Evidence map›Paper›PMID 37610871›Full record

ArticleCell reports2023

Interactive cognitive maps support flexible behavior under threat.

Toby Wise, Caroline J Charpentier, Peter Dayan, Dean Mobbs

Abstract read
In one paragraph

Article in Cell reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. The interoceptive origin of reinforcement learning.Trends in cognitive sciences · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Naturalistic reinforcement learning.Trends in cognitive sciences · 2024
    Review
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.

Toby WiseDepartment of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA. Electronic address: toby.wise@kcl.ac.uk.
Caroline J CharpentierDepartment of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA; Department of Psychology, University of Maryland, College Park, MD, USA; Brain and Behavior Institute, University of Maryland, College Park, MD, USA.
Peter DayanMax Planck Institute for Biological Cybernetics, Tübingen, Germany; University of Tübingen, Tübingen, Germany.
Dean MobbsDepartment of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA; Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA, USA.

Funding

The Neurobiology of Social Decision-Making: Social Inference and ContextP50MH094258 · NIMH · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI MOBBS, DEAN · 2012 to 2021
$18.6M
Neuro-computational mechanisms of social learning and variation along psychiatric symptom dimensions and in autismK99MH123669 · NIMH · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI CHARPENTIER, CAROLINE JULIETTE · 2021 to 2022
$203k
NIMH NIH HHS K99 MH123669NIMH NIH HHS P50 MH094258Wellcome Trust 206460/Z/17/ZWellcome Trust 225945/Z/22/Z
6 · The paper itself

Abstract

In social environments, survival can depend upon inferring and adapting to other agents' goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents' preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants' inferences about threatening agents' preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents' preferences, facilitating generalized predictions of their behavior and effective responses.

Indexed as

AlgorithmsLearningCognitionHumansReinforcement, PsychologySocial Environmentavoidancecognitive mapsCP: Neurosciencedecision-makinglearningplanningsocial inference

Identifiers

PMID37610871
PMCPMC10658881

What OpenQuestion holds

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LicenceCC BY
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.