We want to understand these everyday inductive leaps in computational terms. What is the underlying logic that supports reliable generalization from so little data? What are its cognitive and neural mechanisms, and how can we build more powerful learning machines based on the same principles?
These questions demand a multidisciplinary approach. Our group's research combines computational modeling with behavioral experiments in adults and children. Our models make strong quantitative predictions about behavior, but more importantly, they attempt to explain why cognition works, by viewing it as an approximation to ideal statistical inference given the structure of natural tasks and environments. While our core interests are in human learning and reasoning, we also work actively in machine learning and artificial intelligence. These two programs are inseparable: bringing machine-learning algorithms closer to the capacities of human learning should lead to more powerful AI systems as well as more powerful theoretical paradigms for understanding human cognition.
Current research in our group explores the computational basis of many aspects of human cognition: learning concepts, judging similarity, inferring causal connections, forming perceptual representations, learning word meanings and syntactic principles in natural language, noticing coincidences and predicting the future, inferring the mental states of other people, and constructing intuitive theories of core domains, such as intuitive physics, psychology, biology, or social structure.
Xu, F. and Tenenbaum, J. B. (in press). Word learning as Bayesian inference. Psychological Review.
Tenenbaum, J. B., Griffiths, T. L., and Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 10(7), 309-318.
Griffiths, T. L. and Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science 17(9), 767-773.
Blei, D., Griffiths, T. L, Jordan, M. I., and Tenenbaum, J. B. (2004). Hierarchical topic models and the nested Chinese restaurant process. Advances in Neural Information Processing Systems 16, 17-24. [Best Student Paper, NIPS 2003, Synthetic Systems Category.]
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E., and Blum, B. (2003). Inferring causal networks through observations and interventions. Cognitive Science 27, 453-489.
Tenenbaum, J. B., de Silva, V., and Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319-2323.