From computing transition probabilities to word recognition in sleeping neonates, a two-step neural tale

Abstract

Extracting statistical regularities from the environment is a primary learning mechanism, which might support language acquisition. While it is known that infants are sensitive to transition probabilities between syllables in continuous speech, the format of the encoded representation remains unknown. Here we used electrophysiology to investigate how 31 full-term neonates process an artificial language build by the random concatenation of four pseudo-words and which information they retain. We used neural entrainment as a marker of the regularities the brain is tracking in the stream during learning. Then, we compared the evoked-related potentials (ERP) to different triplets to further explore the format of the information kept in memory. After only two minutes of familiarization with the artificial language, we observed significant neural entrainment at the word rate over left temporal electrodes compared to a random stream, demonstrating that sleeping neonates automatically and rapidly extracted the word pattern. ERPs significantly differed between triplets starting or not with the correct first syllable in the test phase, but no difference was associated with later violations in transition probabilities, revealing a change in the representation format between segmentation and memory processes. If the transition probabilities were used to segment the stream, the retained representation relied on syllables' ordinal position, but still without a complete representation of the words at this age. Our results revealed a two-step learning strategy, probably involving different brain regions.

Detection of regularities in auditory sequences before and at term-age in human neonates

“…Regarding statistical learning, several studies have indeed shown that neonates, at least at term age, are sensitive to conditional statistics between syllables. For example, they use transition probabilities between syllables to discover words in an artificial stream composed of four randomly concatenated tri-syllabic non-words (Teinonen, Fellman, Naatanen, Alku, & Huotilainen, 2009;Kudo, Nonaka, Mizuno, Mizuno, & Okanoya, 2011;Fló, Benjamin, Palu, & Dehaene-Lambertz, 2021; see also Benjamin et al, 2021 for quadrisyllabic words). Because any of the three other words can follow a given word, there is a drop in transition probability between syllables at the end of the word, from 1 within a word to 0.33 between words.…”

Synchronizing with the rhythm: Infant neural entrainment to complex musical and speech stimuli

“…Since data from 8 children were rejected due to insufficient artifact-free trials in the EEG task, the final sample consisted of 25 infants (12 males; mean age = 8.00 months; in days, M = 240.16, SD = 19.13,min = 208,max = 279). Sample size was determined based on the previous SS-EP literature in similar populations (Kabdebon et al, 2015;Cirelli et al, 2016;Peykarjou et al, 2017;Choi et al, 2020;Fló et al, 2022). Information about parents' and children's daily exposure to music was collected, in order to better characterize the sample from this point of view.…”

“…Importantly, it offers an objective definition of the targeted responses: based on the frequency of the stimulation, SS-EPs are expected at a specific narrow frequency band (Zhou et al, 2016). Previous studies on newborns and infants applied frequency-tagging paradigms with periodic visual stimulation to investigate face and object processing (e.g., de Heering and Rossion, 2015;Peykarjou et al, 2017;Buiatti et al, 2019), and with rhythmic speech sounds to investigate the tracking of transitional probabilities in the context of artificial grammar learning (Kabdebon et al, 2015;Choi et al, 2020;Fló et al, 2022).…”