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]
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Testing models of context-dependent outcome encoding in reinforcement learningPreprint
Hayes, W. M., & Wedell, D. H. (2022) · doi:10.31234/osf.io/3mn5u · Cited by 3
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Akam, T., & Walton, M. E. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/z2fmw · Cited by 4
Blocking D2/D3 dopamine receptors increases volatility of beliefs when we learn to trust othersPreprint
Mikuš, N., Eisenegger, C., Mathys, C. D., Clark, L., Müller, U., Robbins, T. W., Lamm, C., & Naef, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.21.496956 · Cited by 3
Kühnel, A., Zietz, J., Theuer, J. K., Neuser, M. P., Teckentrup, V., Müller, F. K., Dayan, P., Svaldi, J., & Kroemer, N. B. (2025) · medRxiv · doi:10.1101/2025.06.19.25329936 · Cited by 3
Nano optical (bio)sensing platforms for early detection of pediatric cancer
Hsu, C., Kumar, A., Kanjariya, P., Rajiv, A., Kashyap, A., Albert, H. M., Pokhriyal, S., & Panigrahi, R. (2025) · Microchimica Acta · doi:10.1007/s00604-025-07246-2 · Cited by 2
“Several illnesses, such as schizophrenia, parkinsonism, Alzheimer’s disease, different types of neuroblastoma, adrenocortical carcinoma, pheochromocytoma, and malignant tumors, can be linked to their abnormal concentration [139–142].”
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Kim, H., Xu, C., Washington, C., Shi, C., Lowman, M., & Kebschull, J. M. (2026) · Neuron · doi:10.1016/j.neuron.2026.05.005 · Cited by 6
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“In well-trained animals, dopamine neurons respond to cues to encode differences in rewardrelated information including reward size (Gan et al. 2010; Lefner et al. 2022; Roesch et al. 2007; Tobler et al. 2005), reward probability (Fiorillo et al.”
BackgroundNeuroscience of Social Understanding
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Reference-point dependent reinforcement learning in humans and rats
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“For example, the ‘ decoy ’ effect, in which preference between two options is shifted in the presence of a third (i.e., decoy), has been observed in humans 25 , bats 26 , hummingbirds 27 , starlings 16,18 , gray jays and honeybees 28 .”
Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks
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“Related observations have been made in the domain of value-based decision-making: dopamine neurons adapt their coding to the range of reward magnitudes 50 , and neurons in the orbitofrontal cortex dynamically adjust their response functions to represent economic value across different ranges 51 .”
Neuroeconomics—An Introduction
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Two prefrontal-subcortical circuits estimate controllability and reflect its impact on learning
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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
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.
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