A complexity measure for intuitive theories

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 theories support inductive inferences about unobserved relations. We describe two behavioral experiments that test our approach, and show that our measure accounts better for subjective complexity than a measure developed by Nelson Goodman.