Computational models of early language acquisition 1 Computational models of early language acquisition

Computational models of early language acquisition 2 How do children acquire the sounds, words, and structures of their native language? A wealth of recent evidence suggests that probabilistic learning mechanisms play a role in language acquisition. Nevertheless, the structure of these mechanisms is controversial and it is still unknown how broadly they apply to the tasks faced by language learners. Computational models can serve as formal theories of probabilistic learning by instantiating proposals about the learning mechanisms available in early language acquisition. However, fulfilling this promise requires that models be evaluated on two grounds: their sufficiency—whether they are able to learn aspects of language given appropriate input—and their fidelity—whether they fit the patterns of success and failure shown by human learners. I review experimental and computational evidence for the application of probabilistic learning across a range of acquisition tasks and argue that models of probabilistic learning succeed when they use expressive representations to capture complex regularities in the input and when they implement a parsimony bias. Computational models of early language acquisition