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模块化的发声学习方法:解析复杂行为特征的多样性。

A Modular Approach to Vocal Learning: Disentangling the Diversity of a Complex Behavioral Trait.

机构信息

Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.

Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.

出版信息

Neuron. 2019 Oct 9;104(1):87-99. doi: 10.1016/j.neuron.2019.09.036.

DOI:10.1016/j.neuron.2019.09.036
PMID:31600518
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10066796/
Abstract

Vocal learning is a behavioral trait in which the social and acoustic environment shapes the vocal repertoire of individuals. Over the past century, the study of vocal learning has progressed at the intersection of ecology, physiology, neuroscience, molecular biology, genomics, and evolution. Yet, despite the complexity of this trait, vocal learning is frequently described as a binary trait, with species being classified as either vocal learners or vocal non-learners. As a result, studies have largely focused on a handful of species for which strong evidence for vocal learning exists. Recent studies, however, suggest a continuum in vocal learning capacity across taxa. Here, we further suggest that vocal learning is a multi-component behavioral phenotype comprised of distinct yet interconnected modules. Discretizing the vocal learning phenotype into its constituent modules would facilitate integration of findings across a wider diversity of species, taking advantage of the ways in which each excels in a particular module, or in a specific combination of features. Such comparative studies can improve understanding of the mechanisms and evolutionary origins of vocal learning. We propose an initial set of vocal learning modules supported by behavioral and neurobiological data and highlight the need for diversifying the field in order to disentangle the complexity of the vocal learning phenotype.

摘要

发声学习是一种行为特征,其中社会和声学环境塑造了个体的发声曲目。在过去的一个世纪里,发声学习的研究在生态学、生理学、神经科学、分子生物学、基因组学和进化学的交叉点上取得了进展。然而,尽管这种特征很复杂,但发声学习通常被描述为二元特征,物种被分为发声学习者或发声非学习者。因此,研究主要集中在少数几种具有强烈发声学习证据的物种上。然而,最近的研究表明,在分类单元中,发声学习能力存在连续体。在这里,我们进一步提出,发声学习是一种由不同但相互关联的模块组成的多组件行为表型。将发声学习表型离散化为其组成模块将有助于整合更广泛多样性的物种的研究结果,利用每个模块在特定模块或特定特征组合中表现出色的方式。这种比较研究可以提高对发声学习机制和进化起源的理解。我们提出了一组由行为和神经生物学数据支持的初始发声学习模块,并强调需要使该领域多样化,以理清发声学习表型的复杂性。

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