Orthogonal neural codes for speech in the infant brain

Abstract

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.

Sleeping neonates track transitional probabilities in speech but only retain the first syllable of words

“…For example, Benavides et al 5 reported a larger novelty response when changing the vowels of a bi-syllabic word (e.g., lili to lala ) compared to a change of consonants (e.g., lili to titi ). However, a recent EEG study showed that phonetic features were at the basis of speech perception in 3-month-old pre-babbling infants, offering the possibility of a structured combinatorial code for speech analysis not limited to vowels 58 .…”

The power of rhythms: how steady-state evoked responses reveal early neurocognitive development

“…While the traditional ERP technique ( Luck, 2005 ) imposes slow presentations rates around 1–2 Hz, with stimuli presented as discrete events, interspaced with long inter-stimulus intervals, SS-EP paradigms allow for stimulus repetitions in the range of 3–15Hz. Similarly, multivariate pattern analysis ( Ashton et al, 2022 ; Bayet et al, 2018 ; Gennari et al, 2021 ) is commonly implemented using slow-paced paradigms (although MVPA can in theory be performed on SS-EP data). Moreover, with an appropriate experimental design, the SS-EP technique allows for recordings from multiple stimuli or conditions presented in a single stimulation stream.…”

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

“…The oscillatory nature of information content associated with sinusoidal components of the evoked response is, we argue, interpretationally problematic. Features resembling successive peaks in classification accuracy are quite commonly reported in the literature (T. Carlson et al, 2013; Gennari et al, 2021; Hogendoorn & Burkitt, 2018; Kurth-Nelson et al, 2015; Mohsenzadeh et al, 2018; Robinson et al, 2020), where they have occasionally been interpreted as evidence for complex underlying phenomena – for example, the activation of distinct feedforward vs feedback connections, or discrete and distinct stages of cognitive processing. As we have shown in Figure 2, successive peaks arise naturally from an evoked response containing sinusoidal components.…”