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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“None declared.”
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InfluentialSelf-citationGreenberg, T., Chase, H. W., Almeida, J. R., Stiffler, R., Zevallos, C. R., Aslam, H. A., Deckersbach, T., Weyandt, S., Cooper, C., Toups, M., et al. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14050594 · Cited by 93
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Long-term dopamine neurochemical monitoring in primates
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“In addition, dopamine release varies with reward amount, and in the hippocampus, dopamine is known to promote synaptic potentiation [33, 34].”
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“To test the hypothesis that novelty and reward both express contextual adaptive scaling, we conducted two fMRI studies in healthy young adults using an adaptive reward coding paradigm adapted from a study in nonhuman primates [Tobler et al., 2005].”
BackgroundMethodResultInfluentiald’Acremont, M., & Bossaerts, P. L. (2008) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.8.4.363 · Cited by 98
Diversity and Homogeneity in Responses of Midbrain Dopamine Neurons
Fiorillo, C. D., Yun, S. R., & Song, M. R. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3886-12.2013 · Cited by 99
“The responses of dopamine neurons are highly sensitive to the reward prediction associated with a context, and it is the relative value of the stimuli that matters if they are presented in the same context (Tobler et al., 2005).”
BackgroundInfluentialSelf-citationDynamic routing of task-relevant signals for decision making in dorsolateral prefrontal cortex
Donahue, C. H., & Lee, D. (2015) · Nature Neuroscience · doi:10.1038/nn.3918 · Cited by 104
“While previous work has examined potentially different anatomical substrates for processing reward probability and magnitude information 20–26 , relatively little is known about how these signals are combined to calculate expected value.”
Baker, T. E., & Holroyd, C. B. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn223 · Cited by 100
Molecular mechanisms of placebo responses in humans
Peciña, M., & Zubieta, J.-K. (2014) · Molecular Psychiatry · doi:10.1038/mp.2014.164 · Cited by 103
Tyree, S. M., & de Lecea, L. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00050 · Cited by 103
“They found that as the size of the reward the animal receives increases, so does the DA neuron response, which was consistent with previous results (Tobler et al., 2005; Cohen et al., 2012), they also found that expectation of a reward resulted in a suppression of the DA neuron response, and that…”
ResultDopamine blockade impairs the exploration-exploitation trade-off in rats
Cinotti, F., Fresno, V., Aklil, N., Coutureau, É., Girard, B., Marchand, A. R., & Khamassi, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43245-z · Cited by 105
Neural basis of quasi-rational decision making
Lee, D. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.02.001 · Cited by 98
Predicting outcomes of decisions in the brain
Polezzi, D., Lotto, L., Daum, I., Sartori, G., & Rumiati, R. (2007) · Behavioural Brain Research · doi:10.1016/j.bbr.2007.09.001 · Cited by 99
Lex, B., & Hauber, W. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp151 · Cited by 100
“areas of DA fibers such as striatum and PL cortex, encode a prediction error (Tobler et al. 2005; Schultz 2007a), a teaching signal that may be critical for instrumental conditioning.”
BackgroundThe Price of Pain and the Value of Suffering
Vlaev, I., Seymour, B., Dolan, R. J., & Chater, N. (2009) · Psychological Science · doi:10.1111/j.1467-9280.2009.02304.x · Cited by 100
“, via dopamine projections to the striatum and the orbitofrontal cortex) strongly implicated in simple choice behavior (Nieuwenhuis et al., 2005; Tobler et al., 2005; Tremblay & Schultz, 1999).”
Learning Reward Uncertainty in the Basal Ganglia
Mikhael, J. G., & Bogacz, R. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005062 · Cited by 99
“For example, DA neurons were found to maintain their activity in classical conditioning in some studies [6, 7], while an increase was observed in others [48, 49].”
BackgroundStriatal sensitivity during reward processing in attention-deficit/hyperactivity disorder
Paloyelis, Y., Mehta, M. A., Faraone, S. V., Asherson, P., & Kuntsi, J. (2012) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2012.05.006 · Cited by 94
Limited Encoding of Effort by Dopamine Neurons in a Cost–Benefit Trade-off Task
Pasquereau, B., & Turner, R. S. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4619-12.2013 · Cited by 97
Cognitive and behavioral training interventions to promote self-control.
Smith, T. R., Panfil, K., Bailey, C., & Kirkpatrick, K. (2019) · Journal of Experimental Psychology Animal Learning and Cognition · doi:10.1037/xan0000208 · Cited by 104
Neural correlates of risk prediction error during reinforcement learning in humans
d'Acremont, M., Lu, Z.-L., Li, X., Van der Linden, M., & Bechara, A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.096 · Cited by 94
“Empirical data and theoretical considerations suggest that reward prediction error is scaled by predicted risk in the brain (Preushoff and Bossaerts, 2007; Tobler et al., 2005), allowing the learning rate to be”
Sucrose‐predictive cues evoke greater phasic dopamine release than saccharin‐predictive cues
McCutcheon, J. E., Beeler, J. A., & Roitman, M. F. (2011) · Synapse · doi:10.1002/syn.21519 · Cited by 92
“These signals are known to vary as a function of reward size, probability, delay, and ambiguity (Bromberg-Martin and Hikosaka, 2009; Day et al., 2010; Gan et al., 2010; Roesch et al., 2007; Tobler et al., 2005).”
BackgroundReward Prediction Error Coding in Dorsal Striatal Neurons
Oyama, K., Hernádi, I., Iijima, T., & Tsutsui, K. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.1719-10.2010 · Cited by 91
“In a previous study (Tobler et al., 2005), dopamine neurons were found to show even more drastic adaptation, such as to code the reward value according to the range of available rewards at the time: when medium or large amount of reward was available, dopamine neurons responded inhibitory to medium reward delivery, an…”
BackgroundZisner, A. R., & Beauchaine, T. P. (2016) · Development and Psychopathology · doi:10.1017/s0954579416000754 · Cited by 101
A quantitative reward prediction error signal in the ventral pallidum
Ottenheimer, D. J., Bari, B. A., Sutlief, E., Fraser, K. M., Kim, T. H., Richard, J. M., Cohen, J. Y., & Janak, P. H. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0688-5 · Cited by 82
Illicit dopamine transients: reconciling actions of abused drugs
Covey, D. P., Roitman, M. F., & Garris, P. A. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2014.02.002 · Cited by 90
Dopamine neurons learn relative chosen value from probabilistic rewards
Lak, A., Stauffer, W. R., & Schultz, W. (2016) · eLife · doi:10.7554/elife.18044 · Cited by 88
“Results from previous studies on fully established tasks suggest that the acquired dopamine responses to probabilistic rewards do not code reward probability on its own but rather increase monotonically with the statistical expected value (Fiorillo et al., 2003; Tobler et al., 2005).”
BackgroundResultSelf-citationDopamine Gates Action Potential Backpropagation in Midbrain Dopaminergic Neurons
Gentet, L. J., & Williams, S. R. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.5234-06.2007 · Cited by 91
“Indeed, a complex multiphase action potential waveform is considered a defining characteristic of midbrain dopaminergic neurons recorded in vivo (Guyenet and Aghajanian, 1978; Ruffieux and Schultz, 1980; Grace and Bunney, 1984a,b; Tobler et al., 2005).”
BackgroundThe role of learning-related dopamine signals in addiction vulnerability
Huys, Q. J. M., Tobler, P. N., Hasler, G., & Flagel, S. B. (2014) · Progress in brain research · doi:10.1016/b978-0-444-63425-2.00003-9 · Cited by 92
Self-citationTransient activation of midbrain dopamine neurons by reward risk
Fiorillo, C. D. (2011) · Neuroscience · doi:10.1016/j.neuroscience.2011.09.037 · Cited by 90
“, 2003), and there is also evidence that the transient response of dopamine neurons may be scaled by prior uncertainty about reward value, through what could be called “divisive normalization” (Tobler et al., 2005).”
BackgroundResultInfluentialSelf-citationCognitive reappraisal modulates expected value and prediction error encoding in the ventral striatum
Staudinger, M. R., Erk, S., Abler, B., & Walter, H. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.095 · Cited by 101
“As reported by Tobler et al. (2005), DA neurons adapt to the information provided by rewardpredicting stimuli: firing rates for receipt of a medium volume of liquid sugar depend on the alternative (high, when the alternative is a small volume; low, when the alternative is a large volume).”
Dopamine and the motivation of cognitive control
Cools, R., Froböse, M. I., Aarts, E., & Hofmans, L. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804281-6.00007-0 · Cited by 99
Gruber, J., Hanßen, R., Qubad, M., Bouzouina, A., Schack, V., Sochor, H., Schiweck, C., Aichholzer, M., Matura, S., Slattery, D. A., et al. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105179 · Cited by 96
Why We Should Use Animals to Study Economic Decision Making – A Perspective
Kalenscher, T., & van Wingerden, M. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00082 · Cited by 93
“So, what are the reasons for studying animal behavior when one is ultimately interested in theories of human economic decision making? Of course, monkeys and other animals are widely used in neuroeconomics and economically flavored neuroscience research, often for methodological reasons (see for instance Floresco et a…”
BackgroundBrown, J. W., & Braver, T. S. (2007) · Brain Research · doi:10.1016/j.brainres.2007.06.080 · Cited by 92
Reward Magnitude Coding in Primate Amygdala Neurons
Bermúdez, M. A., & Schultz, W. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00540.2010 · Cited by 88
Self-citation
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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