Using Data Science to Study Children’s Cognitive Development
Following the seminal work of Piaget, the traditional approach in cognitive development has focused on studying the structure of children’s knowledge in controlled situations (e.g., laboratory experiments). While this approach allows for precise inference about how children behave in certain tasks, it cannot provide an understanding of the social context within which knowledge emerges. In fact, it has been known, at least since Vygotsky, that children acquire new skills and concepts with the help of more competent members of society who scaffold the children’s learning, allowing them to attain skills that are just beyond their current abilities. In fact, much of the children’s abstract knowledge about the world, it has been argued, is mediated through discussions with their parents/caregivers.
In this talk, I explain how new advances in Data Science, especially in Natural Language Processing (NLP), allow us to 1) account for what and how information is presented to children by their parents through language, and 2) make precise predictions about the way this information can be used by children in controlled designs. Thus, NLP can create a fruitful synergy between controlled and naturalistic research methods. More generally, I argue that a complete theory of cognitive development requires interdisciplinary research across computer science and psychology.
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