Statistical language learning in neonates revealed by event-related brain potentials

Tuomas Teinonen, Vineta Fellman, Risto Naatanen, Paavo Alku, Minna Huotilainen

Research output: Contribution to journalArticlepeer-review

154 Citations (SciVal)

Abstract

Background: Statistical learning is a candidate for one of the basic prerequisites underlying the expeditious acquisition of spoken language. Infants from 8 months of age exhibit this form of learning to segment fluent speech into distinct words. To test the statistical learning skills at birth, we recorded event-related brain responses of sleeping neonates while they were listening to a stream of syllables containing statistical cues to word boundaries. Results: We found evidence that sleeping neonates are able to automatically extract statistical properties of the speech input and thus detect the word boundaries in a continuous stream of syllables containing no morphological cues. Syllable-specific event-related brain responses found in two separate studies demonstrated that the neonatal brain treated the syllables differently according to their position within pseudowords. Conclusion: These results demonstrate that neonates can efficiently learn transitional probabilities or frequencies of co-occurrence between different syllables, enabling them to detect word boundaries and in this way isolate single words out of fluent natural speech. The ability to adopt statistical structures from speech may play a fundamental role as one of the earliest prerequisites of language acquisition.
Original languageEnglish
JournalBMC Neuroscience
Volume10
DOIs
Publication statusPublished - 2009

Subject classification (UKÄ)

  • Neurosciences

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