The author discusses the role of scales in determining a child's success on computing scalar implicatures (SIs). The author argues that computing SIs based on "world-knowledge-based" scales, such as <hot, warm>, is more challenging than computing SIs based on "logical" scales, such as <all, some>, because computing SIs based on the former requires using more world-knowledge than computing those based on the latter. The author tested 40 children, aged 4;3-7;7, on computing SIs based on "logical" scales and "world-knowledge-based" scales, and found that children fared significantly better on computing SIs based on "logical" scales. Some of the challenges presented by "world-knowledge-based" gradable adjective scales are the role of speaker standards vs. context standards, the degree of contrast between adjectives, and I-language uses of gradable adjectives. At the same time, children's performance on one "logical" scale, the <and, or> scale, was poor. The author accounted for this in the light of the observation in Geurts (2006) that SIs based on the <and, or> scale arise only in a limited range of contexts. The author argued that, since children are exposed to fewer instances of or SIs in their input, this scale takes longer to get lexicalized. Thus the child's success on computing SIs based on a given scale cannot be predicted based on broad classes of scales alone (such as "logical" vs. "world-knowledge-based") but rather is a function of the challenges presented by individual scales.
Proceedings of the 3rd Conference on Generative Approaches to Language Acquisition North America (GALANA 2008)
edited by Jean Crawford, Koichi Otaki, and Masahiko Takahashi
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