Evidence map›Paper›PMID 28441114›Full record

ReviewAnnual review of neuroscience2017

Neural Circuitry of Reward Prediction Error.

Mitsuko Watabe-Uchida, Neir Eshel, Naoshige Uchida

Abstract readReview
In one paragraph

Review in Annual review of neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 192 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
192citing papers in PubMed, 3 pooled it
–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

192 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Meta-analysis of human prediction error for incentives, perception, cognition, and action.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022
    Pooled it
  2. Pooled it
  3. The Signaling and Pharmacology of the Dopamine D1 Receptor.Frontiers in cellular neuroscience · 2021
    Pooled it
  4. Sequence termination cues drive automated habit-like strategy via dopamine-mediated processes.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Article
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  19. Dopamine signatures of excessive and compulsive cocaine and fentanyl use.bioRxiv : the preprint server for biology · 2026
    Article
  20. The Temporal Scaffolding of Sensory Organization.Annual review of psychology · 2026
    Review

132 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mitsuko Watabe-UchidaDepartment of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138; email: mitsuko@mcb.harvard.edu , uchida@mcb.harvard.edu.
Neir EshelDepartment of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138; email: mitsuko@mcb.harvard.edu , uchida@mcb.harvard.edu.
Naoshige UchidaDepartment of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138; email: mitsuko@mcb.harvard.edu , uchida@mcb.harvard.edu.

Funding

Medical Scientist Training ProgramT32GM007753 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI WALENSKY, LOREN DAVID · 1985 to 2021
$50.0M
Context dependent modulations of dopamine signalingR01MH110404 · NIMH · HARVARD UNIVERSITY · PI UCHIDA, NAOSHIGE · 2016 to 2020
$2.1M
Neural circuits for computing dopamine prediction errorsF30MH100729 · NIMH · HARVARD MEDICAL SCHOOL · PI ESHEL, NEIR · 2013 to 2014
$82k
NIGMS NIH HHS T32 GM007753NIMH NIH HHS F30 MH100729NIMH NIH HHS R01 MH110404
6 · The paper itself

Abstract

Dopamine neurons facilitate learning by calculating reward prediction error, or the difference between expected and actual reward. Despite two decades of research, it remains unclear how dopamine neurons make this calculation. Here we review studies that tackle this problem from a diverse set of approaches, from anatomy to electrophysiology to computational modeling and behavior. Several patterns emerge from this synthesis: that dopamine neurons themselves calculate reward prediction error, rather than inherit it passively from upstream regions; that they combine multiple separate and redundant inputs, which are themselves interconnected in a dense recurrent network; and that despite the complexity of inputs, the output from dopamine neurons is remarkably homogeneous and robust. The more we study this simple arithmetic computation, the knottier it appears to be, suggesting a daunting (but stimulating) path ahead for neuroscience more generally.

Indexed as

RewardAnimalsBrainDopamineHumansLearningNerve NetNeural PathwaysDopaminearithmeticcircuitrydopaminelearningprediction errorreward

Identifiers

PMID28441114
PMCPMC6721851

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.