Evidence map›Paper›PMID 36508677›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

Information flow across the cortical timescale hierarchy during narrative construction.

Claire H C Chang, Samuel A Nastase, Uri Hasson

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
16.8field-weighted citation impact, top 1% of its field
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

51 citing papers in PubMed, 93 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Movie Features Are Hierarchically Encoded in the Dynamics of Functional Connectivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  9. Narrative "twist" shifts within-individual neural representations of dissociable story features.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Article
  11. Article
  12. Hierarchical dynamic coding coordinates speech comprehension in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  13. Article
  14. Brain-Wide Spatiotemporally Distinct Traveling Waves Drive Anxiety-Like Behaviors in Mice.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  15. Article
  16. Article
  17. Emergence of a temporal processing gradient from naturalistic inputs and network connectivity.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  18. Review
  19. Article
  20. 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

3 authors at 1 institution in 1 country.

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

Funding

NRSA Training Grant in Quantitative NeuroscienceT32MH065214 · NIMH · PRINCETON UNIVERSITY · PI Nathaniel Douglass Daw, KENNETH A NORMAN · 2002 to 2026
$7.9M
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 MH112357NIMH NIH HHS T32 MH065214
6 · The paper itself

Abstract

When listening to spoken narratives, we must integrate information over multiple, concurrent timescales, building up from words to sentences to paragraphs to a coherent narrative. Recent evidence suggests that the brain relies on a chain of hierarchically organized areas with increasing temporal receptive windows to process naturalistic narratives. We hypothesized that the structure of this cortical processing hierarchy should result in an observable sequence of response lags between networks comprising the hierarchy during narrative comprehension. This study uses functional MRI to estimate the response lags between functional networks during narrative comprehension. We use intersubject cross-correlation analysis to capture network connectivity driven by the shared stimulus. We found a fixed temporal sequence of response lags-on the scale of several seconds-starting in early auditory areas, followed by language areas, the attention network, and lastly the default mode network. This gradient is consistent across eight distinct stories but absent in data acquired during rest or using a scrambled story stimulus, supporting our hypothesis that narrative construction gives rise to internetwork lags. Finally, we build a simple computational model for the neural dynamics underlying the construction of nested narrative features. Our simulations illustrate how the gradual accumulation of information within the boundaries of nested linguistic events, accompanied by increased activity at each level of the processing hierarchy, can give rise to the observed lag gradient.

Indexed as

Brain MappingSpeech PerceptionBrainComprehensionMagnetic Resonance Imagingcortical hierarchyfMRIfunctional connectivitylanguage processingnaturalistic stimuli

Identifiers

PMID36508677
PMCPMC9907070
OpenAlexW4311501806

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.