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Article Dans Une Revue Proceedings of the National Academy of Sciences of the United States of America Année : 2021

Orthogonal neural codes for speech in the infant brain

Résumé

Creating invariant representations from an everchanging speech signal is a major challenge for the human brain. Such an ability is particularly crucial for preverbal infants who must discover the phonological, lexical, and syntactic regularities of an extremely inconsistent signal in order to acquire language. Within the visual domain, an efficient neural solution to overcome variability consists in factorizing the input into a reduced set of orthogonal components. Here, we asked whether a similar decomposition strategy is used in early speech perception. Using a 256-channel electroencephalographic system, we recorded the neural responses of 3-mo-old infants to 120 natural consonant–vowel syllables with varying acoustic and phonetic profiles. Using multivariate pattern analyses, we show that syllables are factorized into distinct and orthogonal neural codes for consonants and vowels. Concerning consonants, we further demonstrate the existence of two stages of processing. A first phase is characterized by orthogonal and context-invariant neural codes for the dimensions of manner and place of articulation. Within the second stage, manner and place codes are integrated to recover the identity of the phoneme. We conclude that, despite the paucity of articulatory motor plans and speech production skills, pre-babbling infants are already equipped with a structured combinatorial code for speech analysis, which might account for the rapid pace of language acquisition during the first year.

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Dates et versions

hal-03349785 , version 1 (20-09-2021)

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Giulia Gennari, Sébastien Marti, Marie Palu, Ana Fló, Ghislaine Dehaene-Lambertz. Orthogonal neural codes for speech in the infant brain. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118 (31), pp.e2020410118. ⟨10.1073/pnas.2020410118⟩. ⟨hal-03349785⟩
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