JOURNAL ARTICLE

Similative-pretence constructions in language contact situations: A Usage-Based Construction Grammar perspective.

  • Published In: Cognitive Linguistic Studies, 2025, v. 12, n. 2. P. 292 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Olguín Martínez, Jesús; Gries, Stefan Th. 3 of 3

Abstract

The present study introduces a method that can be used to explore in a quantitatively rigorous yet less demanding way (both in terms of data and statistical requirements) how constructional templates and their lexical preferences (lexico-syntactic transference) diffuse in language contact situations. The study investigates the influence of Mexican Spanish similative-pretence constructions on Huasteca Nahuatl similative-pretence constructions as a proof-of-concept kind of application for our method. Speakers of Huasteca Nahuatl have borrowed the markers komo 'like' and komo si 'as if' from Mexican Spanish to express similative (e.g., she swims like a fish) and pretence meanings (e.g., she swims as if she were a fish), respectively. Using a conditional inference forest, the paper demonstrates that speakers of Huasteca Nahuatl have not only borrowed these markers from Mexican Spanish, but also lexical preferences (e.g., verb lemmas) of the constructions in which these markers occur. These findings show that the rigid partition of structural levels that has been adopted by traditional models of language contact proves inadequate for describing complex language situations. The method introduced here provides an integrative, non-modular way to explore language contact from a Usage-Based Construction Grammar perspective. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Cognitive Linguistic Studies. 2025/07, Vol. 12, Issue 2, p292
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2025
  • ISSN:2213-8722
  • DOI:10.1075/cogls.25015.olg
  • Accession Number:189240448
  • Copyright Statement:Copyright of Cognitive Linguistic Studies is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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