Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact. Issue 181 (11th August 2021)

• Record Type: Journal Article
• Authors: Ranacher, Peter; Neureiter, Nico; van Gijn, Rik; Sonnenhauser, Barbara; Escher, Anastasia; Weibel, Robert; Muysken, Pieter; Bickel, Balthasar
• Is Part Of: Journal of the Royal Society interface. Volume 18:Issue 181(2021)
• Publication Date: 2021-08-11
• Subjects: Bayesian clustering -- cultural evolution -- linguistic areas -- spatial analysis -- confounding -- language -- mixture model; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2020.1031
• Languages: English
• ISSNs: 1742-5689

Abstract

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.