Grantee: University of Connecticut, Storrs, Connecticut, USA
Researcher: Sumarga Suanda, Ph.D.
Grant Title: How the Dynamics of Early Interactions Shape Word Learning
https://doi.org/10.37717/220020549
Program Area: Understanding Human Cognition
Grant Type: Scholar Award
Amount: $600,000
Year Awarded: 2018
Duration: 6 years
The typical child is exposed to a sea of data: countless words, numerous objects, and many social interactions. Despite what seems to be a daunting flood of information, children learn quickly and efficiently about their worlds. My laboratory seeks to understand how this prodigious learning takes place. The focus of our work is early word learning, a domain in which children achieve remarkable feats of learning (the average six-year-old knows 14,000 words) and a topic that lies at the cross-sections of cognitive, social and language development. The fact that early word learning is marked by large individual differences with clinical, educational, and societal implications highlights the importance of understanding its underlying processes.
The central aim of our research over the next several years is to investigate how early learning experiences shape word learning. Although there is general agreement that experience matters for later learning and development, through what mechanisms those experiences lead to robust learning is not well understood. Three related lines of research form our agenda in the coming years. First, we will investigate the inter-connections between different dimensions of early language learning experiences (the quantity and quality of child-directed speech, parental responsivity in early conversations, the perceptual transparency of referents, etc.). The inter- connections between these dimensions create conceptual and methodological barriers for understanding how early experience shapes learning. The proposed research seeks to bring these issues to the forefront and to work towards a framework for meeting these challenges.
A second goal of our research is to understand the interaction dynamics that give rise to individual differences in language learning experiences. Individual differences in global-level metrics of early language experience (e.g., the amount and types of speech heard) are well- established predictors of children’s language development. These differences however, accrue over many individual interactions and conversations. Through investigating the real-time dynamics of how individual conversations are initiated, how turn-taking within a conversation is maintained, and how conversational topics shift and stay, we will come to understand with more precision where those highly predictive global-level metrics come from. The final goal of our research is to examine the well-established relationship between early experiences and later learning through a computational lens. By attempting to simulate and re-create within different computational frameworks the observed link between input (early experiences) and output (later learning patterns), we hope to shed light on the possible mechanisms by which early experiences relate to learning, as well as to offer concrete hypotheses for the types of real-world input-output relationships one should expect under different frameworks of learning.
In pursuing these lines of research, my laboratory will bring together different theoretical and methodological approaches, investigate how experiences change as a function of development and context, as well as examine how both children and their caregivers shape early learning. Ultimately, our goal is not only a deeper understanding of early learning but also an understanding for how best to support children whose learning is delayed.