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

Anastasia Klimovich-Gray*, Ander Barrena, Eneko Agirre, Nicola Molinaro

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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).

Original languageEnglish
Pages (from-to)4092-4103
Number of pages12
JournalCerebral Cortex
Issue number9
Early online date7 Apr 2021
Publication statusPublished - 1 Sept 2021

Bibliographical note

Funding Information:
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-BI00 to N.M.); the Agencia Estatal de Investigaci?n (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).

Publisher Copyright:
© 2021 The Author(s). Published by Oxford University Press.


  • coherence
  • MEG
  • neural network
  • phonological processing
  • representational similarity analysis
  • semantic predictions


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