Improving our understanding of how children learn language has profound implications for a variety of fields, ranging from nature versus nurture debates to therapies for treating language impairments. Learning a language involves learning a wide variety of different things, including the words of the language and the way they combine to form phrases and sentences, as well as the distribution of sounds of the language. Little is known about the way that all these aspects of language learning interact, and the goal of this research is to identify which aspects are likely to interact and whether this interaction is synergistic. With support from the National Science Foundation's programs in Linguistics and in Human Language & Communication, Dr. Mark Johnson and Dr. Katherine Demuth will use Bayesian statistics to construct a variety of mathematical models of the language acquisition process, and by comparing these to experimental data, identify the most important synergistic interactions involved in language learning.
