Michael C. Frank is a developmental psychologist and cognitive scientist. His work helps us understand how we go from being speechless infants to toddlers who can talk and understand language. He uses computational models and experiments with infants, children, and adults to understand human language acquisition and its relationship to other aspects of cognition, including social interaction and conceptual structure.
My plans for the fellowship period
Every typically developing child learns their native language. Yet language learning varies tremendously across cultures and individuals. While the most linguistically sophisticated two-year-olds will be forming complete sentences, many others will be barely uttering a handful of words. In some cultures, these words will be the names for things, while in others they will be words for actions or properties. This combination of universality and variability is virtually unknown in human cognition or in biological systems more generally. What are the sources of this variability? How much can be explained by culture-, language- and family-level differences in children’s language input, and how much is due to variability endogenous to the child? In the next three years, my research will be focused on understanding the nature of this developmental variability in early language learning through three related components: 1) building a database of word learning outcomes across cultures and languages, 2) gathering/analyzing data about children’s early language input across cultures, and 3) using computational models to synthesize across these datasets and quantify the effects of different factors on language outcomes.
How will my work change children’s and youth’s lives?
The goal of my work is to develop both theory and data that bear on questions about how children learn language and why individual children differ from one another. A better understanding of these differences will allow for the design and evaluation of interventions to improve early language outcomes. For example, many current interventions target increasing the quantity of language that children hear, without a strong understanding of what aspects of language are most useful for learners: in other words, with a focus on quantity alone, not quality. And critically, markers of the quality of children’s language input may vary from child to child, from language to language, and across development. Linguistically, what’s best for a two-year-old in a high socio-economic status household in the United States is not the same thing that’s best for a one-year-old in rural India. Our work will help develop data-driven insights into how these two situations – and many others – differ from one another.