Testing the Cognitive Processing Model of Chinese Scalar Implicatures
Journal Title: JOURNAL OF ADVANCES IN LINGUISTICS - Year 16, Vol 6, Issue 3
Abstract
This paper reports our experimental study of the cognitive processing of scalar implicature in the Chinese language. Our experiments tested the two processing models: the Default model proposed by the neo-Gricean theorists and the Context-Driven model supported by the post-Gricean, which has long been debated in the pragmatic research field. Our Experiment 1 investigated whether scalar implicature might be generated in neutral contexts (neither enabling nor canceling the processing of scalar implicature). By manipulating the scalar trigger yixie (some) in sentence-initial and sentence-final conditions, the reading times on the target phrase qiyude/qitade (the rest/the others) showed facilitation in the sentence-initial. In Experiment 2, a picture-sentence verification of under-informative utterance was performed in order to test the cost of generating scalar implicature. The results showed the significant effects of the two factors (Quantifiers <all, some> and Picture-Match/NoMatch). The yes/no responses showed that native speakers of Chinese were sensitive to scalar implicature. However, the role of context in processing scalar implicature was not supported. Experiment 3 was designed to test the processing of scalar implicature in Upper-Bound (enabling the processing) and Lower-Bound (canceling the processing) contexts. The result of this online experiment was slightly in favor of the Context-Driven model. The findings of the three experiments were significantly in agreement with neither the CM nor the DM. We presumed that the processing of scalar implicature might be accounted for with an alternative model. This was the first study to use this experimental paradigm in the Chinese language, whose findings were expected to highlight a need for further studies in order to investigate the meaning processing models with various languages and cultures.
Authors and Affiliations
Si Liu, Chunmei Wang
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