17).”
BackgroundMultiple systems in macaques for tracking prediction errors and other types of surprise
Grohn, J., Schüffelgen, U., Neubert, F.-X., Bongioanni, A., Verhagen, L., Sallet, J., Kolling, N., & Rushworth, M. F. S. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000899 · Cited by 13
Socially relative reward valuation in the primate brain
Isoda, M. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.11.008 · Cited by 13
Chen, F., Gao, J., Zhu, C., Zhang, L., Zhang, L., Wang, A., Shi, B., Wang, K., & Yu, F. (2021) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2021.02.018 · Cited by 13
Aging and Neuroeconomics: Insights from Research on Neuromodulation of Reward-based Decision Making
Li, S., Biele, G., Mohr, P. N. C., & Heekeren, H. R. (2007) · Analyse & Kritik · doi:10.1515/auk-2007-0107 · Cited by 14
Altered Basolateral Amygdala Encoding in an Animal Model of Schizophrenia
Hernandez, A., Burton, A. C., O’Donnell, P., Schoenbaum, G. M., & Roesch, M. R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5096-14.2015 · Cited by 15
Developmental Behavioral Toxicity of Methylmercury: Consequences, Conditioning, and Cortex
Levin, E. D., & Buccafusco, J. J. (2006) · CRC Press eBooks · doi:10.1201/9781420004335-12 · Cited by 13
Mere Exposure: Preference Change for Novel Drinks Reflected in Human Ventral Tegmental Area
Ballard, I. C., Hennigan, K., & McClure, S. M. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01098 · Cited by 11
“In particular, dopamine neurons appear to track stimulus values in the absence of learning and adapt these values based on uncertainty (de Lafuente & Romo, 2011), temporal delay (Roesch, Calu, & Schoenbaum, 2007), and reward size (Tobler, 2005).”
BackgroundThe Anterior Insula Processes a Time-Resolved Subjective Risk Prediction Error
Kim, J.-C., Hellrung, L., Nebe, S., & Tobler, P. N. (2025) · The Journal of Neuroscience · doi:10.1523/jneurosci.2302-24.2025 · Cited by 12
“…from neutral predictions suggests that principles of adaptive coding may be at play not only in the domain of signed value prediction errors [e.g., dopamine neurons (Tobler et al., 2005); insula activity (Kirschner et al., 2018)] but also in the domain of unsigned value prediction errors.”
Self-citationFeng, Y.-Y., Bromberg-Martin, E. S., & Monosov, I. E. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114341 · Cited by 18
A distinct hypothalamus–habenula circuit governs risk preference
Groos, D., Reuss, A. M., Rupprecht, P., Stachniak, T. J., Lewis, C., Han, S., Roggenbach, A., Sturman, O., Sych, Y., Wieckhorst, M., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-024-01856-4 · Cited by 12
“In the vertebrate CNS, neuromodulatory regions in the midbrain and hindbrain and their projection targets are known to be crucial for value-guided behaviors 3,13–20 .”
Zamani, R., Ashrafi, H., Akhond, M., & Hemmateenejad, B. (2025) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2025.138084 · Cited by 12
What is the point of behavioural public policy? A contractarian approach
Nick Chater, A. (2022) · Behavioural Public Policy · doi:10.1017/bpp.2022.2 · Cited by 12
“…behave in ways that cannot serve as a scale of utility (having very limited precisions and dynamic ranges, and showing fast local adaptation, e.g., Tobler et al. , 2005). an inherently technocratic perspective: the clever and all-knowing technocrat is tasked with establishing what will maximise…”
Waking Up the Sleep Field: An Overview on the Implications of Genetics and Bioinformatics of Sleep
Pandi-Perumal, S. R., Saravanan, K. M., Paul, S., Pandian, G. N., & Chidambaram, S. B. (2024) · Molecular Biotechnology · doi:10.1007/s12033-023-01009-1 · Cited by 15
“It is a fundamental approach for which no known biochemical function exists, although it occurs in a genetically diverse range of eukaryotes from higher-to lower-order phyla, mammals to arthropods, respectively [29].”
Kurajoh, M., Kadoya, M., Morimoto, A., Naka, M., Miyoshi, A., Kanzaki, A., Kakutani‐Hatayama, M., Hamamoto, K., Shoji, T., Moriwaki, Y., et al. (2016) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2016.08.016 · Cited by 12
Placebo effect and therapeutic context: A challenge in clinical research
Morral, A., Urrútia, G., & Bonfill, X. (2017) · Medicina Clínica (English Edition) · doi:10.1016/j.medcle.2017.06.010 · Cited by 14
Wei, Z., Han, L., Zhong, X., Liu, Y., Zha, R., Wang, Y., Yang, L., Bu, J., Song, H., Wang, W., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.048 · Cited by 12
Effects of reward size and context on learning in macaque monkeys
Ferrucci, L., Nougaret, S., Brunamonti, E., & Genovesio, A. (2019) · Behavioural Brain Research · doi:10.1016/j.bbr.2019.111983 · Cited by 12
“same encoding between different tasks [17] or between different effort-level conditions [18]) but on the other hand, several studies described context-dependent reward effects in which a reward is represented in terms of its relative value in various structures of the so-called reward circuit, such as the striatum [19…”
BackgroundRange-Adaptive Value Representation in Different Stages of Schizophrenia: A Proof of Concept Study
Wang, L.-L., Lam, C. Y. T., Huang, J., Cheung, E. F. C., Lui, S. S. Y., & Chan, R. C. K. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab099 · Cited by 12
Apatía en la enfermedad de Parkinson
García-Ramos, R., Villanueva, C., del Val, J., & Matías‐Guiu, J. (2010) · Neurología · doi:10.1016/s0213-4853(10)70021-9 · Cited by 12
Learning Contextual Reward Expectations for Value Adaptation
Rigoli, F., Chew, B., Dayan, P., & Dolan, R. J. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01191 · Cited by 12
Hayes, D. J., Graham, D. A., & Greenshaw, A. J. (2008) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2008.12.023 · Cited by 12
Adaptive coding of pain prediction error in the anterior insula
Hoskin, R., & Talmi, D. (2023) · European Journal of Pain · doi:10.1002/ejp.2093 · Cited by 12
“For rewarding stimuli, adaptive coding is implemented through dopaminergic neuronal firing (Tobler et al., 2005).”
BackgroundMethodResultInfluential
Metadata
- Authors
- Tobler, P. N., Fiorillo, C. D., & Schultz, W.
- Affiliations
- University of Fribourg (CH)
- Corresponding author
- Fiorillo, Christopher D.
- Schultz, Wolfram
- Venue
- Science
- Published
- 2005-03-11
- Volume
- 307
- Issue
- 5715
- Pages
- 1642-1645
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2098534820
- Cited by
- 1,276
- References
- 24
- Citations by year
- 2026: 36 · 2025: 32 · 2024: 47 · 2023: 56 · 2022: 53 · 2021: 72 · 2020: 57 · 2019: 69 · 2018: 63 · 2017: 65 · 2016: 74 · 2015: 58 · 2014: 72 · 2013: 76 · 2012: 72
- FWCI
- 22.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- reroDoc Digital Library (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- Wellcome Trust (095495)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- dopaminergic neurons, adaptive coding, midbrain, reward prediction, gain modulation, variance, reward sensitivity, neural adaptation
- MeSH terms
- Adaptation, Physiological, Animals, Discrimination, Psychological, Dopamine, Electrophysiology, Female, Learning, Macaca fascicularis, Mesencephalon, Neurons, Photic Stimulation, Probability, Reward
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