How Experience Shapes Mathematical Intuitions: Computational Explorations

McClelland will describe computational modeling research indicating how experience with both formulas and concrete problems can shape perception and intuition about mathematical expressions and their meaning. The models rely on intrinsically gradual learning in neural networks, explaining why acquisition of mathematical intuition can be slow and addressing the role of experience in creating a situation in which someone who does not yet know something may gradually acquire readiness to learn it.