Evidence map›Paper›PMID 40924468›Full record

ArticleCerebral cortex (New York, N.Y. : 1991)2025

Neural correlates of Bayesian social belief updating in the medial prefrontal cortex.

Lieke Hofmans, Wouter van den Bos

Abstract read
In one paragraph

Article in Cerebral cortex (New York, N.Y. : 1991), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Lieke HofmansDepartment of Developmental Psychology, University of Amsterdam, Nieuwe Achtergracht 129b, 1018 WS Amsterdam, The Netherlands.ORCID 0000-0001-7259-6243
Wouter van den BosDepartment of Developmental Psychology, University of Amsterdam, Nieuwe Achtergracht 129b, 1018 WS Amsterdam, The Netherlands.

Funding

EU Horizon 2020 research and innovation programme H2020-DIGYMATEX 870578European Research Council ERC-2018-StG-803,338European Research Council ERC-2023-CoG-101,125,961
6 · The paper itself

Abstract

Social learning, a hallmark of human behavior, entails integrating other's actions or ideas with one's own. While it can accelerate the learning process by circumventing slow and costly individual trial-and-error learning, its effectiveness depends on knowing when and whose information to use. In this study, we explored how individuals use social information based on their own and others' levels of uncertainty. Participants performed a social information use task in which they could revise their initial estimate after viewing a peer's estimate. Uncertainty was manipulated by varying the amount of information provided before their decision and by manipulating the peer's reported confidence. As expected, adjustments were larger when individuals themselves were less certain and the peer was more confident. Through a combination of Bayesian computational modeling and neuroimaging analyses, we were able to identify regions in the anterior and ventral medial prefrontal cortex where neural activity overlapped in response to lower personal certainty, higher peer confidence, and larger belief updates after viewing the peer's estimate. We discuss how these regions in the medial prefrontal cortex likely serve as a convergence zone for the preparation and execution of integrating certainty estimates.

Indexed as

Prefrontal CortexSocial LearningSocial PerceptionAdultBayes TheoremBrain MappingFemaleHumansMagnetic Resonance ImagingMaleUncertaintyYoung AdultBayesian decision-makingcomputational modelingneuroimagingsocial learninguncertainty

Identifiers

PMID40924468
PMCPMC12418961

What OpenQuestion holds

Textmetadata
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.