Alison Gopnik, PhD

My research explores how young children come to know about the world around them. The work is informed by the "theory theory" -- the idea that children develop and change intuitive theories of the world in much the way that scientists do. Most recently, we have been concentrating on young children's causal knowledge and causal learning across domains, including physical, biological and psychological knowledge. In collaboration with computer scientists, we are using the Bayes Net formalism to help explain how children are able to learn causal structure from patterns of data, and we have demonstrated that young children have much more powerful causal learning mechanisms than was previously supposed.

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Emily Rose Reagan

Hi there! My name is Rose and I manage Alison Gopnik’s Cognitive Development and Learning Lab. I am a Pittsburgh native and 2019 graduate of Chatham University, where I studied Psychology and Gender Studies with a certificate in Women’s Leadership.

I am passionate about studying social cognitive development in the realm of social identity, and I’m fascinated by how kids learn rich concepts of identity such as race and gender through causal learning, group dynamics, and sociality in general. I hope to study these topics in a PhD program in the years to come. Outside of lab, I like reading fiction, watching scary movies, and hanging out with my delightfully orange tabby cat, Purrogi.

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I am a postdoctoral researcher in Professor Alison Gopnik's Cognitive Development Lab and Professor Tania Lombrozo's Concepts and Cognition Lab. I study explanation and causal reasoning in adults and across development. I approach explanation both as a process and a product: I investigate cognitive consequences of engaging in explanation (process) and of producing different types of explanation, such as formal, causal-mechanistic, teleological, structural, mathematical, etc., of different levels of complexity (product). My research on causal reasoning examines the role of stability, or robustness of causal relationships across varying background circumstances. In my current research projects, I explore how explanation and causal reasoning contribute to learning, inductive inference, and decision-making, and how this relationship varies with context, domain-specific experience and development.

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My research uses behavioral experiments and computational methods to study how people develop conceptual systems and leverage them to accomplish their goals. My work revolves around an idea called the child as hacker—that symbolic programs provide the most compelling account of sophisticated mental representations and that learning is analogous to a particular style of programming called hacking. I want to understand how human programmers write code–the goals, activities, and tools they use to make code better–and apply these insights to better describe learning in children and adults.

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How do differing cultural & ecological environments shape the developing mind? My research examines this question from a cross-cultural, developmental perspective, focusing on how preferences and decision-making vary across diverse cultures. My primary fieldwork takes place among the Shuar, an indigenous forager-horticulturalist group living in Amazonian Ecuador. I received my BS in Anthropology from UCLA, and my PhD in Anthropology at Yale University.

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I am fascinated by the evolution of great ape social cognition. Specifically, my research explores how humans and our closest living phylogenetic relatives, chimpanzees and bonobos, have evolved to recognize, remember, and represent one another. I use eye-tracking technology and other non-invasive methods with chimpanzees and bonobos living in zoos and sanctuaries around the world to explore how they attend to and process their social worlds. I graduated from Duke University in 2016 with a Bachelor of Science, and earned my PhD from Harvard University in the department of Human Evolutionary Biology in Spring 2022. I was awarded the UC President’s Postdoctoral Fellowship to pursue postdoctoral research here at UC Berkeley with Profs. Alison Gopnik and Jan Engelmann. Besides hanging out with great apes, I love swimming in the ocean, making pottery, and hiking in the Berkeley Hills.

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Eliza Kosoy, 5th Year

I'm interested in the intersection of artificial intelligence and child development. How do kids learn new concepts so quickly and with so little data? How can we model this to create faster machine learning. I study this in two different domains. One through intersections with Brenden Lake's work on one-shot-learning and Omniglot. The other through curiosity and exploration with various faculty at BAIR (Berkeley Artificial Intelligence Research). Previously worked on intuitive physics with Josh Tenenbaum, Tomer Ullman and Liz Spelke. 

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I am interested in studying how children make sense of and explore objects in their everyday lives. Questions I hope to address with my current studies include: What sorts of perceptual priors and biases emerge when children encounter a novel object? On what bases do they categorize objects, as well as how flexible and creative are they in their formulation of categories? How do these compare with the approaches of adults and neural networks take in object reasoning? Finally, what are takeaways from children’s object perception and exploration behaviors that can help us optimize learning in both humans and machines?

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I am interested in the way children can learn so much about the physical and social world so rapidly and flexibly. My research focuses on leveraging experimental and computational approaches to better understand the links between childhood, learning, and exploration. How can artificial intelligence be inspired by what we know about children’s learning? Why is the development of a theory of mind so crucial to our understanding of what makes humans social beings? How can we build mentalizing machines?

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Fei Dai, 1st Year

I’m interested in understanding how children make sense of the world as a flexible learner. Questions I hope to address in my research include: How do children build causal models of the world? How do children effectively generalize prior experiences to novel situations? What cognitive mechanisms shape their exploration and learning? And lastly, how does the learning process of artificial agents diverge from that of children, and what insights can we draw from children's learning strategies to create more human-like artificial intelligence?

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