Evidence map›Paper›PMID 37873125›Full record

ArticlebioRxiv : the preprint server for biology2024

How a speaker herds the audience: Multi-brain neural convergence over time during naturalistic storytelling.

Claire H C Chang, Samuel A Nastase, Uri Hasson

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

3 authors.

Claire H C ChangPrinceton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540, USA.ORCID 0000-0002-3773-1777
Samuel A NastasePrinceton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540, USA.ORCID 0000-0001-7013-5275
Uri HassonPrinceton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540, USA.

Funding

Speaker-listener coupling: a novel neural approach for assessing communicationDP1HD091948 · NICHD · PRINCETON UNIVERSITY · PI HASSON, URI · 2016 to 2021
$5.6M
Neural dynamics supporting integration and recall over long timescales during natural continuous inputR01MH112357 · NIMH · PRINCETON UNIVERSITY · PI HASSON, URI, NORMAN, KENNETH A · 2017 to 2021
$2.5M
NICHD NIH HHS DP1 HD091948NIMH NIH HHS R01 MH112357
6 · The paper itself

Abstract

Storytelling-an ancient way for humans to share individual experiences with others-has been found to induce neural synchronization among listeners. In our exploration of the dynamic fluctuations in listener-listener (LL) coupling throughout stories, we uncover a significant correlation between LL and lag-speaker-listener (lag-SL) couplings over time. Using the analogy of neural pattern (dis)similarity as distances between participants, we term this phenomenon the "herding effect": like a shepherd guiding a group of sheep, the more closely listeners follow the speaker's prior brain activity patterns (higher lag-SL similarity), the more tightly they cluster together (higher LL similarity). This herding effect is particularly pronounced in brain regions where neural synchronization among listeners tracks with behavioral ratings of narrative engagement, highlighting the mediating role of narrative content in the observed multi-brain neural coupling dynamics. By integrating LL and SL neural couplings, this study illustrates how unfolding stories shape a dynamic multi-brain functional network and how the configuration of this network may be associated with moment-by-moment efficacy of communication.

Indexed as

Brain-to-brain couplingfMRImulti-brain neural dynamicsnarrativesnaturalistic stimuli

Identifiers

PMID37873125
PMCPMC10592711

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.