Availability-Based Production Predicts Speakers' Real-time Choices of Mandarin Classifiers
- Meilin Zhan, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Roger Levy, Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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, Mandarin Chinese. In Mandarin, modifying a noun with a numeral obligatorily requires the use of a classifier. While different nouns are compatible with different specific classifiers, there is a general classifier that can be used with most nouns. When the upcoming noun is less predictable, using a more specific classifier would reduce the noun's surprisal, potentially facilitating comprehension (predicted to be preferred under Uniform Information Density, Levy & Jaeger, 2007), but the specific classifier may be dispreferred from a production standpoint if the general classifier is more easily available (predicted by Availability-Based Production; Bock, 1987; Ferreira & Dell, 2000). Here we report a picture-naming experiment confirming two distinctive predictions made by Availability-Based Production.
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