Date of Original Version



Conference Proceeding

Abstract or Description

Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that the subjective complexity of a theory is determined by the length of its representation in this language. This complexity measure helps to explain how theories are learned from relational data, and how they support inductive inferences about unobserved relations. We describe two experiments that test our approach, and show that it provide s a better account of human learning and reasoning than an approach developed by Goodman [1]

Included in

Psychology Commons



Published In

Advances in Neural Information Processing Systems, 20.