Inferring linguistic transmission between generations at the scale of individuals

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. This demonstrates the feasibility of using contemporaneous within-population linguistic diversity to infer historical features of human cultural evolution.