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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Neurons in MBD nuclei encode reward prediction errors.
“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]
Reward-related BOLD signals should maintain their sensitivity over a large range of reward values.
“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]
Reward-related responses adapt to the context in which a reward is presented, depending on a combination of the overall expected value and their variance rather than the absolute magnitude of reward value.
“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]
Both reward magnitude and reward probability are coded relatively rather than absolutely.
“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]
Activity in individual neurons in both habenula and VTA reflects both the value of a stimulus in terms of the reward that it predicts and VoI.
“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]
Dopamine neurons in the ventral tegmental area have reward prediction error gain that varies with reward-value variance.
“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]
Spiking activity of dopamine neurons increases with both reward magnitude and probability.
“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
Research in non-human primates and humans indicates that differences in probability with which a cue predicts reward produce quantitative change in engagement of dopaminergic systems.
“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]
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Self-citation
- 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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