It takes a village: The role of community size in linguistic regularization

Abstract

Studies of artificial language learning provide insight into how learning biases and iterated learning may shape natural languages. Prior work has looked at how learners deal with unpredictable variation and how a language changes across multiple generations of learners. The present study combines these features, exploring how word order variation is preserved or regularized over generations. We investigate how these processes are affected by (1) learning biases, (2) the size of the language community, and (3) the amount of input provided. Our results show that when the input comes from a single speaker, adult learners frequency match, reproducing the variability in the input across three generations. However, when the same amount of input is distributed across multiple speakers, frequency matching breaks down. When regularization occurs, there is a strong bias for SOV word order (relative to OSV and VSO). Finally, when the amount of input provided by multiple speakers is increased, learners are able to frequency match. These results demonstrate that both population size and the amount of input per speaker each play a role in language convergence.