Generalization of Syntactic Knowledge in Semiartificial Language Learning
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Tao, Y., & Williams, J. (2018). Generalization of Syntactic Knowledge in Semiartificial Language Learning. Language Learning, 68 (4)https://doi.org/10.1111/lang.12299
© 2018 Language Learning Research Club, University of Michigan A hallmark of grammatical knowledge is the ability to parse novel syntactic structures. Previous artificial language studies have examined learning hierarchical structures, but few have involved meaningful language and shown generalization to novel structures. This study addressed this issue using the semiartificial language paradigm. The experimental group of monolingual English speakers was exposed to singly embedded sentences combining Chinese syntax with English lexis. A novel testing procedure was used where participants made plausibility judgments on grammatical sentences featuring two-level embedding. Only correct application of the rules underlying the training sentences permitted correct discrimination of plausible and implausible sentences. The experimental group performed above chance, an untrained control group performed at chance, and an untrained and unaware Chinese control group showed above-chance performance. Confidence judgments and rule recognition analysis suggested that learning was implicit. We concluded that the experimental group demonstrated structural generalization and might have acquired the syntactic rule system.
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External DOI: https://doi.org/10.1111/lang.12299
This record's URL: https://www.repository.cam.ac.uk/handle/1810/289792