The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Tonic dopamine: opportunity costs and the control of response vigor
Abstract
Rationale Dopamine neurotransmission has long been known to exert a powerful influence over the vigor, strength, or rate of responding. However, there exists no clear understanding of the computational foundation for this effect; predominant accounts of dopamine's computational function focus on a role for phasic dopamine in controlling the discrete selection between different actions and have nothing to say about response vigor or indeed the free-operant tasks in which it is typically measured.Objectives We seek to accommodate free-operant behavioral tasks within the realm of models of optimal control and thereby capture how dopaminergic and motivational manipulations affect response vigor.Methods We construct an average reward reinforcement learning model in which subjects choose both which action to perform and also the latency with which to perform it. Optimal control balances the costs of acting quickly against the benefits of getting reward earlier and thereby chooses a best response latency.Results In this framework, the long-run average rate of reward plays a key role as an opportunity cost and mediates motivational influences on rates and vigor of responding. We review evidence suggesting that the average reward rate is reported by tonic levels of dopamine putatively in the nucleus accumbens.Conclusions Our extension of reinforcement learning models to free-operant tasks unites psychologically and computationally inspired ideas about the role of tonic dopamine in striatum, explaining from a normative point of view why higher levels of dopamine might be associated with more vigorous responding.
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In the citing paper
“Tonic DA levels are largely independent of environmental triggers and may index an individual’s level of incentive-related drive (Niv et al, 2007; Willuhn, Wanat, Clark, & Phillips, 2010).”
From Incentive Motivation, Cognitive Control, and the Adolescent Brain: Is It Time for a Paradigm Shift? (2012) · cites this paper as Niv et al, 2007
In the citing paper
“Supportive evidence for this proposed interplay between tonic and phasic modes of DA activity and effects on behavior relies entirely on animal and computational models (Niv et al., 2007; Willuhn, 2010).”
From Incentive Motivation, Cognitive Control, and the Adolescent Brain: Is It Time for a Paradigm Shift? (2012) · cites this paper as Niv et al., 2007
In the citing paper
“These systems also interact as tonic DA activity regulates phasic signaling in an inhibitory fashion and phasic DA has been shown to enhance tonic activity (Niv, Daw et al. 2007).”
From Developmental imaging genetics: Linking dopamine function to adolescent behavior (2014) · cites this paper as Niv, Daw et al. 2007
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
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-citationPredictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience?
Berridge, K. C., & Robinson, T. E. (1998) · Brain Research Reviews · doi:10.1016/s0165-0173(98)00019-8 · Cited by 4,127
(2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,601
Dopamine, learning and motivation.
Wise, R. A. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1406 · Cited by 3,379 · cited 2×
“One possible explanation for these effects is that phasic signals transiently affect dopamine tone (Phillips and Wightman 2004; Roitman et al. 2004; Wise 2004), influencing vigor selection.”
Specious reward: A behavioral theory of impulsiveness and impulse control.
Ainslie, G. (1975) · Psychological Bulletin · doi:10.1037/h0076860 · Cited by 2,708
Herrnstein, R. J. (1970) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1970.13-243 · Cited by 2,611
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533
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Metadata
- Authors
- Niv, Y., Daw, N. D., Joel, D., & Dayan, P.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Gatsby Computational Neuroscience Unit (GB)
- Tel Aviv University (IL)
- Corresponding author
- Niv, Yael
- Venue
- Psychopharmacology
- Published
- 2006-10-10
- Volume
- 191
- Issue
- 3
- Pages
- 507-520
- ISSN
- 0033-3158 (ISSN-L)
- 1432-2072
- OpenAlex
- W2156952192
- Semantic Scholar
- e1625352c7b6e275ec9a63984a5af25a98032671
- Cited by
- 1,133
- References
- 116
- Citations by year
- 2026: 49 · 2025: 65 · 2024: 75 · 2023: 59 · 2022: 76 · 2021: 80 · 2020: 64 · 2019: 63 · 2018: 69 · 2017: 69 · 2016: 84 · 2015: 52 · 2014: 62 · 2013: 61 · 2012: 44
- FWCI
- 9.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Psychopharmacology (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Gatsby Charitable Foundation
- Hebrew University of Jerusalem
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- tonic dopamine, response vigor, opportunity cost, optimal control, average-reward reinforcement learning, response latency, nucleus accumbens, striatum, free-operant behavior, reward rate, motivation, phasic dopamine
- MeSH terms
- Animals, Brain, Choice Behavior, Conditioning, Operant, Dopamine, Models, Psychological, Motivation, Reaction Time, Reinforcement, Psychology, Reinforcement Schedule, Reward, Neurotransmitter Agents, Rats
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 ›
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