Dopamine signals for reward value and risk: basic and recent data
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BACKGROUND: Previous lesion, electrical self-stimulation and drug addiction studies suggest that the midbrain dopamine systems are parts of the reward system of the brain. This review provides an updated overview about the basic signals of dopamine neurons to environmental stimuli. METHODS: The described experiments used standard behavioral and neurophysiological methods to record the activity of single dopamine neurons in awake monkeys during specific behavioral tasks. RESULTS: Dopamine neurons show phasic activations to external stimuli. The signal reflects reward, physical salience, risk and punishment, in descending order of fractions of responding neurons. Expected reward value is a key decision variable for economic choices. The reward response codes reward value, probability and their summed product, expected value. The neurons code reward value as it differs from prediction, thus fulfilling the basic requirement for a bidirectional prediction error teaching signal postulated by learning theory. This response is scaled in units of standard deviation. By contrast, relatively few dopamine neurons show the phasic activation following punishers and conditioned aversive stimuli, suggesting a lack of relationship of the reward response to general attention and arousal. Large proportions of dopamine neurons are also activated by intense, physically salient stimuli. This response is enhanced when the stimuli are novel; it appears to be distinct from the reward value signal. Dopamine neurons show also unspecific activations to non-rewarding stimuli that are possibly due to generalization by similar stimuli and pseudoconditioning by primary rewards. These activations are shorter than reward responses and are often followed by depression of activity. A separate, slower dopamine signal informs about risk, another important decision variable. The prediction error response occurs only with reward; it is scaled by the risk of predicted reward. CONCLUSIONS: Neurophysiological studies reveal phasic dopamine signals that transmit information related predominantly but not exclusively to reward. Although not being entirely homogeneous, the dopamine signal is more restricted and stereotyped than neuronal activity in most other brain structures involved in goal directed behavior.
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The role of dopaminergic neurotransmission in the response to unpredicted or alerting sensory events is less well characterized.
“Less well characterized is the role of dopaminergic neurotransmission in the response to unpredicted or alerting sensory events, which may encourage investigation or prime motivated behavioral responses to these stimuli (Horvitz, 2000; Bromberg-Martin et al., 2010a; Schultz, 2010).”
From Ventral striatum dopamine release encodes unique properties of visual stimuli in mice (2023) · cites this paper as Schultz, 2010
While expectation of reward has received particular attention as a trigger for a dopamine response, other related cognitive-affective states are supported as well.
“While the expectation of reward has received particular attention as a trigger for a dopamine response (Schultz, 2013), other related cognitive-affective states are supported as well (Schultz, 2010).”
From Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning (2016) · cites this paper as Schultz, 2010
Occasional risks to expected rewards (challenge) can transiently dampen extant levels of reward-related dopamine but amplify the impact of subsequent positive (but not negative) cues, strengthening the learning effect.
“Occasional risks to expected rewards (i.e., challenge) can transiently dampen the extant levels of rewardrelated dopamine but amplify the impact of subsequent Fig. 3 Schematic of the OPTIMAL theory positive (but not negative) cues, strengthening the learning effect (Schultz, 2010(Schultz, , 2013)).”
From Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning (2016) · cites this paper as Schultz, 2010
The DAergic system, particularly the mesocorticolimbic pathway, is involved in drug addiction because DA modulates limbic and cortical sites for goal-directed behavior.
“The role of the DAergic system (in particular the mesocorticolimbic pathway) in drug addiction derives from DA’s ability to modulate many of the limbic and cortical sites involved in goal-directed behavior (Fig. 1) [18, 28–31].”
From Mechanistic insights into nicotine withdrawal (2011) · cites this paper as [31]
Once conditioning has occurred, DA signals act as a predictor of reward, incentivizing the animal to perform the behaviour that will result in consuming the expected reward.
“once conditioning has occurred, DA signals act as a predictor of reward (49), incentivizing the animal to perform the behaviour that will result in consuming the expected reward (drug or food).”
From Obesity and addiction: Neurobiological overlaps (2013) · cites this paper as [49]
DA signaling appears to code for a reward prediction error.
“This transition is conveyed through DA signaling, which appears to code for a 'reward prediction error' (Schultz 2010).”
From Food and Drug Reward: Overlapping Circuits in Human Obesity and Addiction (2011) · cites this paper as Schultz 2010
Some of these brain regions also serve to focus attention and promote DA-dependent attention and decision-making.
“In addition to mediating DA-related anxiety and habitual behaviour and learning (21,22), some of these brain regions also serve to focus attention and promote DA-dependent attention and decision-making (23).”
From Neuroendocrinology of Parental Response to Baby‐Cry (2011) · cites this paper as [23]
Decisions are assumed to track increased expected value, and social reward can influence value calculations.
“From a reward-learning perspective, decisions are assumed to track increased expected value (Niv & Montague, 2008; Schultz, 2010) and social reward can influence value calculations (Evans et al., 2011; Fehr, 2009).”
From Risk-taking and social exclusion in adolescence: neural mechanisms underlying peer influences on decision-making (2013) · cites this paper as Schultz, 2010
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- Authors
- Schultz, W.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Schultz, Wolfram
- ORCID
- Schultz, Wolfram
- Venue
- Behavioral and Brain Functions
- Published
- 2010
- Volume
- 6
- Issue
- 1
- Pages
- 24
- ISSN
- 1744-9081 (ISSN-L)
- PMC
- PMC2876988
- OpenAlex
- W2156581621
- Cited by
- 653
- References
- 70
- Citations by year
- 2026: 16 · 2025: 19 · 2024: 26 · 2023: 25 · 2022: 22 · 2021: 24 · 2020: 35 · 2019: 31 · 2018: 45 · 2017: 44 · 2016: 44 · 2015: 63 · 2014: 46 · 2013: 65 · 2012: 74
- FWCI
- 6.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Behavioral and Brain Functions (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Funders
- National Science Foundation
- Wellcome Trust (095495)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- 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, reward value, risk, prediction error, midbrain dopamine system, punishment, novelty, economic decision-making, expected value, single-unit recording
- MeSH terms
- Animals, Haplorhini, Behavior, Animal, Conditioning, Psychological, Dopamine, Neurons, Reward, Risk-Taking
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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