Evidence map›Paper›PMID 34860554›Full record

ArticleScience advances2021

Predicting speech from a cortical hierarchy of event-based time scales.

Lea-Maria Schmitt, Julia Erb, Sarah Tune, Anna U Rysop, Gesa Hartwigsen, Jonas Obleser

Abstract read
In one paragraph

Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
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  10. "What" and "When" Predictions Jointly Modulate Speech Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  11. Diverse Frontoparietal Connectivity Supports Semantic Prediction and Integration in Sentence Comprehension.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Semantic composition in experimental and naturalistic paradigms.Imaging neuroscience (Cambridge, Mass.) · 2024
    Article
  17. Review
  18. Semantic Representations during Language Comprehension Are Affected by Context.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
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  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

6 authors.

Lea-Maria SchmittDepartment of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.ORCID 0000-0002-9356-2234
Julia ErbDepartment of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
Sarah TuneDepartment of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
Anna U RysopLise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1 A, 04103 Leipzig, Germany.ORCID 0000-0002-9853-358X
Gesa HartwigsenLise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1 A, 04103 Leipzig, Germany.ORCID 0000-0002-8084-1330
Jonas ObleserDepartment of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.ORCID 0000-0002-7619-0459

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How do predictions in the brain incorporate the temporal unfolding of context in our natural environment? We here provide evidence for a neural coding scheme that sparsely updates contextual representations at the boundary of events. This yields a hierarchical, multilayered organization of predictive language comprehension. Training artificial neural networks to predict the next word in a story at five stacked time scales and then using model-based functional magnetic resonance imaging, we observe an event-based “surprisal hierarchy” evolving along a temporoparietal pathway. Along this hierarchy, surprisal at any given time scale gated bottom-up and top-down connectivity to neighboring time scales. In contrast, surprisal derived from continuously updated context influenced temporoparietal activity only at short time scales. Representing context in the form of increasingly coarse events constitutes a network architecture for making predictions that is both computationally efficient and contextually diverse.

Identifiers

PMID34860554
PMCPMC8641937

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Registered trials

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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.