ICS Colloquium: Eliana Colunga, PhD, University of Colorado Boulder

Title: Using computational models to understand, predict, and nudge word learning trajectories

Presenter: Eliana Colunga, PhD, Associate Professor, Department of Psychology & Neuroscience, University of Colorado Boulder

Abstract: One of the important accomplishments of children in their first few years of life is learning the language or languages of their environment. Legend has it that toddlers can do this task masterfully and effortlessly, though it is acknowledged that this is not the case for all children. In this talk I will present work using computational models to understand, predict, and intervene in early word learning. The idea is that language is a highly structured system of similarities and relationships, and that children become more and more skilled learners by exploiting the regularities in this structure. If this is the case, we may be able to nudge developmental trajectories by shifting the structure present in a child’s language environment. I will discuss computational models that can be used to characterize and predict the early vocabulary trajectories of children with different language abilities, present evidence suggesting that it is indeed possible to make model-based target word selections that will have a greater impact on subsequent vocabulary growth, and discuss different ways of delivering this environmental “nudge”.

Bio: Dr. Colunga is an Associate Professor in the department of Psychology and Neuroscience and the Computer Science Department, and a faculty fellow with the Institute of Cognitive Science at CU Boulder. Dr. Colunga received her PhD in Computer Science and Cognitive Science from Indiana University and her MS in Artificial Intelligence and BS in Computer Science from the Instituto Tecnologico y de Estudios Superiores de Monterrey (ITESM) in Monterrey, Mexico. Her lab studies interactions between language and cognition using cross-linguistic, developmental, and computational modeling methods, by building computer models that simulate how young children learn words in different situations to understand how language develops. Her work has been funded by the John Merck Scholars Foundation, the National Institute of Health, and the National Science Foundation.