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bioRxiv · 10.1101/2022.12.20.521245

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

Abstract

Newborn infants have been shown to extract temporal regularities from sound sequences, both in the form of learning regular sequential properties, and extracting periodicity in the input, commonly referred to as a beat. However, these two types of regularities are often indistinguishable in isochronous sequences, as both statistical learning and beat perception can be elicited by the regular alternation of accented and unaccented sounds. Here, we manipulated the isochrony of sound sequences in order to disentangle statistical learning from beat perception in sleeping newborn infants in an EEG experiment, as previously done in adults and macaque monkeys. We used a binary accented sequence that induces a beat when presented with isochronous timing, but not when presented with randomly jittered timing. We compared mismatch responses to infrequent deviants falling on either accented or unaccented (i.e., odd and even) positions. Results showed a clear difference between metrical positions in the isochronous sequence, but not in the equivalent jittered sequence. This suggests that beat processing is present in newborns. However, the current paradigm did not show effects of statistical learning, despite previous evidence for this ability in newborns. These results show that statistical learning does not explain beat processing in newborn infants. Research highlightsSleeping newborns process musical beat. Transition probabilities are not enough to explain beat perception in newborn infants. No evidence of statistical learning (based on transition probabilities) without isochronous stimulation in newborns. Results converge with previous evidence on beat perception of newborn infants.

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BibTeXRIS

Haden, G. P., Bouwer, F. L., Honing, H., Winkler, I.. 2022-12-21. Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities. https://doi.org/10.1101/2022.12.20.521245

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