Priors, Progressions, and Predictions in Science Learning: Theory-Based Bayesian Models of Children's Revising Beliefs of Water Displacement

Abstract

Despite sometimes noisy evidence (e.g., perceptual processing errors), young children are capable of predicting and evaluating events based on complex causal representations. Children rapidly revise their beliefs and learn scientific concepts - sometimes without prior knowledge of an underlying causal system. What might we need in our computational models of belief revision to similarly simulate children's behaviors when learning such causal systems? Building from experimental data of elementary school children's intuitive beliefs and predictions of water displacement, we propose three aspects of human inference and belief revision that warrant attention within the subfield of computational cognition. Each aspect is described by identifying the gaps between empirical findings and current computational implementations. Then, specific implementations of these aspects are built using models of theory-based Bayesian inference. First, we construct children's prior beliefs at the individual level based on their prior behavior. Second, we approximate children's learning using an 'optimal' Bayesian model, revealing the dynamics of belief revision trial-by-trial. Third, we investigate that the role prediction may have in facilitating learning. By performing these key computational steps, we find support for contemporary claims that children may be approximately 'Bayesian' learners and increase awareness of the importance of generating predictions in active learning.