Semantic Regularities in Grammatical Categories: Learning Grammatical Gender in an Artificial Language

Abstract

The knowledge of grammatical categories such as nouns and verbs is considered to lie at the foundations of human language comprehension and production abilities. Words' distributional and phonological properties contribute to both adult and infant learning of grammatical categories. Here we investigate the contribution of semantic cues to the acquisition of grammatical categories using grammatical gender. Grammatical gender is traditionally considered a semantically arbitrary category, however there may be finer-grained correlations between semantic categories and gender classes. We taught adult native English speakers an artificial language with two gender-like classes, created via distributional, phonological and semantic properties. We demonstrate that the participants' performance on both an implicit and an explicit task is influenced by the semantic regularities in the two genders. We discuss the implications of the findings for theories of grammatical category learning and use.


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