Nora, A. | Search bioRxiv

bioRxiv2026

Unstable word form representations and greater reliance on right hemisphere during word learning in adults with a history of developmental language disorder

Impairment in acquiring new vocabulary is a persistent deficit in developmental language disorder (DLD), but its cortical substrates remain unclear. We investigated learning of new words in young adults with DLD and typically developing (TD) control participants using magnetoencephalography (MEG). Participants were exposed to spoken real words and two sets of pseudowords, with shared beginnings, in two MEG sessions. One set of pseudowords was actively trained for three days, paired with fantasy animal pictures. Both groups learned new pseudowords, but adults with DLD recalled fewer items and confused similar sounding pseudowords more often than TD adults. MEG recordings revealed robust left temporal learning effects in both groups, along with left inferior frontal involvement. The DLD group additionally showed stronger right-hemispheric involvement and a significant right temporal learning effect for the newly learned words. Furthermore, only the DLD group showed changes in the left and right temporal responses for the real words after the learning phase, indicating altered lexical access for familiar words after integration of new word forms with overlapping phonology into the mental lexicon. Together, the findings suggest persisting problems in creating and accessing stable and distinctive phonological memory representations for word forms in adults with DLD, along with altered hemispheric balance in processing.

neuroscience↗

bioRxiv2019

Dynamic time-locking mechanism in the cortical representation of spoken words

Human speech has a unique capacity to carry and communicate rich meanings. However, it is not known how the highly dynamic and variable perceptual signal is mapped to existing linguistic and semantic representations. In this novel approach, we utilized the natural acoustic variability of sounds and mapped them to magnetoencephalography (MEG) data using physiologically-inspired machine-learning models. We aimed at determining how well the models, differing in their representation of temporal information, serve to decode and reconstruct spoken words from MEG recordings in 16 healthy volunteers. We discovered that time-locking of the cortical activation to the unfolding speech input is crucial for the encoding of the acoustic-phonetic features. In contrast, time-locking was not highlighted in cortical processing of non-speech environmental sounds that conveyed the same meanings as the spoken words, including human-made sounds with temporal modulation content similar to speech. The amplitude envelope of the spoken words was particularly well reconstructed based on cortical evoked responses. Our results indicate that speech is encoded cortically with especially high temporal fidelity. This mechanism may contribute to the frequently reported entrainment of the cortical oscillations to the amplitude envelope of speech. Furthermore, the phoneme content was reflected in cortical evoked responses simultaneously with the spectrotemporal features, pointing to an instantaneous transformation of the unfolding acoustic features into linguistic representations during speech processing.\n\nSummary: Computational modeling of cortical responses to spoken words highlights the relevance of temporal tracking of spectrotemporal features, which is likely pivotal for transforming the acoustic-phonetic features into linguistic representations.

neuroscience↗