School of Biology, University of St Andrews, St Andrews, UK.
Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Philos Trans R Soc Lond B Biol Sci. 2021 Oct 25;376(1836):20200236. doi: 10.1098/rstb.2020.0236. Epub 2021 Sep 6.
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 (, 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 (, 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'.
学习如何影响发声是动物交流和人类语言研究的一个关键问题。鸟类和人类在行为和神经生物学层面上的平行研究使我们对发声学习的机制有了很多了解。随后的研究揭示了哺乳动物中存在多种情况,其中经验对发声模式也有重大影响。Janik 和 Slater(, 1-11. (doi:10.1006/anbe.2000.1410)) 引入了发声使用和产生学习的区别,提供了一个通用框架来分类不同类型的学习如何影响发声。Petkov 和 Jarvis(, 12. (doi:10.3389/fnevo.2012.00012)) 在此基础上强调了有限和更复杂发声产生学习者之间更连续的分布。然而,随着更多研究提供经验数据,初始框架的局限性变得明显。我们根据这些框架进行了改进,以反映自发表以来的进展以及广泛认同的观点,即发声学习不是二元特征。我们提出了一种新的分类系统,基于 Janik 和 Slater 的定义,将发声学习分解为关键维度,以帮助理解这种复杂行为所涉及的机制。我们考虑了发声如何在没有学习的情况下发生变化,以及一个考虑到语境特异性和时间的使用学习框架。我们确定了发声产生学习的维度,包括听觉模型的复制(对模型声音的趋同/发散,复制的准确性)、变化程度(学习的类型和广度)和时间(学习发生的时间、所需的时间长度以及保留时间)。我们考虑了分类的灰色地带以及对这些行为的当前机制理解。我们的框架确定了研究需求,并将有助于为努力揭示发声学习的多维性质的神经生物学和进化研究提供信息。本文是主题为“动物和人类的发声学习”的特刊的一部分。