Inferring linguistic transmission between generations at the scale of individuals

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 population genetics perform historical inferences data sampled at individual scale, within a population. We built four demographic models transmission over generations, each differing number teachers involved during language acquisition and relative roles teachers. then compared simulated obtained with these real Tajik speakers Central Asia, area known for its large diversity, using approximate Bayesian computation methods. Under this statistical framework, were able select that best explained data, infer best-fitting parameters under selected models. The model assumes lexicon individuals is result vertical two teachers, specific teacher. This demonstrates feasibility features cultural evolution.