Evidence map›Paper›PMID 31551597›Full record

ReviewNature neuroscience2019

Learning task-state representations.

Yael Niv

Abstract readReview
In one paragraph

Review in Nature neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 172 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
172citing papers in PubMed, 1 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

172 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
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  10. The hippocampus as a generative model.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026
    Review
  11. Article
  12. Long-term Learning Induces Plastic Changes in Frontostriatal Circuits.bioRxiv : the preprint server for biology · 2026
    Article
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  15. Review
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  18. A neural mechanism for online discovery of latent contexts.bioRxiv : the preprint server for biology · 2026
    Article
  19. Article
  20. Article

112 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

1 author.

Yael NivPsychology Department and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA. yael@princeton.edu.ORCID http://orcid.org/0000-0002-0259-8371

Funding

Orbitofrontal cortex as a cognitive map of task statesR01DA042065 · NIDA · PRINCETON UNIVERSITY · PI NIV, YAEL · 2016 to 2020
$1.8M
NIDA NIH HHS R01 DA042065
6 · The paper itself

Abstract

Arguably, the most difficult part of learning is deciding what to learn about. Should I associate the positive outcome of safely completing a street-crossing with the situation 'the car approaching the crosswalk was red' or with 'the approaching car was slowing down'? In this Perspective, we summarize our recent research into the computational and neural underpinnings of 'representation learning'-how humans (and other animals) construct task representations that allow efficient learning and decision-making. We first discuss the problem of learning what to ignore when confronted with too much information, so that experience can properly generalize across situations. We then turn to the problem of augmenting perceptual information with inferred latent causes that embody unobservable task-relevant information, such as contextual knowledge. Finally, we discuss recent findings regarding the neural substrates of task representations that suggest the orbitofrontal cortex represents 'task states', deploying them for decision-making and learning elsewhere in the brain.

Indexed as

AnimalsDecision MakingGeneralization, PsychologicalHumansLearningPrefrontal Cortex

Identifiers

PMID31551597
PMCPMC7241310

What OpenQuestion holds

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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.