The proposed workshop, to be held at the Banff International Research Station for Mathematical Innovation and Discovery, aims to capitalize on a major new direction in research on formal models of human cognition, exploring probabilistic models of learning and cognitive development. The technical advances that have been made in the use of probabilistic models over the last twenty years in statistics, computer science, and machine learning have made this research enterprise possible, resulting in a set of mathematical and computational tools that can be used to build explicit models of psychological phenomena. By indicating the conclusions that a rational learner might draw from the data provided by experience, Bayesian models can be used to investigate how nature and nurture contribute to human knowledge. Although computational models have been used to aid empirical research on learning in the past, the lack of communication and collaboration between formal theorists and experimental laboratories has always been a stumbling block. This workshop will bring together two groups of researchers: experts in computational modeling and scientists studying cognitive development. The goal is both to report and to discuss the progress we have made so far with existing collaborative research and to foster future collaborations between computational scientists and learning researchers, leading to new insights and new models of how people learn and develop. A special emphasis will be placed on developing strategies of the application of these insights to educational research and practice.
