Infant-directed speech facilitates seven-month-old infants’ cortical tracking of speech

Abstract

This study assessed cortical tracking of temporal information in incoming natural speech in seven-month-old infants. Cortical tracking refers to the process by which neural activity follows the dynamic patterns of the speech input. In adults, it has been shown to involve attentional mechanisms and to facilitate effective speech encoding. However, in infants, cortical tracking or its effects on speech processing have not been investigated. This study measured cortical tracking of speech in infants and, given the involvement of attentional mechanisms in this process, cortical tracking of both infant-directed speech (IDS), which is highly attractive to infants, and the less captivating adult-directed speech (ADS), were compared. IDS is the speech register parents use when addressing young infants. In comparison to ADS, it is characterised by several acoustic qualities that capture infants’ attention to linguistic input and assist language learning. Seven-month-old infants’ cortical responses were recorded via electroencephalography as they listened to IDS or ADS recordings. Results showed stronger low-frequency cortical tracking of the speech envelope in IDS than in ADS. This suggests that IDS has a privileged status in facilitating successful cortical tracking of incoming speech which may, in turn, augment infants’ early speech processing and even later language development.

Development of neural oscillatory activity in response to speech in children from 4 to 6 years old

“…On the one hand, studies that focused their analyses on power (amplitude) measures failed to find hemispheric specialization for the different temporal components of speech (Peña et al, 2010;Telkemeyer et al, 2011). On the other hand, the only study that tried to measure entrainment to the speech envelope in infants found larger responses over the left as compared to the right hemisphere when listening both to infant-and adult-directed speech (Kalashnikova et al, 2018). Note, however, that they assessed the infants' Time Response Function (TRF), a measure that might be optimal to pick up evoked components of the auditory response, but not auditory entrainment (Doelling, Assaneo, Bevilacqua, Pesaran, & Poeppel, 2019).…”

Quantifying the individual auditory and visual brain response in 7- month-old infants watching a brief cartoon movie

“…The present manuscript thus extends previous approaches on infant mTRFs (Kalashnikova et al, 2018) by (a) using multisensory stimuli; (b) directly contrasting infant and adult responses; (c) testing a large group of infants (n=52); and (d) contrasting two different approaches to compute mTRFs, namely using a generic response function compared to an individual response function.…”

“…One important advantage of using mTRFs compared to classical ERPs approaches is the possibility of using more complex and naturalistic experimental settings. These might include the processing of continuous auditory signals (as demonstrated by Kalashnikova et al, 2018), or audiovisual video material, as in the present study. In contrast to ERPs, which require the repetitive presentation of short stimuli, with mTRFs, it is possible to investigate the brain’s entrainment over a longer period of time and in a much closer approximation of natural settings.…”

“…In adults, mTRFs have successfully been used to track the processing of ongoing speech (e.g., Fiedler et al, 2019) as well as ongoing and naturalistic visual input (O’Sullivan, Crosse, Di Liberto, & Lalor, 2017). Furthermore, Kalashnikova et al (2018) used mTRFs in infants to analyze the processing of ongoing auditory speech signals, reporting a stronger cortical tracking for infant-directed compared to adult-directed speech (Kalashnikova, Peter, Di Liberto, Lalor, & Burnham, 2018).…”

A Tutorial on Auditory Attention Identification Methods

“…Leastsquares Ding and Simon, 2012a;Di Liberto et al, 2015;Alickovic et al, 2016, in rewiev;Fiedler et al, 2017Fiedler et al, , 2019Hjortkjaer et al, 2018;Kalashnikova et al, 2018;Lesenfants et al, 2018;Lunner et al, 2018;Verschueren et al, 2018;Wong et al, 2018 Inverse/backward modeling Supervised case: O'Sullivan et al, 2015O'Sullivan et al, , 2017Aroudi et al, 2016;Das et al, 2016Das et al, , 2018Presacco et al, 2016;Biesmans et al, 2017;Fuglsang et al, 2017;Van Eyndhoven et al, 2017;Zink et al, 2017;Bednar and Lalor, 2018;Ciccarelli et al, 2018;Etard et al, 2018;Hausfeld et al, 2018;Narayanan and Bertrand, 2018;Schäfer et al, 2018;Vanthornhout et al, 2018;Verschueren et al, 2018;Wong et al, 2018;Akbari et al, 2019;Somers et al, 2019 trial separately, and averaging over all training de/en-coders (Crosse et al, 2016).…”