Evidence map›Paper›PMID 40998765›Full record

ArticleNature communications2025

Fragmentation and multithreading of experience in the default-mode network.

Fahd Yazin, Gargi Majumdar, Neil Bramley, Paul Hoffman

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Fahd YazinSchool of Philosophy, Psychology & Language Sciences, University of Edinburgh, Edinburgh, UK. fahd7yazin@gmail.com.ORCID http://orcid.org/0009-0003-9638-0617
Gargi MajumdarBiological Psychology and Neuropsychology, University of Hamburg, Hamburg, Germany.ORCID http://orcid.org/0009-0003-9473-4661
Neil BramleySchool of Philosophy, Psychology & Language Sciences, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0002-4141-8476
Paul HoffmanSchool of Philosophy, Psychology & Language Sciences, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0002-3248-3225

Funding

RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/T004444/1
6 · The paper itself

Abstract

Reliance on internal predictive models of the world is central to many theories of human cognition. Yet it is unknown whether humans acquire multiple separate internal models, each evolved for a specific domain, or maintain a globally unified representation. Using fMRI during naturalistic experiences (movie watching and narrative listening), we show that three topographically distinct midline prefrontal cortical regions perform distinct predictive operations. The ventromedial PFC updates contextual predictions (States), the anteromedial PFC governs reference frame shifts for social predictions (Agents), and the dorsomedial PFC predicts transitions across the abstract state spaces (Actions). Prediction-error-driven neural transitions in these regions, indicative of model updates, coincided with subjective belief changes in a domain-specific manner. We find these parallel top-down predictions are unified and selectively integrated with visual sensory streams in the Precuneus, shaping participants' ongoing experience. Results generalized across sensory modalities and content, suggesting humans recruit abstract, modular predictive models for both vision and language. Our results highlight a key feature of human world modeling: fragmenting information into abstract domains before global integration.

Indexed as

CognitionDefault Mode NetworkPrefrontal CortexAdultBrain MappingFemaleHumansLanguageMagnetic Resonance ImagingMaleYoung Adult

Identifiers

PMID40998765
PMCPMC12462485

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.