Semantic Cues Facilitate Structural Generalizations in Artificial Language Learning

Abstract

ABSTRACT Natural languages contain systematic relationships between verb meaning and verb argument structure. Artificial language learning studies typically remove those relationships and instead pair verb meanings randomly with structures. Adult participants in such studies can detect statistical regularities associated with words in these languages and their use of novel words will adhere to those statistical regularities. However, word use in natural languages is associated with more than distributional statistics. Using an artificial language learning paradigm, we asked how a relationship between verb meaning and sentence structure affected learning and structure generalization. Twenty-four English-speaking adults watched videos described in an artificial language with two possible sentence structures. Half of the participants (statistics-only condition) learned a language with no relationship between verb meaning and sentence structure. The other half (semantics condition) learned a language in which verb meaning predicted which structures a verb occurred in. Although all learners were able to comprehend the learned structures with novel verbs, participants in the semantics conditions made grammaticality judgments and productions with novel verbs that were more consistent with the target language than participants in the statistics-only condition. The availability of semantic cues to verb subcategory supports artificial language learning.