Year: 2019
Type: preprint
Abstract: Speakers often face choices as to how to structure their intended message into an utterance. Here we investigate the influence of contextual predictability on the encoding of linguistic content manifested by speaker choice in a classifier language. In English, a numeral modifies a noun directly (e.g., three computers). In classifier languages such as Mandarin Chinese, it is obligatory to use a classifier (CL) with the numeral and the noun (e.g., three CL.machinery computer, three CL.general computer). While different nouns are compatible with different specific classifiers, there is a general classifier "ge" (CL.general) that can be used with most nouns. When the upcoming noun is less predictable, the use of a more specific classifier would reduce surprisal at the noun thus potentially facilitate comprehension (predicted by Uniform Information Density, Levy & Jaeger, 2007), but the use of that more specific classifier may be dispreferred from a production standpoint if accessing the general classifier is always available (predicted by Availability-Based Production; Bock, 1987; Ferreira & Dell, 2000). Here we use a picture-naming experiment showing that Availability-Based Production predicts speakers' real-time choices of Mandarin classifiers.
Source: arXiv (Cornell University)
Authors: Zhan, Meilin, Levy, Roger
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2945496388