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

Mora Maldonado, Jennifer Culbertson

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
JournalLinguistic Inquiry
Early online date2 Sept 2020
Publication statusE-pub ahead of print - 2 Sept 2020

Keywords / Materials (for Non-textual outputs)

  • person systems
  • pronouns
  • artificial language learning
  • linguistic universals
  • semantics


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