Evidence map›Paper›PMID 35194556›Full record

ArticleCurrent opinion in behavioral sciences2021

The Role of Executive Function in Shaping Reinforcement Learning.

Milena Rmus, Samuel D McDougle, Anne G E Collins

Abstract read
In one paragraph

Article in Current opinion in behavioral sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

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  12. Semantic Anchors Facilitate Task Encoding in Continual Learning.Open mind : discoveries in cognitive science · 2025
    Article
  13. Article
  14. Review
  15. The challenge of learning adaptive mental behavior.Journal of psychopathology and clinical science · 2024
    Article
  16. Latent-state and model-based learning in PTSD.Trends in neurosciences · 2024
    Review
  17. The construction and use of cognitive maps in model-based control.Journal of experimental psychology. General · 2024
    Article
  18. Article
  19. 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

3 authors.

Milena RmusDepartment of Psychology, University of California, Berkeley.
Samuel D McDougleDepartment of Psychology, Yale University.
Anne G E CollinsDepartment of Psychology, University of California, Berkeley.

Funding

The neural computations supporting hierarchical reinforcement learningR01MH119383 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI COLLINS, ANNE G.E. · 2019 to 2023
$1.9M
NIMH NIH HHS R01 MH119383
6 · The paper itself

Abstract

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments.. Instead, these aspects of instrumental behavior are assumed to be supported by the brain's executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.

Identifiers

PMID35194556
PMCPMC8859995

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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.