Speaker: Mora Maldonado (Edinburgh)
Title: Experimental investigations into the learnability of person systems
Time: 16:00 - 17:30
Location: Online, via Zoom
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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. I will present joint work with Jenny Culberston where we use an artificial language learning methodology to investigate the existence of universal constraints on person systems. This work constitutes the first experimental evidence for learnability differences in this domain.
In the first part of the talk, I will show you the results of three studies revealing that there is a universal basis for a set of primitives organizing the person space, which learners are sensitive to regardless of their native language. Besides showing a strong preference for feature-based systems, our findings additionally suggest that learners have an egocentric bias towards person partitions where the speaker is distinct from other categories. We explore the possibility of considering natural class-based similarity and speaker-distinctiveness as two independent forces influencing the learnability of person systems.
In the second part of the talk, I will show you the results of two studies where we explore how these person features relate to other indexical expressions, such as locatives.