Statistical learning for vocal sequence acquisition in a songbird
Statistical learning for vocal sequence acquisition in a songbird
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鸣禽声音序列采集的统计学习
DOI：
10.1038/s41598-020-58983-8
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发表时间：
2020
影响因子：
4.6
通讯作者：
Sakata Jon T.
中科院分区：
文献类型：
--
作者：
James Logan S.;Sun Herie;Wada Kazuhiro;Sakata Jon T.
鸟鸣是一种习得的交流行为，它由离散的声学元素（“音节”）组成，这些元素以受控的方式排列。虽然音节声学结构的学习已经被广泛研究，但对鸣禽的序列学习知之甚少。统计学习可能有助于收购的声乐序列，我们调查了序列学习的性质和程度在不同层次的歌曲组织在孟加拉雀，文鸟striatavar。我们发现，在半自然的条件下，学生（儿子）在多个组织层次上显著地再现了他们导师（父亲）歌曲的序列统计（例如，音节库、流行率和转换）。例如，在“分支点”（相对复杂的序列，其次是多种类型的过渡）的音节过渡的概率显着相关的导师和学生的歌曲。我们证实了父亲和儿子之间的序列相似性学习的贡献，实验辅导少年孟加拉雀与无关的导师的歌曲。我们还发现，导师和学生之间序列相似性的程度和保真度可以通过导师歌曲中序列的普遍性来显著预测，并且不同类型的序列修饰（例如，音节添加或删除）遵循不同的模式。总之，这些数据提供了令人信服的支持统计学习在发声学习中的作用，并确定因素，可以调节发声序列学习的程度。
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 striatavar.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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