Artificial language learning

Abstract

AbstractArtificial language learning experiments have been used for decades by language acquisition researchers interested in how learners derive representations and make generalizations based on exposure to limited data. Recently, they have been co-opted by theoretical linguists to test hypotheses about how properties of human cognition shape natural language phonology, morphology, and syntax. Empirical evidence derived from these methods has been used to build more precise accounts of the link between how languages are learned (and processed) and cross-linguistic tendencies long-noted in the typological record. This chapter explains why artificial language learning is an important tool in the syntactician’s toolbox, what phenomena it has been used to study to date, and where research with these methods is heading in the future.