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Cited by 597Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology StudiesNeural dynamics and brain function

Dopamine reward prediction error coding

Open access
Schultz, Wolfram·2016·Dialogues in Clinical Neuroscience
DOI 10.31887/dcns.2016.18.1/wschultzPMC PMC4826767
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Abstract

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

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Crossref · 10.31887/dcns.2016.18.1/wschultz

Cites

37 of 37 references verified0 citation errors · 0 not checkable

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  • This learning facilitates formation of LTM cause/effect relations between needs and their aims/objects, yielding a reward prediction error cycle coding ongoing learning.

    “This facilitates the formation of LTM cause/effect relations between particular needs and their adequate aims and objects, which in turn yields the iterative “reward prediction error” cycle that codes ongoing learning from experience (see Schultz, 2016).”

    From The Hard Problem of Consciousness and the Free Energy Principle (2019) · cites this paper as Schultz, 2016

    In “Reward prediction errors for learning” (page unavailable), the paper explains that differences between predicted and received rewards update predictions and behavior through experience. It does not describe long-term-memory cause-and-effect relations between needs and their aims or objects, or say that forming those relations yields the reward prediction error cycle. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The reward prediction error, in which changes in dopamine firing encode differences between expected and actual reward outcomes, enables dynamic learning from experience and updates in reward expectations.

    “The reward prediction error, in which changes in dopamine firing encode differences between an expected vs. actual reward outcome, allows for dynamic learning from experience and updates in reward expectations (Gradin et al., 2011; Schultz, 2016).”

    From The effects of early life stress on reward processing (2018) · cites this paper as Schultz, 2016

  • Dopamine signaling to the basal ganglia, including the nucleus accumbens, contributes to the reward prediction error.

    “Dopamine signaling to the basal ganglia, including the nucleus accumbens, also plays a role in what is known as the reward prediction error, a subconstruct of approach motivation.”

    From The effects of early life stress on reward processing (2018) · cites this paper as Schultz, 2016

  • Dopaminergic neurons increase firing rate for unpredicted reward outcomes only, whereas expected rewards provoke no change in dopamine firing rate.

    “It was thus shown that dopaminergic neurons increase the firing rate for unpredicted reward outcomes only, whereas expected rewards provoke no change in dopamine firing rate.”

    From Circuit Mechanisms of Reward, Anhedonia, and Depression (2019) · cites this paper as Schultz, 2016

  • For aversive stimuli, dopaminergic neurons show decreased firing rates below baseline.

    “This is also true for aversive stimuli, with decreased firing rates of dopaminergic neurons below baseline.”

    From Circuit Mechanisms of Reward, Anhedonia, and Depression (2019) · cites this paper as Schultz, 2016

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Research works printed but not linked: 2

  1. [1]Pavlov PI. Conditioned Reflexes. London, UK: Oxford University Press: 1927
  2. [26]von Neumann J. Morgenstern O. The Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press: 1944

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Authors
Schultz, W.
Affiliations
University of Cambridge (GB)
Corresponding author
Schultz, Wolfram
ORCID
Schultz, Wolfram
Venue
Dialogues in Clinical Neuroscience
Publisher
Informa UK Limited
Published
2016-03-31
Volume
18
Issue
1
Pages
23-32
ISSN
  • 1294-8322 (ISSN-L)
  • 1958-5969
DOI
10.31887/dcns.2016.18.1/wschultz
PMC
PMC4826767
OpenAlex
W2419438565
Cited by
597
References
37
Citations by year
2026: 84 · 2025: 83 · 2024: 77 · 2023: 68 · 2022: 64 · 2021: 53 · 2020: 36 · 2019: 62 · 2018: 45 · 2017: 20 · 2016: 4
FWCI
24.37
Type
journal article
Language
en
Open access
Licence
Locations
  • Dialogues in Clinical Neuroscience (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Wellcome Trust (093875)
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
dopamine, reward prediction error, midbrain dopaminergic neurons, reward learning, drug addiction, neuroplasticity, striatum, amygdala, frontal cortex
MeSH terms
Animals, Brain, Dopamine, Forecasting, Humans, Learning, Nerve Net, Neuronal Plasticity, Reward

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