Literature DB >> 31600518

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

Morgan Wirthlin1, Edward F Chang2, Mirjam Knörnschild3, Leah A Krubitzer4, Claudio V Mello5, Cory T Miller6, Andreas R Pfenning7, Sonja C Vernes8, Ofer Tchernichovski9, Michael M Yartsev10.   

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.
Copyright © 2019. Published by Elsevier Inc.

Entities:  

Year:  2019        PMID: 31600518     DOI: 10.1016/j.neuron.2019.09.036

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  11 in total

Review 1.  Acoustic allometry and vocal learning in mammals.

Authors:  Maxime Garcia; Andrea Ravignani
Journal:  Biol Lett       Date:  2020-07-08       Impact factor: 3.703

2.  ZEBrA: Zebra finch Expression Brain Atlas-A resource for comparative molecular neuroanatomy and brain evolution studies.

Authors:  Peter V Lovell; Morgan Wirthlin; Taylor Kaser; Alexa A Buckner; Julia B Carleton; Brian R Snider; Anne K McHugh; Alexander Tolpygo; Partha P Mitra; Claudio V Mello
Journal:  J Comp Neurol       Date:  2020-02-19       Impact factor: 3.215

3.  Neurophysiological coordination of duet singing.

Authors:  Melissa J Coleman; Nancy F Day; Pamela Rivera-Parra; Eric S Fortune
Journal:  Proc Natl Acad Sci U S A       Date:  2021-06-08       Impact factor: 11.205

4.  Linking the genomic signatures of human beat synchronization and learned song in birds.

Authors:  Reyna L Gordon; Andrea Ravignani; Julia Hyland Bruno; Cristina M Robinson; Alyssa Scartozzi; Rebecca Embalabala; Maria Niarchou; Nancy J Cox; Nicole Creanza
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2021-08-23       Impact factor: 6.671

Review 5.  Defining the multidimensional phenotype: New opportunities to integrate the behavioral ecology and behavioral neuroscience of vocal learning.

Authors:  Timothy F Wright; Elizabeth P Derryberry
Journal:  Neurosci Biobehav Rev       Date:  2021-02-20       Impact factor: 9.052

6.  Learning Speech Production and Perception through Sensorimotor Interactions.

Authors:  Shihab Shamma; Prachi Patel; Shoutik Mukherjee; Guilhem Marion; Bahar Khalighinejad; Cong Han; Jose Herrero; Stephan Bickel; Ashesh Mehta; Nima Mesgarani
Journal:  Cereb Cortex Commun       Date:  2020-11-27

7.  Vocal learning: Beyond the continuum.

Authors:  Pedro Tiago Martins; Cedric Boeckx
Journal:  PLoS Biol       Date:  2020-03-30       Impact factor: 8.029

8.  Comparison of methods for rhythm analysis of complex animals' acoustic signals.

Authors:  Lara S Burchardt; Mirjam Knörnschild
Journal:  PLoS Comput Biol       Date:  2020-04-08       Impact factor: 4.475

9.  Molecular specializations of deep cortical layer analogs in songbirds.

Authors:  Alexander A Nevue; Peter V Lovell; Morgan Wirthlin; Claudio V Mello
Journal:  Sci Rep       Date:  2020-10-30       Impact factor: 4.379

10.  Vocal learning and flexible rhythm pattern perception are linked: Evidence from songbirds.

Authors:  Andrew A Rouse; Aniruddh D Patel; Mimi H Kao
Journal:  Proc Natl Acad Sci U S A       Date:  2021-07-16       Impact factor: 11.205

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