Person systems convey the roles entities play in the context of speech (e.g., speaker, addressee). Like other linguistic category systems, not all ways of partitioning the person space are equally likely cross-linguistically. Different theories have been proposed to constrain the set of possible person partitions that humans can represent, explaining their typological distribution. This paper introduces an artificial language learning methodology to investigate the existence of universal constraints on person systems. We report the results of three experiments that inform these theoretical approaches by generating behavioural evidence for the impact of constraints on the learnability of different person partitions. Our findings constitute the first experimental evidence for learnability differences in this domain.
|Early online date||2 Sep 2020|
|Publication status||E-pub ahead of print - 2 Sep 2020|
- person systems
- artificial language learning
- linguistic universals