Evidence map›Paper›PMID 38839302›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2024

Speech Prosody Serves Temporal Prediction of Language via Contextual Entrainment.

Yulia Lamekina, Lorenzo Titone, Burkhard Maess, Lars Meyer

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2024. 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

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. How Low-Frequency Neural Activity Structures Language in Time.Neurobiology of language (Cambridge, Mass.) · 2026
    Article
4 · The record

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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.

Yulia LamekinaResearch Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany ilamekina@research.baycrest.org.ORCID 0000-0002-7016-038X
Lorenzo TitoneResearch Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany.ORCID 0009-0004-6442-6980
Burkhard MaessMethods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany.ORCID 0000-0002-7857-291X
Lars MeyerResearch Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany.ORCID 0000-0002-7487-5518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Temporal prediction assists language comprehension. In a series of recent behavioral studies, we have shown that listeners specifically employ rhythmic modulations of prosody to estimate the duration of upcoming sentences, thereby speeding up comprehension. In the current human magnetoencephalography (MEG) study on participants of either sex, we show that the human brain achieves this function through a mechanism termed entrainment. Through entrainment, electrophysiological brain activity maintains and continues contextual rhythms beyond their offset. Our experiment combined exposure to repetitive prosodic contours with the subsequent presentation of visual sentences that either matched or mismatched the duration of the preceding contour. During exposure to prosodic contours, we observed MEG coherence with the contours, which was source-localized to right-hemispheric auditory areas. During the processing of the visual targets, activity at the frequency of the preceding contour was still detectable in the MEG; yet sources shifted to the (left) frontal cortex, in line with a functional inheritance of the rhythmic acoustic context for prediction. Strikingly, when the target sentence was shorter than expected from the preceding contour, an omission response appeared in the evoked potential record. We conclude that prosodic entrainment is a functional mechanism of temporal prediction in language comprehension. In general, acoustic rhythms appear to endow language for employing the brain's electrophysiological mechanisms of temporal prediction.

Indexed as

MagnetoencephalographySpeech PerceptionAcoustic StimulationAdultComprehensionFemaleHumansLanguageMalePhotic StimulationSpeechYoung Adultlanguage comprehensionneural entrainmentprosodytemporal predictions

Identifiers

PMID38839302
PMCPMC11236583

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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.