Exploring the Role of Representation in Models of Grammatical Category Acquisition

One major aspect of successful language acquisition is the ability to generalize from properties of experienced items to novel items. We present a computational study of artificial language learning, where the generalization patterns of three generative models are compared to those of human learners across 10 experiments. Results suggest that an explicit representation of word categories is the best model for capturing the generalization patterns of human learners across a wide range of learning environments. We discuss the representational assumptions implied by these models.