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Abstract Previous work has shown that English native speakers interpret sentences as predicted by a noisy‐channel model: They integrate both the real‐world plausibility of the meaning—the prior—and the likelihood that the intended sentence may be corrupted into the perceived sentence. In this study, we test the noisy‐channel model in Mandarin Chinese, a language taxonomically different from English. We present native Mandarin speakers sentences in a written modality (Experiment 1) and an auditory modality (Experiment 2) in three pairs of syntactic alternations. The critical materials are literally implausible but require differing numbers and types of edits in order to form more plausible sentences. Each sentence is followed by a comprehension question that allows us to infer whether the speakers interpreted the item literally, or made an inference toward a more likely meaning. Similar to previous research on related English constructions, Mandarin participants made the most inferences for implausible materials that could be inferred as plausible by deleting a single morpheme or inserting a single morpheme. Participants were less likely to infer a plausible meaning for materials that could be inferred as plausible by making an exchange across a preposition. And participants were least likely to infer a plausible meaning for materials that could be inferred as plausible by making an exchange across a main verb. Moreover, we found more inferences in written materials than spoken materials, possibly a result of a lack of word boundaries in written Chinese. Overall, the fact that the results were so similar to those found in related constructions in English suggests that the noisy‐channel proposal is robust. 

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.