Funded Grants

Understanding early language environments: Analytic techniques and outcome predictions

Questions about the role of the environment are fundamental to human cognition. How are we shaped by our environments, and how do constraints on human developmental, social and cognitive behaviors shape the environment from which we learn? To understand the role of the environment in cognitive development is to understand which properties of the environment are essential for healthy development, to understand which aspects of development are malleable, and to understand how the environment might drive developmental change and new knowledge. I will address these questions with respect to language development in children. My focus is not on simply collecting new data about the environment, but on conceptual, methodological and mathematical advances that will enable us to ask and better answer questions about the massive scale of everyday experience.

The fields of cognitive science and cognitive development are at an exciting point in time with respect to questions about the learning environment. Wearable sensors, recording devices, machine learning and computing power are allowing researchers to collect and analyze naturalistic datasets more easily and on a larger scale than ever before. The transformative potential of large, naturalistic datasets is particularly evident in the field of language development. These advances have powerful implications for healthy childhood development, because both the quantity and quantity of language are strong predictors of language development, as well as later achievements across many different domains, including formal education and reading. Analyses of large, naturalistic datasets will fundamentally change our beliefs about what language is, how it is learned, the input language upon which language learning operates, and what is unique versus common across individual children’s development.

My research program has three broad goals. These goals have implications not only for understanding the mechanisms that underlie language development, but also for understanding how the early language environment prepares the child for subsequent learning. First, to understand the best ways to analyze large, naturalistic language data sets. This includes best practices in data sampling, and gaining an understanding of the sources of variability between and within individuals, and how to best operationalize those dimensions of the input in data analyses. Second, to understand the contribution of contexts (e.g., breakfast-time, dinnertime, storytime) to our measurements of language input, how different contexts vary from each other, and vary from the rest of the day. Third, to develop better links between aspects of children’s language environments and learning outcomes. This includes understanding how shifts in the language environment might predict different learning outcomes, as well as understanding how distributional differences across individuals or across items (e.g., words, categories, sentences) might predict learning outcomes in those individuals or items.

This research will advance the field’s best practices for sampling and analyzing large naturalistic datasets in ways that are consistent with both the mathematical properties of language distributions and the reality of children’s day-to-day experiences with language. These advances will have enormous implications for our theoretical understanding the role of the environment in early cognitive development and the mechanisms of early language learning.