Header

UZH-Logo

Maintenance Infos

Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact


Ranacher, Peter; Neureiter, Nico; van Gijn, Rik; Sonnenhauser, Barbara; Escher, Anastasia; Weibel, Robert; Muysken, Pieter; Bickel, Balthasar (2021). Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact. Journal of the Royal Society Interface, 18(181):20201031.

Abstract

When speakers of different languages interact, they are likely to influence each other: contact leaves traces in the linguistic record, which in turn can reveal geographical areas of past human interaction and migration. However, other factors may contribute to similarities between languages. Inheritance from a shared ancestral language and universal preference for a linguistic property may both overshadow contact signals. How can we find geographical contact areas in language data, while accounting for the confounding effects of inheritance and universal preference? We present sBayes, an algorithm for Bayesian clustering in the presence of confounding effects. The algorithm learns which similarities are better explained by confounders, and which are due to contact effects. Contact areas are free to take any shape or size, but an explicit geographical prior ensures their spatial coherence. We test sBayes on simulated data and apply it in two case studies to reveal language contact in South America and the Balkans. Our results are supported by findings from previous studies. While we focus on detecting language contact, the method can also be used to uncover other traces of shared history in cultural evolution, and more generally, to reveal latent spatial clusters in the presence of confounders.

Abstract

When speakers of different languages interact, they are likely to influence each other: contact leaves traces in the linguistic record, which in turn can reveal geographical areas of past human interaction and migration. However, other factors may contribute to similarities between languages. Inheritance from a shared ancestral language and universal preference for a linguistic property may both overshadow contact signals. How can we find geographical contact areas in language data, while accounting for the confounding effects of inheritance and universal preference? We present sBayes, an algorithm for Bayesian clustering in the presence of confounding effects. The algorithm learns which similarities are better explained by confounders, and which are due to contact effects. Contact areas are free to take any shape or size, but an explicit geographical prior ensures their spatial coherence. We test sBayes on simulated data and apply it in two case studies to reveal language contact in South America and the Balkans. Our results are supported by findings from previous studies. While we focus on detecting language contact, the method can also be used to uncover other traces of shared history in cultural evolution, and more generally, to reveal latent spatial clusters in the presence of confounders.

Statistics

Citations

Altmetrics

Downloads

20 downloads since deposited on 25 Aug 2021
20 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Department of Comparative Linguistics
07 Faculty of Science > Institute of Geography
Special Collections > NCCR Evolving Language
Special Collections > Centers of Competence > Center for the Interdisciplinary Study of Language Evolution
08 Research Priority Programs > Language and Space
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:Biomedical Engineering, Biochemistry, Biomaterials, Bioengineering, Biophysics, Biotechnology
Language:English
Date:1 August 2021
Deposited On:25 Aug 2021 15:03
Last Modified:25 Aug 2021 15:09
Publisher:Royal Society Publishing
ISSN:1742-5689
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1098/rsif.2020.1031
Project Information:
  • : FunderSNSF
  • : Grant IDCRSII1_160739
  • : Project TitleLinguistic morphology in time and space (LiMiTS)
  • : FunderSNSF
  • : Grant IDCRSII5_183578
  • : Project TitleOut of Asia: Linguistic Diversity and Population History

Download

Hybrid Open Access

Download PDF  'Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact'.
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 2MB
View at publisher
Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)