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Bards, Poets, and Cliques: Frequency-Dependent Selection and the Evolution of Language Genes

Overview of attention for article published in Bulletin of Mathematical Biology, December 2010
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Title
Bards, Poets, and Cliques: Frequency-Dependent Selection and the Evolution of Language Genes
Published in
Bulletin of Mathematical Biology, December 2010
DOI 10.1007/s11538-010-9619-z
Pubmed ID
Authors

Reed A. Cartwright

Abstract

The ability of humans to communicate via language is a complex, adapted phenotype, which undoubtedly has a recently evolved genetic component. However, the evolutionary dynamics of language-associated alleles are poorly understood. To improve our knowledge of such systems, a population-genetics model for language-associated genes is developed. (The model is general and applicable to social interactions other than communication.) When an allele arises that potentially improves the ability of individuals to communicate, it will experience positive frequency-dependent selection because its fitness will depend on how many other individuals communicate the same way. Consequently, new and rare alleles are selected against, posing a problem for the evolutionary origin of language. However, the model shows that if individuals form language-based cliques, then novel language-associated alleles can sweep through a population. Thus, the origin of language ability can be sufficiently explained by Darwinian processes operating on genetic diversity in a finite population of human ancestors.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 14%
Unknown 12 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 21%
Other 2 14%
Student > Bachelor 2 14%
Lecturer 1 7%
Lecturer > Senior Lecturer 1 7%
Other 4 29%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 43%
Biochemistry, Genetics and Molecular Biology 2 14%
Mathematics 1 7%
Arts and Humanities 1 7%
Linguistics 1 7%
Other 1 7%
Unknown 2 14%