Linguistic correlates of societal variation: A quantitative analysis

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Abstract

Traditionally, many researchers have supported a uniformitarian view whereby all languages are of roughly equal complexity, facilitated by internal trade-offs between complexity at different levels, such as morphology and syntax. The extent to which the speakers’ societies influence the trade-offs has not been well studied. In this paper, we focus on morphology and syntax, and report significant correlations between specific linguistic and societal features, in particular those relating to exoteric (open) vs. esoteric (close-knit) society types, characterizable in terms of population size, mobility, communication across distances, etc. We conduct an exhaustive quantitative analysis drawing upon WALS, D-Place, Ethnologue and Glottolog, finding some support for our hypothesis that languages spoken by exoteric societies tend towards more complex syntaxes, while languages spoken by esoteric societies tend towards more complex morphologies.

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Most cited references 53

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brms: An R Package for Bayesian Multilevel Models Using Stan

Paul-Christian Bürkner (2017)

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MissForest--non-parametric missing value imputation for mixed-type data.

Daniel Stekhoven, Peter Bühlmann (2012)

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch

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Language Structure Is Partly Determined by Social Structure

Gary Lupyan, Rick Dale (2010)

Background Languages differ greatly both in their syntactic and morphological systems and in the social environments in which they exist. We challenge the view that language grammars are unrelated to social environments in which they are learned and used. Methodology/Principal Findings We conducted a statistical analysis of >2,000 languages using a combination of demographic sources and the World Atlas of Language Structures— a database of structural language properties. We found strong relationships between linguistic factors related to morphological complexity, and demographic/socio-historical factors such as the number of language users, geographic spread, and degree of language contact. The analyses suggest that languages spoken by large groups have simpler inflectional morphology than languages spoken by smaller groups as measured on a variety of factors such as case systems and complexity of conjugations. Additionally, languages spoken by large groups are much more likely to use lexical strategies in place of inflectional morphology to encode evidentiality, negation, aspect, and possession. Our findings indicate that just as biological organisms are shaped by ecological niches, language structures appear to adapt to the environment (niche) in which they are being learned and used. As adults learn a language, features that are difficult for them to acquire, are less likely to be passed on to subsequent learners. Languages used for communication in large groups that include adult learners appear to have been subjected to such selection. Conversely, the morphological complexity common to languages used in small groups increases redundancy which may facilitate language learning by infants. Conclusions/Significance We hypothesize that language structures are subjected to different evolutionary pressures in different social environments. Just as biological organisms are shaped by ecological niches, language structures appear to adapt to the environment (niche) in which they are being learned and used. The proposed Linguistic Niche Hypothesis has implications for answering the broad question of why languages differ in the way they do and makes empirical predictions regarding language acquisition capacities of children versus adults.

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Author and article information

Contributors

Sihan Chen:

ORCID: https://orcid.org/0000-0002-9307-5014

Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing

David Gil: Role: ConceptualizationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing

Sergey Gaponov: Role: Writing – original draftRole: Writing – review & editing

Jana Reifegerste:

ORCID: https://orcid.org/0000-0002-0618-5881

Role: Writing – original draftRole: Writing – review & editing

Tessa Yuditha: Role: Writing – original draftRole: Writing – review & editing

Tatiana Tatarinova: Role: Writing – original draftRole: Writing – review & editing

Ljiljana Progovac: Role: ConceptualizationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing

Antonio Benítez-Burraco:

ORCID: https://orcid.org/0000-0003-4574-5666

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing

Marcus Perlman: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS One

Journal ID (publisher-id): plos

Title: PLOS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 16 April 2024

Publication date Collection: 2024

Volume: 19

Issue: 4

Electronic Location Identifier: e0300838

Affiliations

[1 ] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America

[2 ] Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

[3 ] Department of Biology and Computational Biology, University of LaVerne, LaVerne, CA, United States of America

[4 ] Department of Neurology, Georgetown University, Washington, DC, United States of America

[5 ] Department of Spanish, Linguistics & Theory of Literature, University of Seville, Seville, Spain

[6 ] Linguistics Program, Wayne State University, Detroit, MI, United States of America

University of Birmingham, UNITED KINGDOM

Author notes

Competing Interests: The authors have declared that no competing interests exist.

‡ LP and ABB also contributed equally to this work.

* E-mail: abenitez8@ 123456us.es

Author information

Sihan Chen https://orcid.org/0000-0002-9307-5014

Jana Reifegerste https://orcid.org/0000-0002-0618-5881

Antonio Benítez-Burraco https://orcid.org/0000-0003-4574-5666

Article

Publisher ID: PONE-D-23-15161

DOI: 10.1371/journal.pone.0300838

PMC ID: 11020942

PubMed ID: 38626198

SO-VID: 72141417-385d-4116-b223-6dc5f640b373

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 17 May 2023

Date accepted : 5 March 2024

Page count

Figures: 4, Tables: 0, Pages: 15

Funding

Funded by: MCIN/AEI/ 10.13039/501100011033

Award ID: PID2020-114516GB-I00

Award Recipient :

ORCID: https://orcid.org/0000-0003-4574-5666

Antonio Benítez-Burraco

This research was supported by grant PID2020-114516GB-I00 funded by MCIN/AEI/ 10.13039/501100011033 (to ABB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Data Availability The data generated by this research and used in the analyses can be found at https://github.com/cshnican/XSlanguages

Linguistic correlates of societal variation: A quantitative analysis

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Most cited references 53

brms: An R Package for Bayesian Multilevel Models Using Stan

MissForest--non-parametric missing value imputation for mixed-type data.

Language Structure Is Partly Determined by Social Structure

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