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

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

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Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. Electronic address:
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Museum for Natural History Berlin, Invalidenstr. 43, 10115 Berlin, Germany.
Center for Neuroscience, University of California, Davis, CA, USA; Department of Psychology, University of California, Davis, CA, USA.
Department of Behavioral Neuroscience, OHSU, Portland, OR, USA.
Cortical Systems and Behavior Laboratory, Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands.
Department of Psychology, Hunter College, The City University of New York, New York, NY, USA.
Helen Wills Neuroscience Institute and Department of Bioengineering, UC Berkeley, Berkeley, CA, USA. Electronic address:


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.

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