Evidence map›Paper›PMID 35917612›Full record

ArticleCognition2022

Humans adaptively resolve the explore-exploit dilemma under cognitive constraints: Evidence from a multi-armed bandit task.

Vanessa M Brown, Michael N Hallquist, Michael J Frank, Alexandre Y Dombrovski

Abstract read
In one paragraph

Article in Cognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Apparent learning biases emerge from optimal inference: Insights from master equation analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Individual differences in sequential decision-making.bioRxiv : the preprint server for biology · 2025
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Review
  10. Article
  11. Article
  12. Article
  13. Naturalistic reinforcement learning.Trends in cognitive sciences · 2024
    Review
  14. Article
  15. Article
  16. Bayesian Reinforcement Learning With Limited Cognitive Load.Open mind : discoveries in cognitive science · 2024
    Article
  17. 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

4 authors.

Vanessa M BrownDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: brownvm2@upmc.edu.
Michael N HallquistDepartment of Psychology, Pennsylvania State University, State College, PA, USA; Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Michael J FrankDepartment of Cognitive, Linguistic, and Psychological Sciences and Carney Institute for Brain Science, Brown University, Providence, RI, USA.
Alexandre Y DombrovskiDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.

Funding

PSYCHOBIOLOGY OF SUICIDAL BEHAVIOR IN BPDR01MH048463 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Alexandre Y. Dombrovski · 1992 to 2026
$13.9M
Reward Learning in Late-Life Suicidal BehaviorR01MH100095 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Alexandre Y. Dombrovski · 2014 to 2026
$6.8M
Neurocomputational mechanisms of impaired social decision-making in borderline personalityR01MH119399 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HALLQUIST, MICHAEL NELSON · 2019 to 2024
$2.5M
Neurocomputational substrates of maladaptive uncertainty learning and avoidance in anxietyK23MH122626 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BROWN, VANESSA · 2021 to 2025
$854k
NIMH NIH HHS K23 MH122626NIMH NIH HHS R01 MH048463NIMH NIH HHS R01 MH100095NIMH NIH HHS R01 MH119399
6 · The paper itself

Abstract

When navigating uncertain worlds, humans must balance exploring new options versus exploiting known rewards. Longer horizons and spatially structured option values encourage humans to explore, but the impact of real-world cognitive constraints such as environment size and memory demands on explore-exploit decisions is unclear. In the present study, humans chose between options varying in uncertainty during a multi-armed bandit task with varying environment size and memory demands. Regression and cognitive computational models of choice behavior showed that with a lower cognitive load, humans are more exploratory than a simulated value-maximizing learner, but under cognitive constraints, they adaptively scale down exploration to maintain exploitation. Thus, while humans are curious, cognitive constraints force people to decrease their strategic exploration in a resource-rational-like manner to focus on harvesting known rewards.

Indexed as

Choice BehaviorDecision MakingCognitionExploratory BehaviorHumansRewardUncertaintyCognitive constraintsExploitationExplorationLearning

Identifiers

PMID35917612
PMCPMC9530017

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.