The DOI matches its Crossref record; 284 of the 284 references it cites were found in a registry or an index.
Predictive Reward Signal of Dopamine Neurons
Abstract
The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest that midbrain dopamine systems are involved in processing reward information and learning approach behavior. Most dopamine neurons show phasic activations after primary liquid and food rewards and conditioned, reward-predicting visual and auditory stimuli. They show biphasic, activation-depression responses after stimuli that resemble reward-predicting stimuli or are novel or particularly salient. However, only few phasic activations follow aversive stimuli. Thus dopamine neurons label environmental stimuli with appetitive value, predict and detect rewards and signal alerting and motivating events. By failing to discriminate between different rewards, dopamine neurons appear to emit an alerting message about the surprising presence or absence of rewards. All responses to rewards and reward-predicting stimuli depend on event predictability. Dopamine neurons are activated by rewarding events that are better than predicted, remain uninfluenced by events that are as good as predicted, and are depressed by events that are worse than predicted. By signaling rewards according to a prediction error, dopamine responses have the formal characteristics of a teaching signal postulated by reinforcement learning theories. Dopamine responses transfer during learning from primary rewards to reward-predicting stimuli. This may contribute to neuronal mechanisms underlying the retrograde action of rewards, one of the main puzzles in reinforcement learning. The impulse response releases a short pulse of dopamine onto many dendrites, thus broadcasting a rather global reinforcement signal to postsynaptic neurons. This signal may improve approach behavior by providing advance reward information before the behavior occurs, and may contribute to learning by modifying synaptic transmission. The dopamine reward signal is supplemented by activity in neurons in striatum, frontal cortex, and amygdala, which process specific reward information but do not emit a global reward prediction error signal. A cooperation between the different reward signals may assure the use of specific rewards for selectively reinforcing behaviors. Among the other projection systems, noradrenaline neurons predominantly serve attentional mechanisms and nucleus basalis neurons code rewards heterogeneously. Cerebellar climbing fibers signal errors in motor performance or errors in the prediction of aversive events to cerebellar Purkinje cells. Most deficits following dopamine-depleting lesions are not easily explained by a defective reward signal but may reflect the absence of a general enabling function of tonic levels of extracellular dopamine. Thus dopamine systems may have two functions, the phasic transmission of reward information and the tonic enabling of postsynaptic neurons.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Moreover, AL is, according to all definitions, “predictive.” Because the CS (e.g., the sound of a buzzer) repeatedly precedes the US (e.g., the smell of food), it comes to “predict” the response (salivation). The extent to which the organism anticipates the reinforcement when the CS occurs influences the strength of learning. Only unanticipated events lead to learning. The greater the difference”
From The Transition to Minimal Consciousness through the Evolution of Associative Learning (2016) · cites this paper as Schultz, 1998
In the citing paper
“Previous literature suggests that dopaminergic neuron firing (Schultz, 1998) and dopamine release in the medial NAc core (Kutlu et al., 2022) in response to novel sensory events habituates as the stimulus becomes familiar.”
From Ventral striatum dopamine release encodes unique properties of visual stimuli in mice (2023) · cites this paper as Schultz, 1998
In the citing paper
“Although it was originally postulated that the DA released from the VTA only mediates the hedonic or pleasurable effects of natural reinforcers, it is now clear that changes in DAergic activity can also signal punishment, aversion, or lack of expected reward [53–58].”
From Mechanistic insights into nicotine withdrawal (2011) · cites this paper as [53]
In the citing paper
“With repeated exposure, the DA neurons stop firing when receiving the food and fire instead when exposed to the stimulus that predicts food delivery (33).”
From The Addictive Dimensionality of Obesity (2013) · cites this paper as [33]
In the citing paper
“These responses constitute biological implementations of the crucial error term for reinforcement learning according to the Rescorla–Wagner model and temporal difference reinforcement models73; such a signal is appropriate for mediating learning and updating of reward predictions for approach behaviour and economic decisions4.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [4]
In the citing paper
“Experiments with monkeys and humans have shown that these neurons encode a prediction error, the difference between the reinforcer expected and the reinforcer obtained within a trial (O'Doherty et al., 2004; Schultz, 1998).”
From DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS (2005) · cites this paper as Schultz, 1998
In the citing paper
“A seminal finding was that phasic activity of dopamine neurons in the ventral tegmental area (VTA) encodes a reward prediction error (the difference between the expected and actual reward) (Schultz et al., 1997; Schultz, 1998; Roesch et al., 2007).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Schultz, 1998
In the citing paper
“Fast phasic events occur in response to reward-related events, which may serve as important teaching signals for error detection and modulate behavioral changes in response to the environment (Schultz 1998).”
From Developmental imaging genetics: Linking dopamine function to adolescent behavior (2014) · cites this paper as Schultz 1998
In the citing paper
“Slow changes in tonic levels of DA may be a preparatory mechanism for an organism to respond to environmental cues associated with reward (Schultz 1998).”
From Developmental imaging genetics: Linking dopamine function to adolescent behavior (2014) · cites this paper as Schultz 1998
In the citing paper
“Most dopaminergic neurons within the core reward circuitry, particularly in the VS, show short bursts of phasic activation in response to reward and, after learning, in response to conditioned cues that signal a potential reward [13].”
From Social “wanting” dysfunction in autism: neurobiological underpinnings and treatment implications (2012) · cites this paper as [13]
Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
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-citationParallel organization of functionally segregated circuits linking basal ganglia and cortex
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.09.030186.002041 · Cited by 8,527
The 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
Model predictive control: Theory and practice—A survey
García, C. E., Prett, D. M., & Morari, M. (1989) · Automatica · doi:10.1016/0005-1098(89)90002-2 · Cited by 5,392
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
(1991) · Springer eBooks · doi:10.1007/978-3-7091-2840-4 · Cited by 5,092
Skinner, B. F., & Hull, C. L. (1944) · The American Journal of Psychology · doi:10.2307/1416955 · Cited by 4,061
WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
Flagged first, then by citations
Research works printed but not linked: 8
- Contreras-Vidal J. L., 1996, Soc. Neurosci. Abstr., 22, 2029
- Hollerman J. R., 1996, Soc. Neurosci. Abstr., 22, 1388
- Hollerman J. R., 1994, Soc. Neurosci. Abstr., 20, 780
- Linden A., 1990, J. Psychophysiol., 4, 145
- 10.1037/0735-7044.105.2.295 · DOI
- Stein L., 1964, Federation Proc., 23, 836
- Suri R. E., 1996, Soc. Neurosci. Abstr., 22, 1389
- Tremblay L., 1995, Soc. Neurosci. Abstr., 21, 952
Cited by
Sorted by citations
Items 401–407 of 407
Biback, C., & Zack, M. (2015) · Current Addiction Reports · doi:10.1007/s40429-015-0064-9 · Cited by 20
Saji, K., Ikeda, Y., Kim, W., Shingai, Y., Tateno, A., Takahashi, H., Okubo, Y., Fukayama, H., & Suzuki, H. (2013) · International Journal of Neuropsychopharmacology · doi:10.1017/s1461145712001678 · Cited by 19
Udo de Haes, J. I., Kortekaas, R., Van Waarde, A., Maguire, R. P., Pruim, J., & den Boer, J. A. (2005) · Psychopharmacology · doi:10.1007/s00213-005-0193-2 · Cited by 18
Ventral striatum dopamine release encodes unique properties of visual stimuli in mice
Gonzalez, L. S., Fisher, A. A., D'Souza, S. P., Cotella, E. M., Lang, R. A., & Robinson, J. E. (2023) · eLife · doi:10.7554/elife.85064 · Cited by 18
Implications of Cellular Models of Dopamine Neurons for Schizophrenia
Yu, N., Tucker, K. R., Levitan, E. S., Shepard, P. D., & Canavier, C. C. (2014) · Progress in Molecular Biology and Translational Science · doi:10.1016/b978-0-12-397897-4.00011-5 · Cited by 17
Addressing altered anticipation as a transdiagnostic target through computational psychiatry
Sepúlveda, P., Aitsahalia, I., Kumar, K., Atkin, T., & Iigaya, K. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.02.014 · Cited by 3
When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education
Wang, P., Viberg, O., Law, E. L.-C., & Greiff, S. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10202-y · Cited by 3
Metadata
- Authors
- Schultz, W.
- Affiliations
- University of Fribourg (CH)
- Corresponding author
- Schultz, Wolfram
- ORCID
- Schultz, Wolfram
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1998-07-01
- Volume
- 80
- Issue
- 1
- Pages
- 1-27
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2109059823
- Cited by
- 4,564
- References
- 284
- Citations by year
- 2026: 115 · 2025: 132 · 2024: 156 · 2023: 136 · 2022: 164 · 2021: 165 · 2020: 231 · 2019: 167 · 2018: 168 · 2017: 162 · 2016: 223 · 2015: 208 · 2014: 228 · 2013: 205 · 2012: 223
- FWCI
- 20.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- 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 › Memory and Neural Mechanisms
- Keywords
- dopaminergic neurons, reward prediction error, midbrain dopamine system, reinforcement learning, approach behavior, tonic dopamine, norepinephrine, nucleus basalis of Meynert, climbing fibers, aversive stimuli
- MeSH terms
- Animals, Brain, Dopamine, Humans, Learning, Models, Neurological, Neurons, Reward, Signal Transduction
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review