In typical development, word learning goes from slow and laborious to seemingly rapid and effortless. Typically developing 3-year-olds are so skilled at learning noun categories that they seem to intuit the whole range of things in the category from hearing a single instance named. As children learn these early vocabularies, they also seem to learn abstract generalities about how nouns map to different kinds of categories. These abstract kind-specific generalizations in turn speed up subsequent noun learning. This is not necessarily the case for children below the 15th-20th percentile on productive vocabulary (late talkers). Although there is continuity in vocabulary measures at the group level, the outcome for individual children cannot be accurately predicted on the basis of vocabulary production or comprehension. The proposed research seeks to understand the forces that create the developmental trajectories in the noun learning of individual children. To that end, I propose to 1) study different groups of children, including early and late talkers, 2) study individual developmental trajectories and the relation between experiences and the characteristics of those individual trends, 3) build and test computational models of noun learning through which we can predict the future word learning of a child from the composition of that child's current vocabulary and, 4) attempt to perturb these developmental trajectories by offering focused and enriching experiences. Understanding how different factors change the noun learning pathways in individual children with different developmental profiles should allow us to both predict the likely developmental trajectories for individual children and the sorts of experiences that may impact this trajectory.
By modeling how early word learning sets up and creates skilled word learning we may understand and ultimately remediate delays in early language by intervening in the developmental process.