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

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.

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

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).