Learning From Others: The Consequences of Psychological Reasoning for Human Learning

Abstract

From early childhood, human beings learn not only from collections of facts about the world but also from social contexts through observations of other people, communication, and explicit teaching. In these contexts, the data are the result of human actions-actions that come about because of people's goals and intentions. To interpret the implications of others' actions correctly, learners must understand the people generating the data. Most models of learning, however, assume that data are randomly collected facts about the world and cannot explain how social contexts influence learning. We provide a Bayesian analysis of learning from knowledgeable others, which formalizes how learners may use a person's actions and goals to make inferences about the actor's knowledge about the world. We illustrate this framework using two examples from causal learning and conclude by discussing the implications for cognition, social reasoning, and cognitive development.

Keywords: Bayesian model; learning; reasoning; social cognition.