Person of interest: Experimental investigations into the learnability of person systems

Abstract

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 pro- posed 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.

<em>You say yes, I say no</em>: Investigating the link between meaning and form in response particles

“…Further, there are a number of other cases in the literature in which learners have shown sensitivity to shared features-similar to feature congruence-in artificial language learning experiments (e.g., Maldonado & Culbertson 2020;Saldana et al 2022). We return to this issue in Experiment 3 by looking for evidence of sensitivity to feature (in)congruence in a different type of task.…”