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Faculty & Research

Elizabeth Bonawitz

David J. Vitale Associate Professor of Learning Sciences

Elizabeth Bonawitz

Email:  [javascript protected email address]
Personal Site:   Link to Site
Vitae/CV:   Elizabeth Bonawitz.pdf
Faculty Assistant:  Claire Goggin

Profile

Elizabeth Bonawitz is the David J. Vitale Associate Professor of Learning Sciences at Harvard University. Her work focuses on the basic science theories of learning with the broader goal of informing educational practice. Her research bridges two research traditions: cognitive development and computational modeling. Specifically, Bonawitz’s empirical approach focus on the structure of children's early causal beliefs, how evidence and prior beliefs interact to affect children's learning, the developmental processes that influence children's belief revision and curiosity, and the role of social factors (such as learning from others) in guiding learning. Bonawitz received her Ph.D. from MIT in the brain in cognitive sciences in 2009 working with Dr. Laura Schulz. She then completed a post-doctoral fellowship at University of California, Berkeley with Thomas Griffiths and Alison Gopnik (2009-2013). She was an Assistant and Associate professor of psychology at Rutgers University, Newark from 2013 until 2020 when she moved to Harvard. Bonawitz is the recipient of the James McDonnell Foundation Understanding Human Cognition Scholar Award and the Jacobs Early Career Research Fellowship. Her work is additionally currently funded by several NSF grants, the Caplan Foundation, and the Templeton Foundation. Her research has been published in top journals in psychology, cognitive science, and education. Additionally, she has served as Associate Editor for Cognitive Science (journal) and is on the governing board of the Cognitive Development Society and Children Helping Science

Areas of Expertise
Research

Research
Elizabeth Bonawitz focuses on the basic science theories of learning with the broader goal of informing educational practice. Her research bridges two research traditions: Cognitive Development and Computational Modeling. Specifically, BonawitzÂ’s empirical approach focus on the structure of children's early causal beliefs, how evidence and prior beliefs interact to affect children's learning, the developmental processes that influence children's belief revision and curiosity, and the role of social factors (such as learning from others) in guiding learning.

Sponsored Projects

 

Innovating Developmental Science with an Online, Scalable Meta-Science Platform for Investigating Cognitive Development During Early Childhood (2021-2024)
University of Texas - Dallas

Now, more than ever, developmentalists have recognized the need for reaching representative samples of participants, reaching parents across the country, and creating opportunities to engage in science beyond traditional University lab-locked research (Sheskin et al, in press). Concurrently, there has been a reckoning in psychology, with an increasing emphasis on examining the degree to which our research is reproducible, beyond upper SES, white populations. In addition, with the COVID pandemic, researchers have realized the need for developing robust tools for online testing. Thus, online testing and replicability have been two huge trends in psychology for the past decade, but they have been previously hard to implement in children because the idea was that they had to be in lab. In this proposal we tackle these major themes of the developmental sciences with three Aims. (1) We will create a transformative online infrastructure that will support a “big science" approach to research with children through virtual participation options from anywhere in the county. (2) We will conduct a massive, multi-site online replication and extension of several classic studies in cognitive development. (3) We will leverage this platform to build a model forfuture online collaborative developmental science, including conducting new experiments and exploring new approaches to research. Together with collaborators from seven other institutions, I will advise on the implementation of these goals, while also supporting the development, recruitment, and testing of participants through the tobe built online infrastructure. Costs will also support the research for undergraduates internship, where student from underrepresented groups will be able to collaborate in carrying out the research and gain valuable experiences in STEM. The proposed research projects are synergistic to ongoing experiments in the lab and thus also serve to buoy concurrent research programs.

 

Learning in Early Childhood: A Computational Cognitive Developmental Approach (2021-2024)
James S. McDonnell Foundation, Inc.

The success of every course of action we choose in our daily lives depends on the accuracy of our intuitive beliefs. Given the importance of these beliefs to our survival, one would think that we would be strongly motivated to get our picture of the world right. When there is an opportunity to learn, we should explore. Yet, by adulthood, it seems that the desire to feel right often outweighs the drive to explore new ideas so that we can be accurate. For example, typical adults will choose to give up a chance at more money in order to avoid reading information that conficts with their beliefs; in fact, adults rate the experience of simply listening to viewpoints that confict with their own as comparable in pain to getting a tooth pulled (Frimer, Skitka, Motyl, 2017). Epistemic indifference does not extend all the way back into early childhood, however. My work has demonstrated that young children are sophisticated learners who are highly motivated to explore. Yet we currently know very little about the nature of the individual drives and early childhood experiences that maintain and foster this love of learning. Instead, our culture has created many experiences that diminish it. For example, as children progress through the American school system, they rate themselves as less engaged and less interested in learning new things (Gallup, 2017). It thus becomes critical to ask, how do we foster and maintain minds that love to learn? My research program aims to provide answers to these questions. Computational models of learning provide a powerful approach to specify and explore these questions. Furthermore, combining the tools of computational modeling and developmental psychology creates a unique research program that can characterize the role of early childhood experiences in shaping the epistemic stance taken throughout the lifespan.

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