Artificial Language Learning in Children

Abstract

Artificial language learning methods—in which learners are taught miniature constructed languages in a controlled laboratory setting—have become a valuable experimental tool for research on language development. These methods offer a complement to natural language acquisition data, allowing researchers to control both the input to learning and the learning environment. A large proportion of artificial language learning studies has aimed to understand the mechanisms of learning in infants. This review focuses instead on investigations into the nature of early linguistic representations and how they are influenced by both the structure of the input and the cognitive features of the learner. Looking not only at young infants but also at children beyond infancy, we discuss evidence for early abstraction, conditions on generalization, the acquisition of grammatical categories and dependencies, and recent work connecting the cognitive biases of learners to language typology. We end by outlining important areas for future research.