Statistical Structures in Artificial languages Prime Relative Clause Attachment Biases in English

Abstract

The phenomenon of syntactic priming is well studied in the literature, but the mechanisms behind it are still under debate. In this study, we trained English-speaking participants in artificial language sequences with dependencies that are either adjacent or non-adjacent. The participants then wrote completions to relative clause (RC) fragments. We found that participants who learn non-adjacent dependencies in the artificial language, exhibit a bias to write high-attachment (non-adjacent) continuations for RCs, when compared to participants in a control condition who exhibit low-attachment (adjacent) biases in RCs. The implications for theories of syntactic priming and its relations to implicit learning are discussed.


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