Evidence map›Paper›PMID 33192386›Full record

ArticleFrontiers in human neuroscience2020

Electrophysiological Dynamics of Visual Speech Processing and the Role of Orofacial Effectors for Cross-Modal Predictions.

Maëva Michon, Gonzalo Boncompte, Vladimir López

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Article in Frontiers in human neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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.

Maëva MichonLaboratorio de Neurociencia Cognitiva y Evolutiva, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
Gonzalo BoncompteLaboratorio de Neurodinámicas de la Cognición, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
Vladimir LópezLaboratorio de Psicología Experimental, Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human brain generates predictions about future events. During face-to-face conversations, visemic information is used to predict upcoming auditory input. Recent studies suggest that the speech motor system plays a role in these cross-modal predictions, however, usually only audio-visual paradigms are employed. Here we tested whether speech sounds can be predicted on the basis of visemic information only, and to what extent interfering with orofacial articulatory effectors can affect these predictions. We registered EEG and employed N400 as an index of such predictions. Our results show that N400's amplitude was strongly modulated by visemic salience, coherent with cross-modal speech predictions. Additionally, N400 ceased to be evoked when syllables' visemes were presented backwards, suggesting that predictions occur only when the observed viseme matched an existing articuleme in the observer's speech motor system (i.e., the articulatory neural sequence required to produce a particular phoneme/viseme). Importantly, we found that interfering with the motor articulatory system strongly disrupted cross-modal predictions. We also observed a late P1000 that was evoked only for syllable-related visual stimuli, but whose amplitude was not modulated by interfering with the motor system. The present study provides further evidence of the importance of the speech production system for speech sounds predictions based on visemic information at the pre-lexical level. The implications of these results are discussed in the context of a hypothesized trimodal repertoire for speech, in which speech perception is conceived as a highly interactive process that involves not only your ears but also your eyes, lips and tongue.

Indexed as

articulemecross-modal predictionERPsorofacial movementsplace of articulationspeech motor systemviseme

Identifiers

PMID33192386
PMCPMC7653187

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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.