Kauhanen Henri, Gopal Deepthi, Galla Tobias, Bermúdez-Otero Ricardo
Zukunftskolleg, University of Konstanz, Universitätsstraße 10, 78464 Konstanz, Germany.
Department of Theoretical and Applied Linguistics, University of Cambridge, Sidgwick Avenue, Cambridge CB3 9DA, UK.
Sci Adv. 2021 Jan 1;7(1). doi: 10.1126/sciadv.abe6540. Print 2021 Jan.
Quantifying the speed of linguistic change is challenging because the historical evolution of languages is sparsely documented. Consequently, traditional methods rely on phylogenetic reconstruction. Here, we propose a model-based approach to the problem through the analysis of language change as a stochastic process combining vertical descent, spatial interactions, and mutations in both dimensions. A notion of linguistic temperature emerges naturally from this analysis as a dimensionless measure of the propensity of a linguistic feature to undergo change. We demonstrate how temperatures of linguistic features can be inferred from their present-day geospatial distributions, without recourse to information about their phylogenies. Thus, the evolutionary dynamics of language, operating across thousands of years, leave a measurable geospatial signature. This signature licenses inferences about the historical evolution of languages even in the absence of longitudinal data.
量化语言变化的速度具有挑战性,因为语言的历史演变记录稀少。因此,传统方法依赖于系统发育重建。在这里,我们通过将语言变化分析为一个结合了垂直传承、空间相互作用和两个维度上的变异的随机过程,提出了一种基于模型的解决该问题的方法。作为语言特征发生变化倾向的无量纲度量,语言温度的概念自然地从这一分析中浮现出来。我们展示了如何从语言特征的当今地理空间分布中推断出其温度,而无需借助其系统发育信息。因此,跨越数千年运作的语言进化动态留下了一个可测量的地理空间特征。即使在没有纵向数据的情况下,这个特征也能为关于语言历史演变的推断提供依据。