Person of Interest: Experimental Investigations into the Learnability of Person Systems - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Linguistic Inquiry Année : 2022

Person of Interest: Experimental Investigations into the Learnability of Person Systems

Résumé

Person systems convey the roles entities play in the context of speech (e.g., speaker, addressee). As with other linguistic category systems, not all ways of partitioning the person space are equally likely crosslinguistically. Different theories have been proposed to constrain the set of possible person partitions that humans can represent, explaining their typological distribution. This article 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 behavioral 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.

Dates et versions

hal-03918013 , version 1 (02-01-2023)

Identifiants

Citer

Mora Maldonado, Jennifer Culbertson. Person of Interest: Experimental Investigations into the Learnability of Person Systems. Linguistic Inquiry, 2022, 53 (2), pp.295-336. ⟨10.1162/ling_a_00406⟩. ⟨hal-03918013⟩
6 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More