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One Way or Another: Cortical Language Areas Flexibly Adapt Processing Strategies to Perceptual And Contextual Properties of Speech

Authors :
Klimovich-Gray, Anastasia
Barrena, Ander
Agirre, Eneko
Molinaro, Nicola
Klimovich-Gray, Anastasia
Barrena, Ander
Agirre, Eneko
Molinaro, Nicola
Publication Year :
2022

Abstract

Published:07 April 2021<br />Cortical circuits rely on the temporal regularities of speech to optimize signal parsing for sound-to-meaning mapping. Bottom-up speech analysis is accelerated by top–down predictions about upcoming words. In everyday communications, however, listeners are regularly presented with challenging input—fluctuations of speech rate or semantic content. In this study, we asked how reducing speech temporal regularity affects its processing—parsing, phonological analysis, and ability to generate context-based predictions. To ensure that spoken sentences were natural and approximated semantic constraints of spontaneous speech we built a neural network to select stimuli from large corpora. We analyzed brain activity recorded with magnetoencephalography during sentence listening using evoked responses, speech-to-brain synchronization and representational similarity analysis. For normal speech theta band (6.5–8 Hz) speech-to-brain synchronization was increased and the left fronto-temporal areas generated stronger contextual predictions. The reverse was true for temporally irregular speech—weaker theta synchronization and reduced top–down effects. Interestingly, delta-band (0.5 Hz) speech tracking was greater when contextual/semantic predictions were lower or if speech was temporally jittered. We conclude that speech temporal regularity is relevant for (theta) syllabic tracking and robust semantic predictions while the joint support of temporal and contextual predictability reduces word and phrase-level cortical tracking (delta).

Details

Database :
OAIster
Notes :
The European Union’s Horizon 2020 research and innovation programme (under the Marie Sklodowska-Curie grant agreement No 798971 awarded to A.K.G.); the Spanish Ministry of Science, Innovation and Universities (grant RTI2018-096311-B-I00 to N.M.); the Agencia Estatal de Investigación (AEI), the Fondo Europeo de Desarrollo Regional (FEDER); the Basque Government (through the BERC 2018-2021 program), the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation (SEV-2015-0490), DeepText project (KK-2020/00088) and Ixa excellence research group (IT1343-19). the UPV/EHU (a postdoctoral grant ESPDOC18/101 to A.B.); the NVIDIA Corporation (to A.B. with the donation of a Titan V GPU used for this research)., English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1286735628
Document Type :
Electronic Resource