How do young children learn so much about the world so quickly and accurately? And how can they learn so much when, at the same time, they often seem so irrational and unpredictable? The research in this proposal will help answer these questions by bringing together ideas from computer science with research on very young children. The basic idea is that young children learn in some of the same ways as the most powerful machine-learning programs. Both the children and the computers explore a wide range of more and less likely possibilities. Moreover, children may sometimes actually explore more widely than adults and so be smarter or at least more open-minded learners. Some of their apparently irrational play, like their wide-ranging pretend play, may really reflect powerful learning methods.
This work should have significant broader impact for educational practice. If we understand children's basic natural rational learning mechanisms we can use those mechanisms to help teach science more effectively. In particular, there are significant practical questions about how we can leverage children's spontaneous play to help them learn. Similarly, this research has impact for studies of developmental disabilities such as autism and mental retardation. There is reason to think that children with these syndromes may have particular difficulty with the kind of learning about possibilities that is facilitated by pretend play, and understanding that learning may help us understand and remedy these difficulties.
