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Cited by 1,133Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology StudiesReinforcement Learning in Robotics

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

Niv, Yael, Daw, Nathaniel D., Joel, Daphna, Dayan, Peter·2007·Psychopharmacology
DOI 10.1007/s00213-006-0502-4
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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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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
ORCID
Venue
Psychopharmacology
Publisher
Springer Science and Business Media LLC
Published
2006-10-10
Volume
191
Issue
3
Pages
507-520
ISSN
  • 0033-3158 (ISSN-L)
  • 1432-2072
DOI
10.1007/s00213-006-0502-4
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

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