Computational and developmental investigations of children’s self-representations and learning decisions

Asaba, Mika, Boston MA

Investigators

Abstract

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Drs. Julian Jara-Ettinger and Julia Leonard at Yale University, this postdoctoral fellowship award supports an early career scientist studying the development of children’s self-representations. Children’s understanding of the self––what they like, what they know, what they are good at––has a deep influence on their learning and achievement. However, we lack a clear understanding of how exactly children develop views of the self, how these views are affected by their social interactions, and how they impact important real-world behaviors, such as children’s learning decisions. This research will advance fundamental scientific knowledge on children’s self-representations by developing a formal, quantitative theory of how children revise self-representations in social contexts, and how these self-representations impact their learning. The studies will combine computational and developmental approaches to test the hypothesis that children rationally construct and revise self-representations by integrating their prior experiences with social interactions. First, we will build a computational model that combines self-observations (e.g., failures and successes) with social feedback (e.g., what others think of the self) to explain learning about the self in social contexts. This model will be exhaustively tested and refined through studies with adults and compared against published developmental findings on relevant work. Second, we will use our model and alternative lesioned models to generate and test quantitative predictions for the developmental trajectory of children’s (3- to 6-year-olds) reasoning about the self. These models will make different assumptions about children’s sensitivities to self-observations and social feedback and if and how they integrate these sources of information. Our developmental experiment will systematically vary children’s performance on toys and a teacher’s beliefs about their competence, and test how these inform important learning behaviors: children’s persistence and challenge-seeking. By integrating state-of-the-art computational and developmental methods, this project will shed light on a deeply important learning problem for children: who they are, and what they can do. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.