Artificial grammar learning of shape-based noun classification

Abstract

Systems of noun classification serve to categorize entities based on a set of semantic and/or phonological features. Previous work, for the most part focused on gender-based classes, has suggested that learners acquiring such systems rely primarily on phonological cues, while semantic cues are used only weakly. We show, using an artificial language learning task with adults, that semantic information alone is sufficient to learn a realistic shape-based classification system, challenging the view of phonology bias. Further, our results show that compared to learners exposed to semantically cohesive categories, learners trained on randomly assigned classes are less successful at recalling the category of exposure items. This finding suggests that, contrary to memory-based theories of learning, categories are not necessarily formed by abstraction from memorized exemplars, but can instead be constructed from lower-level properties that category members share.


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