Evidence map›Paper›PMID 33661094›Full record

ArticleeLife2021

Signed and unsigned reward prediction errors dynamically enhance learning and memory.

Nina Rouhani, Yael Niv

Abstract read
In one paragraph

Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing 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

58 citing papers in PubMed.

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  18. Gradient-Free De Novo Learning.Entropy (Basel, Switzerland) · 2025
    Article
  19. Prediction Error-Related Memory Enhancement Depends on the Neural State Surrounding the Prediction Error Event.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  20. Article
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

2 authors.

Nina RouhaniChen Neuroscience Institute, California Institute of Technology, Pasadena, United States.ORCID 0000-0003-2814-0462
Yael NivDepartment of Psychology, Princeton University, Princeton, United States.ORCID 0000-0002-0259-8371

Funding

Neural and computational mechanisms of selective attention in experience-based deR01MH098861 · NIMH · PRINCETON UNIVERSITY · PI NIV, YAEL · 2012 to 2014
$1.1M
Quantifying the latent-cause inference process in humansR21MH120798 · NIMH · PRINCETON UNIVERSITY · PI NIV, YAEL · 2019 to 2020
$444k
NIMH NIH HHS R01 MH098861NIMH NIH HHS R21 MH120798
6 · The paper itself

Abstract

Memory helps guide behavior, but which experiences from the past are prioritized? Classic models of learning posit that events associated with unpredictable outcomes as well as, paradoxically, predictable outcomes, deploy more attention and learning for those events. Here, we test reinforcement learning and subsequent memory for those events, and treat signed and unsigned reward prediction errors (RPEs), experienced at the reward-predictive cue or reward outcome, as drivers of these two seemingly contradictory signals. By fitting reinforcement learning models to behavior, we find that both RPEs contribute to learning by modulating a dynamically changing learning rate. We further characterize the effects of these RPE signals on memory and show that both signed and unsigned RPEs enhance memory, in line with midbrain dopamine and locus-coeruleus modulation of hippocampal plasticity, thereby reconciling separate findings in the literature.

Indexed as

LearningMemoryReinforcement, PsychologyRewardDopamineHumansDopaminecomputational modelhumanmemoryneurosciencereinforcement learningreward prediction error

Identifiers

PMID33661094
PMCPMC8041467

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.