Increased top-down semantic processing in natural speech linked to better reading in dyslexia

Anastasia Klimovich-Gray, Giovanni Di Liberto, Lucia Amoruso, Ander Barrena, Eneko Agirre, Nicola Molinaro

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2 Citations (Scopus)
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Early research proposed that individuals with developmental dyslexia use contextual information to facilitate lexical access and compensate for phonological deficits. Yet at present there is no corroborating neuro-cognitive evidence. We explored this with a novel combination of magnetoencephalography (MEG), neural encoding and grey matter volume analyses. We analysed MEG data from 41 adult native Spanish speakers (14 with dyslexic symptoms) who passively listened to naturalistic sentences. We used multivariate Temporal Response Function analysis to capture online cortical tracking of both auditory (speech envelope) and contextual information. To compute contextual information tracking we used word-level Semantic Surprisal derived using a Transformer Neural Network language model. We related online information tracking to participants’ reading scores and grey matter volumes within the reading-linked cortical network. We found that right hemisphere envelope tracking was related to better phonological decoding (pseudoword reading) for both groups, with dyslexic readers performing worse overall at this task. Consistently, grey matter volume in the superior temporal and bilateral inferior frontal areas increased with better envelope tracking abilities. Critically, for dyslexic readers only, stronger Semantic Surprisal tracking in the right hemisphere was related to better word reading. These findings further support the notion of a speech envelope tracking deficit in dyslexia and provide novel evidence for top-down semantic compensatory mechanisms.
Original languageEnglish
Article number120072
Number of pages11
Early online date7 Apr 2023
Publication statusPublished - Jun 2023

Bibliographical note

AKG was supported by the Marie Sklodowska-Curie grant agreement No 798971. NM and LA acknowledge Spanish Ministry of Science, Innovation and Universities (grants RTI2018-096311-B-I00, PDC2022-133917-I00), Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional and support from the Basque Government through the BERC 2022-2025, Spanish Ministry of Economy and Competitiveness, “Severo Ochoa” Programme for Centres/Units of Excellence in R&D (CEX2020-001010-S).

Data Availability Statement

The primary data for this experiment is stored on secured servers at BCBL and can be made available via a request to the Authors upon reaching a prior formal data sharing agreement with BCBL.

The neuroimaging and behavioural data have been collected and is stored at BCBL (Basque Center on Cognition, Brain and Language) servers. This data can be shared upon reaching a formal data sharing agreement between BCBL and the host institution of the researcher who requests the data access.

Code availability - the following data packages were used in the analysis:

1. MNE Python platform and analysis pipeline (Gramfort et al., 2014) mne 1.0.3 available at

2. MaxFilter version 2.2 by Elekta Neuromag, Elekta (2006). MaxFilter User's Guide

3. mTRF Matlab toolbox (Crosse et al., 2016) available at


  • dyslexia
  • magnetoencephalography
  • speech
  • semantics
  • predictive processing


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