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

View ORCID ProfilePeter Ranacher, View ORCID ProfileNico Neureiter, View ORCID ProfileRik van Gijn, View ORCID ProfileBarbara Sonnenhauser, Anastasia Escher, View ORCID ProfileRobert Weibel, View ORCID ProfilePieter Muysken, View ORCID ProfileBalthasar Bickel
doi: https://doi.org/10.1101/2021.03.31.437731
Peter Ranacher
1University Research Priority Program (URPP) Language and Space, University of Zurich, Zurich, Switzerland
2Department of Geography, University of Zurich, Zurich, Switzerland
3Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Switzerland
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  • For correspondence: peter.ranacher@geo.uzh.ch
Nico Neureiter
1University Research Priority Program (URPP) Language and Space, University of Zurich, Zurich, Switzerland
2Department of Geography, University of Zurich, Zurich, Switzerland
3Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Switzerland
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Rik van Gijn
4Leiden University Centre for Linguistics, Leiden, Netherlands
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Barbara Sonnenhauser
5Department of Slavonic Languages and Literatures, University of Zurich, Switzerland
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Anastasia Escher
5Department of Slavonic Languages and Literatures, University of Zurich, Switzerland
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Robert Weibel
1University Research Priority Program (URPP) Language and Space, University of Zurich, Zurich, Switzerland
2Department of Geography, University of Zurich, Zurich, Switzerland
3Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Switzerland
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Pieter Muysken
7Centre for Language Studies, Radboud University Nijmegen
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Balthasar Bickel
1University Research Priority Program (URPP) Language and Space, University of Zurich, Zurich, Switzerland
3Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Switzerland
6Department of Comparative Language Science, University of Zurich, Switzerland
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Abstract

When speakers of two or more languages interact, they are likely to influence each other: contact leaves traces in the linguistic record, which in turn can reveal geographic areas of past human interaction and migration. However the complex, multi-dimensional nature of contact has hindered the development of a rigorous methodology for detecting its traces. Specifically, other factors may contribute to similarities between languages. Inheritance (a property is passed from an ancestor to several descendant languages), and universal preference (a property is universally preferred), may both overshadow contact signals. How can we find geographic 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 in a set of features are better accounted for by confounders, and which are due to contact effects. Contact areas are free to take any shape or size, but an explicit geographic prior ensures their spatial coherence. We test the clustering method 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 —mostly qualitative— findings from previous studies. While we focus on the specific problem of 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/derpetermann/sBayes

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted April 01, 2021.
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Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact
Peter Ranacher, Nico Neureiter, Rik van Gijn, Barbara Sonnenhauser, Anastasia Escher, Robert Weibel, Pieter Muysken, Balthasar Bickel
bioRxiv 2021.03.31.437731; doi: https://doi.org/10.1101/2021.03.31.437731
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Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact
Peter Ranacher, Nico Neureiter, Rik van Gijn, Barbara Sonnenhauser, Anastasia Escher, Robert Weibel, Pieter Muysken, Balthasar Bickel
bioRxiv 2021.03.31.437731; doi: https://doi.org/10.1101/2021.03.31.437731

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