Exploring generalization using artificial language learning: comparing adult and child learners

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Such learning has been considered problematic for theories of acquisition: if learners generalize abstract patterns to new words, how do they learn lexically-based exceptions? One approach claims that learners use distributional statistics to make inferences about when generalization is appropriate, a hypothesis which has recently received support from Artificial Language Learning experiments with adult learners. Since adult and child language learning may be different, it is essential to extend these results to child learners. This presentation will address this issue