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.
We recognize that the institutions of scientific research have often privileged some people at the expense of many others. In the Cocosci group, we know that we must do better and we value and make space for group members’ contributions to efforts at creating systemic change both within our lab and in the broader MIT community. Our group has been engaged in ongoing work to better understand and address structural racism, sexism, classism, and other barriers to full participation in research, as part of a long-term conversation about the bigger picture of our work. (You can find some of the resources we have compiled and found helpful by clicking the following link )