8 April 2016, ACLC Seminar, Naomi Feldman

Children have impressive statistical learning abilities. In phonetic category acquisition, for example, they are sensitive to the distributional properties of sounds in their input. However, knowing that children have statistical learning abilities is only a small part of understanding how they make use of their input during language acquisition. This work uses Bayesian models to examine three basic assumptions that go into statistical learning theories: the structure of learners' hypothesis space, the way in which input data are sampled, and the features of the input that learners attend to. Simulations show that although a naive view of statistical learning may not support robust phonetic category acquisition, there are several ways in which learners can potentially benefit by leveraging the rich statistical structure of their input.

For more information, see http://aclc.uva.nl/news-and-events/events/aclc-smart-seminar/all-events/ or http://ling.umd.edu/~nhf/