Investigating the neural correlates of continuous speech computation with frequency-tagged neuroelectric responses

Simple item page
dc.contributor.authorBuiatti, Marco
dc.contributor.authorPena, Marcela
dc.contributor.authorDehaene Lambertz, Ghislaine
dc.date.accessioned2024-01-10T12:39:52Z
dc.date.available2024-01-10T12:39:52Z
dc.date.issued2009
dc.description.abstractIn order to learn an oral language, humans have to discover words from a continuous signal. Streams of artificial monotonous speech can be readily segmented based on the statistical analysis of the syllables' distribution. This parsing is considerably improved when acoustic cues, such as subliminal pauses, are added suggesting that a different mechanism is involved. Here we used a frequency-tagging approach to explore the neural mechanisms underlying word learning while listening to continuous speech. High-density EEG was recorded in adults listening to a concatenation of either random syllables or tri-syllabic artificial words, with or without subliminal pauses added every three syllables. Peaks in the EEG power spectrum at the frequencies of one and three syllables occurrence were used to tag the perception of a monosyllabic or trisyllabic structure, respectively. Word streams elicited the suppression of a one-syllable frequency peak, steadily present during random streams, suggesting that syllables are no more perceived as isolated segments but bounded to adjacent syllables. Crucially, three-syllable frequency peaks were only observed during word streams with pauses, and were positively correlated to the explicit recall of the detected words. This result shows that pauses facilitate a fast, explicit and successful extraction of words from continuous speech, and that the frequency-tagging approach is a powerful tool to track brain responses to different hierarchical units of the speech structure. (C) 2008 Elsevier Inc. All rights reserved.
dc.description.funderMcDonnell foundation
dc.fechaingreso.objetodigital03-04-2024
dc.format.extent11 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.neuroimage.2008.09.015
dc.identifier.eissn1095-9572
dc.identifier.issn1053-8119
dc.identifier.pubmedidMEDLINE:18929668
dc.identifier.urihttps://doi.org/10.1016/j.neuroimage.2008.09.015
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/77244
dc.identifier.wosidWOS:000262301100021
dc.information.autorucPsicología;Peña M;S/I;1002571
dc.issue.numero2
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final519
dc.pagina.inicio509
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE
dc.revistaNEUROIMAGE
dc.rightsacceso restringido
dc.subjectEEG
dc.subjectExplicit learning
dc.subjectProsody
dc.subjectSpeech segmentation
dc.subjectSteady-state response
dc.subjectAUDITORY-CORTEX
dc.subjectCONSCIOUS PERCEPTION
dc.subjectYOUNG INFANTS
dc.subjectSEGMENTATION
dc.subjectLANGUAGE
dc.subjectUNITS
dc.subjectPATTERNS
dc.subjectSEGREGATION
dc.subjectBOUNDARIES
dc.subjectSYLLABLES
dc.titleInvestigating the neural correlates of continuous speech computation with frequency-tagged neuroelectric responses
dc.typeartículo
dc.volumen44
sipa.codpersvinculados1002571
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2024-04-03. Investigating the neural correlates of continuous speech computation with frequency-tagged neuroelectric responses.pdf
Size:
3.02 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas