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

A Nora*, A Faisal, J Seol, H. Renvall, E. Formisano, R Salmelin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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 dynamic time-locking of the cortical activation to the unfolding speech input is crucial for the encoding of the acoustic-phonetic features of speech. 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 speech tracking by evoked responses may partly reflect the same underlying neural mechanism as 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.Significance statement It has remained unclear how speech is processed differently from other sounds with comparable meanings and spectrotemporal characteristics. In this study, computational modeling of cortical responses to spoken words highlights the relevance of temporal tracking of spectrotemporal features especially for speech. This mechanism is likely pivotal for transforming the acoustic-phonetic features into linguistic representations.

Original languageEnglish
Article number0475-19
Number of pages18
JournaleNeuro
Volume7
Issue number4
Early online date8 Jun 2020
DOIs
Publication statusPublished - 2020

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