Inferring linguistic transmission between generations at the scale of individuals

Abstract

8 Historical linguistics strongly benefited from recent methodological advances inspired by 9 phylogenetics. Nevertheless, no available method uses contemporaneous within-population 10 linguistic diversity to reconstruct the history of human populations. Here, we developed an 11 approach inspired from population genetics to perform historical linguistic inferences from 12 linguistic data sampled at the individual scale, within a population. We built four within-population 13 demographic models of linguistic transmission over generations, each differing by the number of 14 teachers involved during the language acquisition and the relative roles of the teachers. We then 15 compared the simulated data obtained with these models with real contemporaneous linguistic data 16 sampled from Tajik speakers from Central Asia, an area known for its large within-population 17 linguistic diversity, using approximate Bayesian computation methods. Under this statistical 18 framework, we were able to select the models that best explained the data, and infer the best-fitting 19 parameters under the selected models. This demonstrates the feasibility of using contemporaneous 20 within-population linguistic diversity to infer historical features of human cultural evolution. 1 bioRxiv preprint doi: https://doi.org/10.1101/441246; this version posted February 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. 21