This project will investigate the roles of comparison and explanation in conceptual change, in both children and adults. Comparison seems to play a major role in human learning: People compare multiple situations to help construct models, compare models with data to see how well they explain it, and compare models to each other to determine what data might distinguish between them. A key problem is deciding when to accept, revise, or abandon a model, based on the successes and failures of its explanations. This problem will be investigated by a combination of theoretical work, computer simulation, and psychological experimentation. The computer simulation will focus on how comparison processes are used in the construction, challenging, and testing of causal models, and on how learners should abandon, revise, or continue with its current models as a consequence. The phenomena modeled will be drawn both from the developmental literature (e.g., learning what kinds of things can float, and what quantities remain constant under physical transformations (i.e., conservation phenomena)) and from new studies of adults learning causal models. By adding to the science base needed to create software systems that can learn more like people do, this project will have Broader Impacts through improvement of intelligent tutoring systems, learning environments, and decision-support systems, and in general on the way intelligent systems are built.
