Classifiers as Count Syntax: Individuation and Measurement in the Acquisition of Mandarin Chinese.
The distinction between mass nouns (e.g., butter) and count nouns (e.g., table) offers a test case for asking how the syntax and semantics of natural language are related, and how children exploit syntax-semantics mappings when acquiring language. Virtually no studies have examined this distinction in classifier languages (e.g., Mandarin Chinese) due to the widespread assumption that such languages lack mass-count syntax. However, Cheng and Sybesma (1998) argue that Mandarin encodes the mass-count at the classifier level: classifiers can be categorized as "mass-classifiers" or "count-classifiers." Mass and count classifiers differ in semantic interpretation and occur in different syntactic constructions. The current study is first an empirical test of Cheng and Sybesma's hypothesis, and second, a test of the acquisition of putative mass and count classifiers by children learning Mandarin. Experiments 1 and 2 asked whether count-classifiers select individuals and mass classifiers select nonindividuals and sets of individuals. Adult Mandarin-speakers indeed showed this pattern of interpretation, while 4- to 6-year-olds had not fully mastered the distinction. Experiment 3 tested participants' syntactic sensitivity by asking them to match two syntactic constructions (one that supported the mass or portion reading and one that did not) to two contrasting choices (a portion of an object and a whole object). A developmental trend was observed for the syntactic knowledge from 4-year-old children into adulthood: adults were near perfect and the older children were more likely than the younger children to correctly match the contrasting phrases to the objects. Thus, in three experiments we find support for Cheng and Sybesma's analysis, but also find that children master the syntax and semantics of Mandarin classifiers much later than English-speaking children acquire knowledge of the English mass-count distinction.
Department of Psychology, Harvard University.
Peggy
P
Li
David
D
Barner
Becky H
BH
Huang