We use empirical methods and formal tools to uncover the mechanisms of human learning and inference.
We study the computational basis of human learning and inference. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, we try to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating “style” and “content” in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences, predicting the future.
We approach these topics with a range of empirical methods — primarily, behavioral testing of adults, children, and machines — and formal tools — drawn chiefly from Bayesian statistics and probability theory, but also from geometry, graph theory, and linear algebra. Our work is driven by the complementary goals of trying to achieve a better understanding of human learning in computational terms and trying to build computational systems that come closer to the capacities of human learners.
If you are an undergraduate at MIT interested in research with our group, please send a short email about your interests and background (as specific as possible), with resume/CV and any previous research papers, to firstname.lastname@example.org.