Evidence map›Paper›PMID 35114098›Full record

ArticleCurrent biology : CB2022

The role of state uncertainty in the dynamics of dopamine.

John G Mikhael, HyungGoo R Kim, Naoshige Uchida, Samuel J Gershman

Abstract read
In one paragraph

Article in Current biology : CB, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed.

  1. Sequence termination cues drive automated habit-like strategy via dopamine-mediated processes.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Article
  2. Article
  3. Article
  4. Memory erasure by dopamine-gated retrospective learning.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. A hardwired neural circuit for temporal difference learning.bioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Striatal Gradient in Value-Decay Explains Regional Differences in Dopamine Patterns and Reinforcement Learning Computations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Mesolimbic dopamine ramps reflect environmental timescales.bioRxiv : the preprint server for biology · 2025
    Article
  15. Article
  16. Review
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  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

John G MikhaelProgram in Neuroscience, Harvard Medical School, Boston, MA 02115, USA; MD-PhD Program, Harvard Medical School, Boston, MA 02115, USA. Electronic address: john_mikhael@hms.harvard.edu.
HyungGoo R KimCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Naoshige UchidaDepartment of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Samuel J GershmanDepartment of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.

Funding

Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
Towards a Unified Framework for Dopamine Signaling in the StriatumU19NS113201 · NINDS · HARVARD MEDICAL SCHOOL · PI WILBRECHT, LINDA E · 2019 to 2023
$18.7M
PHD TRAINING IN NEUROSCIENCET32MH020017 · NIMH · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI JOHN ASSAD · 1997 to 2026
$15.8M
Context dependent modulations of dopamine signalingR01MH110404 · NIMH · HARVARD UNIVERSITY · PI UCHIDA, NAOSHIGE · 2016 to 2020
$2.1M
Neural circuits that regulate dopamine neuron activityR01MH095953 · NIMH · HARVARD UNIVERSITY · PI UCHIDA, NAOSHIGE · 2012 to 2016
$2.1M
NIGMS NIH HHS T32 GM007753NIMH NIH HHS R01 MH095953NIMH NIH HHS R01 MH110404NIMH NIH HHS T32 MH020017NINDS NIH HHS U19 NS113201
6 · The paper itself

Abstract

Reinforcement learning models of the basal ganglia map the phasic dopamine signal to reward prediction errors (RPEs). Conventional models assert that, when a stimulus predicts a reward with fixed delay, dopamine activity during the delay should converge to baseline through learning. However, recent studies have found that dopamine ramps up before reward in certain conditions even after learning, thus challenging the conventional models. In this work, we show that sensory feedback causes an unbiased learner to produce RPE ramps. Our model predicts that when feedback gradually decreases during a trial, dopamine activity should resemble a "bump," whose ramp-up phase should, furthermore, be greater than that of conditions where the feedback stays high. We trained mice on a virtual navigation task with varying brightness, and both predictions were empirically observed. In sum, our theoretical and experimental results reconcile the seemingly conflicting data on dopamine behaviors under the RPE hypothesis.

Indexed as

DopamineRewardAnimalsLearningMiceReinforcement, PsychologyUncertaintyDopaminebumpsdopaminerampsreinforcement learningreward prediction errorsensory feedbackstate uncertaintystate value

Identifiers

PMID35114098
PMCPMC8930519

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.