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Open Access Publications from the University of California

Distributional learning of recursive structures


Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a language, some structures allow infinite self-embedding while others are more restricted. For example, English allows infinite free embedding of the prenominal genitive -s, whereas the postnominal genitive of is largely restricted to one level and to a limited set of items. Therefore, speakers need to learn from experience which specific structures allow free embedding and which do not. One effort to account for the underlying learning mechanism, the distributional learning proposal, suggests the recursion of a structure (e.g. X1’s- X2) is licensed if X1 position and X2 position are productively substitutable in the input. A series of corpus studies have confirmed the availability of such distributional cues in child directed speech. The present study further tests the distributional learning proposal with an artificial language learning experiment.

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