Iterated Learning Models of Language Change: A Case Study of Sino‐Korean Accent

Abstract

Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as resulting from successive generations of phonotactic learning. Comparisons between different iterated learning models also suggest that Korean learners’ phonotactic generalizations are guided by storage of entire syllable‐sized units, and provide evidence that perceptual confusions between different forms substantially impacted historical change. This suggests that in addition to accounting for the evolution of broad general characteristics of language, iterated learning models can also provide insight into more detailed patterns of historical language change.

Cognitive Linguistics and Language Evolution

“…In recent work, the IL paradigm, as well as artificial language learning paradigms more generally, have also been used to address typological questions (Levshina 2018) or, using computational modelling (Ito & Feldman 2022) or communication game experiments (Ventura et al 2022), for investigating historical language change. In addition, some communication game studies have significantly increased the pool of participants by drawing on online interfaces or even on smartphone apps (Morin et al 2018(Morin et al , 2022.…”