Sexual communication and domestication may give rise to the signal complexity necessary for the emergence of language: An indication from songbird studies

Okanoya, Kazuo

doi:10.3758/s13423-016-1165-8

Cited by 38 publications

(31 citation statements)

References 30 publications

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“…Bengalese finches (Lonchura striata domestica) are the domesticated strain of the whiterumped munia (L. striata). Domestication has been claimed to increase vocal learning complexity: if a "wild" species is already a good vocal learner, it becomes a more complex vocal learner after undergoing domestication [52]. In the case of the Bengalese finch, for example, this happens despite the fact that this bird species was not bred for its song.…”

Section: Complexity Considerationsmentioning

confidence: 99%

Vocal learning: Beyond the continuum

Martins¹,

Boeckx²

2020

PLoS Biol

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Vocal learning is the ability to modify vocal output on the basis of experience. Traditionally, species have been classified as either displaying or lacking this ability. A recent proposal, the vocal learning continuum, recognizes the need to have a more nuanced view of this phenotype and abandon the yes-no dichotomy. However, it also limits vocal learning to production of novel calls through imitation, moreover subserved by a forebrain-to-phonatorymuscles circuit. We discuss its limitations regarding the characterization of vocal learning across species and argue for a more permissive view.

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Section: Complexity Considerationsmentioning

confidence: 99%

Vocal learning: Beyond the continuum

Martins¹,

Boeckx²

2020

PLoS Biol

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“…This special issue provides a very rich selection of comparative data, including work on nonhuman primates (Arbib, 2016;Byrne & Cochet, 2016;Fischer, 2016;Lyn, 2017;Seyfarth & Cheney, 2016), birds (Fehér, 2016;Okanoya, 2017;ten Cate, 2016), bats (Vernes, 2016), or a mixture of species (Pepperberg, 2016). I will thus delve no further in this introduction into the value and virtues of comparative data.…”

Section: Comparative Cognition and Cognitive Biologymentioning

confidence: 99%

Empirical approaches to the study of language evolution

2017

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The study of language evolution, and human cognitive evolution more generally, has often been ridiculed as unscientific, but in fact it differs little from many other disciplines that investigate past events, such as geology or cosmology. Well-crafted models of language evolution make numerous testable hypotheses, and if the principles of strong inference (simultaneous testing of multiple plausible hypotheses) are adopted, there is an increasing amount of relevant data allowing empirical evaluation of such models. The articles in this special issue provide a concise overview of current models of language evolution, emphasizing the testable predictions that they make, along with overviews of the many sources of data available to test them (emphasizing comparative, neural, and genetic data). The key challenge facing the study of language evolution is not a lack of data, but rather a weak commitment to hypothesistesting approaches and strong inference, exacerbated by the broad and highly interdisciplinary nature of the relevant data. This introduction offers an overview of the field, and a summary of what needed to evolve to provide our species with language-ready brains. It then briefly discusses different contemporary models of language evolution, followed by an overview of different sources of data to test these models. I conclude with my own multistage model of how different components of language could have evolved.

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“…To that goal, recent studies have added the evolution of more complex patterns of vocal learning, such as learning from multiple tutors and more variable song. A preeminent example of this type of evolutionary change is the domesticated Bengalese finch (BF; Lonchura striata domestica) [73].…”

Section: (C) Slow Developmental Trajectories and The Evolution Of Vocmentioning

confidence: 99%

Birdsong as a window into language origins and evolutionary neuroscience

Aamodt

Farias-Virgens

White

2019

Phil. Trans. R. Soc. B

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Humans and songbirds share the key trait of vocal learning, manifested in speech and song, respectively. Striking analogies between these behaviours include that both are acquired during developmental critical periods when the brain's ability for vocal learning peaks. Both behaviours show similarities in the overall architecture of their underlying brain areas, characterized by cortico-striato-thalamic loops and direct projections from cortical neurons onto brainstem motor neurons that control the vocal organs. These neural analogies extend to the molecular level, with certain song control regions sharing convergent transcriptional profiles with speech-related regions in the human brain. This evolutionary convergence offers an unprecedented opportunity to decipher the shared neurogenetic underpinnings of vocal learning. A key strength of the songbird model is that it allows for the delineation of activity-dependent transcriptional changes in the brain that are driven by learned vocal behaviour. To capitalize on this advantage, we used previously published datasets from our laboratory that correlate gene co-expression networks to features of learned vocalization within and after critical period closure to probe the functional relevance of genes implicated in language. We interrogate specific genes and cellular processes through converging lines of evidence: human-specific evolutionary changes, intelligence-related phenotypes and relevance to vocal learning gene co-expression in songbirds. This article is part of the theme issue ‘What can animal communication teach us about human language?’

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Sexual communication and domestication may give rise to the signal complexity necessary for the emergence of language: An indication from songbird studies

Cited by 38 publications

References 30 publications

Vocal learning: Beyond the continuum

Vocal learning: Beyond the continuum

Empirical approaches to the study of language evolution

Birdsong as a window into language origins and evolutionary neuroscience

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