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Alison Gopnik is a leading professor in the field of psychology, focusing particularly on cognitive development and causal learning. Her research investigates how young children understand and interact with the world, employing a framework known as 'theory theory,' which posits that children form and revise intuitive theories about their surroundings much like scientists do. Gopnik's work highlights the advanced causal knowledge capacities of young children, revealing robust mechanisms of causal learning across physical, biological, and psychological domains. She collaborates with computer scientists, using Bayesian network formalism to elucidate the ways in which children derive causal structures from data. Through her research, she aims to demonstrate that children's learning mechanisms are far more powerful than previously assumed, contributing significantly to our understanding of child psychology and development.
The Mathematics Subject GRE is required for the Fall 2026 admissions cycle. General GRE is optional.