Inferring linguistic transmission between generations at the scale of individuals

IF 2.1 0 LANGUAGE & LINGUISTICS
Valentin Thouzeau, Antonin Affholder, Philippe Mennecier, Paul Verdu, Frédéric Austerlitz
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引用次数: 0

Abstract

Historical linguistics strongly benefited from recent methodological advances inspired by phylogenetics. Nevertheless, no available method uses contemporaneous within-population linguistic diversity to reconstruct the history of human populations. Here, we developed an approach inspired from population genetics to perform historical linguistic inferences from linguistic data sampled at the individual scale, within a population. We built four within-population demographic models of linguistic transmission over generations, each differing by the number of teachers involved during the language acquisition and the relative roles of the teachers. We then compared the simulated data obtained with these models with real contemporaneous linguistic data sampled from Tajik speakers from Central Asia, an area known for its large within-population linguistic diversity, using approximate Bayesian computation methods. Under this statistical framework, we were able to select the models that best explained the data, and infer the best-fitting parameters under the selected models. The selected model assumes that the lexicon of individuals is the result of a vertical transmission by two teachers, with a specific lexicon for each teacher. This demonstrates the feasibility of using contemporaneous within-population linguistic diversity to infer historical features of human cultural evolution.
以个体为尺度推断代际间的语言传递
历史语言学很大程度上得益于系统发育学在方法论上的最新进展。然而,目前还没有可用的方法利用同时期的种群内语言多样性来重建人类种群的历史。在这里,我们开发了一种受群体遗传学启发的方法,从群体内个体尺度上采样的语言数据中进行历史语言推断。我们建立了四个跨代语言传播的人口统计学模型,每个模型都因语言习得过程中教师的数量和教师的相关角色而有所不同。然后,我们使用近似贝叶斯计算方法,将这些模型获得的模拟数据与中亚地区塔吉克语使用者的真实同期语言数据进行比较,中亚地区以人口内语言多样性大而闻名。在这个统计框架下,我们可以选择最能解释数据的模型,并在选择的模型下推断出最适合的参数。所选择的模型假设个体的词汇是两个教师垂直传播的结果,每个教师有一个特定的词汇。这证明了使用同时期种群内语言多样性来推断人类文化进化的历史特征的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Language Evolution
Journal of Language Evolution Social Sciences-Linguistics and Language
CiteScore
4.50
自引率
7.70%
发文量
8
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