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Statistical learning for vocal sequence acquisition in a songbird

Birdsong is a learned communicative behavior that consists of discrete acoustic elements (“syllables”) that are sequenced in a controlled manner. While the learning of the acoustic structure of syllables has been extensively studied, relatively little is known about sequence learning in songbirds. S...

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Autores principales: James, Logan S., Sun, Herie, Wada, Kazuhiro, Sakata, Jon T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7010765/
https://www.ncbi.nlm.nih.gov/pubmed/32041978
http://dx.doi.org/10.1038/s41598-020-58983-8
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author James, Logan S.
Sun, Herie
Wada, Kazuhiro
Sakata, Jon T.
author_facet James, Logan S.
Sun, Herie
Wada, Kazuhiro
Sakata, Jon T.
author_sort James, Logan S.
collection PubMed
description Birdsong is a learned communicative behavior that consists of discrete acoustic elements (“syllables”) that are sequenced in a controlled manner. While the learning of the acoustic structure of syllables has been extensively studied, relatively little is known about sequence learning in songbirds. Statistical learning could contribute to the acquisition of vocal sequences, and we investigated the nature and extent of sequence learning at various levels of song organization in the Bengalese finch, Lonchura striata var. domestica. We found that, under semi-natural conditions, pupils (sons) significantly reproduced the sequence statistics of their tutor’s (father’s) songs at multiple levels of organization (e.g., syllable repertoire, prevalence, and transitions). For example, the probability of syllable transitions at “branch points” (relatively complex sequences that are followed by multiple types of transitions) were significantly correlated between the songs of tutors and pupils. We confirmed the contribution of learning to sequence similarities between fathers and sons by experimentally tutoring juvenile Bengalese finches with the songs of unrelated tutors. We also discovered that the extent and fidelity of sequence similarities between tutors and pupils were significantly predicted by the prevalence of sequences in the tutor’s song and that distinct types of sequence modifications (e.g., syllable additions or deletions) followed distinct patterns. Taken together, these data provide compelling support for the role of statistical learning in vocal production learning and identify factors that could modulate the extent of vocal sequence learning.
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spelling pubmed-70107652020-02-21 Statistical learning for vocal sequence acquisition in a songbird James, Logan S. Sun, Herie Wada, Kazuhiro Sakata, Jon T. Sci Rep Article Birdsong is a learned communicative behavior that consists of discrete acoustic elements (“syllables”) that are sequenced in a controlled manner. While the learning of the acoustic structure of syllables has been extensively studied, relatively little is known about sequence learning in songbirds. Statistical learning could contribute to the acquisition of vocal sequences, and we investigated the nature and extent of sequence learning at various levels of song organization in the Bengalese finch, Lonchura striata var. domestica. We found that, under semi-natural conditions, pupils (sons) significantly reproduced the sequence statistics of their tutor’s (father’s) songs at multiple levels of organization (e.g., syllable repertoire, prevalence, and transitions). For example, the probability of syllable transitions at “branch points” (relatively complex sequences that are followed by multiple types of transitions) were significantly correlated between the songs of tutors and pupils. We confirmed the contribution of learning to sequence similarities between fathers and sons by experimentally tutoring juvenile Bengalese finches with the songs of unrelated tutors. We also discovered that the extent and fidelity of sequence similarities between tutors and pupils were significantly predicted by the prevalence of sequences in the tutor’s song and that distinct types of sequence modifications (e.g., syllable additions or deletions) followed distinct patterns. Taken together, these data provide compelling support for the role of statistical learning in vocal production learning and identify factors that could modulate the extent of vocal sequence learning. Nature Publishing Group UK 2020-02-10 /pmc/articles/PMC7010765/ /pubmed/32041978 http://dx.doi.org/10.1038/s41598-020-58983-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
James, Logan S.
Sun, Herie
Wada, Kazuhiro
Sakata, Jon T.
Statistical learning for vocal sequence acquisition in a songbird
title Statistical learning for vocal sequence acquisition in a songbird
title_full Statistical learning for vocal sequence acquisition in a songbird
title_fullStr Statistical learning for vocal sequence acquisition in a songbird
title_full_unstemmed Statistical learning for vocal sequence acquisition in a songbird
title_short Statistical learning for vocal sequence acquisition in a songbird
title_sort statistical learning for vocal sequence acquisition in a songbird
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7010765/
https://www.ncbi.nlm.nih.gov/pubmed/32041978
http://dx.doi.org/10.1038/s41598-020-58983-8
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