Computational models of development, social influences

Abstract

In the article we argue that past Bayesian approaches that model children's learning from data are missing an important element - the role of other people in generating that data. We propose that children take the origin of data into account when learning, which can be understood through ideal observer analyses of the social situation. Moreover, when observing evidence, children are not just learning from others, but also about others. We review recent literature suggesting that children can make inferences about the knowledge and goals of the individual selecting the data and use this knowledge to bolster learning from this evidence.