Abstract

How learning affects vocalizations is a key question in the study of animal communication and human language. Parallel efforts in birds and humans have taught us much about how vocal learning works on a behavioural and neurobiological level. Subsequent efforts have revealed a variety of cases among mammals in which experience also has a major influence on vocal repertoires. Janik and Slater (Anim. Behav. 60, 1–11. (doi:10.1006/anbe.2000.1410)) introduced the distinction between vocal usage and production learning, providing a general framework to categorize how different types of learning influence vocalizations. This idea was built on by Petkov and Jarvis (Front. Evol. Neurosci. 4, 12. (doi:10.3389/fnevo.2012.00012)) to emphasize a more continuous distribution between limited and more complex vocal production learners. Yet, with more studies providing empirical data, the limits of the initial frameworks become apparent. We build on these frameworks to refine the categorization of vocal learning in light of advances made since their publication and widespread agreement that vocal learning is not a binary trait. We propose a novel classification system, based on the definitions by Janik and Slater, that deconstructs vocal learning into key dimensions to aid in understanding the mechanisms involved in this complex behaviour. We consider how vocalizations can change without learning, and a usage learning framework that considers context specificity and timing. We identify dimensions of vocal production learning, including the copying of auditory models (convergence/divergence on model sounds, accuracy of copying), the degree of change (type and breadth of learning) and timing (when learning takes place, the length of time it takes and how long it is retained). We consider grey areas of classification and current mechanistic understanding of these behaviours. Our framework identifies research needs and will help to inform neurobiological and evolutionary studies endeavouring to uncover the multi-dimensional nature of vocal learning.This article is part of the theme issue ‘Vocal learning in animals and humans’.

Highlights

  • We propose a novel classification system, based on the definitions by Janik and Slater, that deconstructs vocal learning into key dimensions to aid in understanding the mechanisms involved in this complex behaviour

  • Interest in vocal learning skills has been with us from the beginning of animal behaviour research, with scholars as early as Darwin [1] recognizing the role of learning in the development of bird song and its parallels with human vocal performance

  • Janik & Slater [2,3] introduced a framework that distinguished between vocal usage learning, in which existing signals are given in a new context or sequence, and vocal production learning, in which signals are modified in form after experience with the signals of others

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Summary

Introduction

Interest in vocal learning skills has been with us from the beginning of animal behaviour research, with scholars as early as Darwin [1] recognizing the role of learning in the development of bird song and its parallels with human vocal. Our framework gives us a means to directly compare the different aspects of vocal learning abilities across animals In this way, we aim to make clear the research directions that are needed to close current gaps in knowledge and make significant strides in understanding the remarkable trait of vocal learning. We aim to make clear the research directions that are needed to close current gaps in knowledge and make significant strides in understanding the remarkable trait of vocal learning We hope this framework will make it possible to investigate and reveal the mechanisms that drive each dimension, both in individual species, or in a true ‘like-with-like’ cross-species approach, and in this way, better understand the prevalence and evolution of this complex phenomenon

Non-learned inputs into vocal variation
Vocal usage learning
Vocal production learning
Grey areas in classification of vocal learning
Mechanisms
Outlook
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