P22: Type Theory for Inference and Learning in Minds and Machines

Author: Felix Anthony Sosa

Abstract: A unique property of human reasoning is generating reasonable hypotheses to novel queries: if I asked you what you ate yesterday, you would respond with a food, not a time. The thought that one would respond otherwise can be seen as humorous at best, or pathological at worst. While understanding how people generate hypotheses is of central importance to cognitive science, no satisfying formal system has been proposed. Motivated by this property of human reasoning, we speculate that a core component of any reasoning system is a type theory: a formal imposition of structure on the kinds of computations an agent can perform and how they're performed. We further motivate this system with three empirical observations: adaptive constraints on learning and inference (i.e. generating reasonable hypotheses), how people draw distinctions between improbability and impossibility, and the ability people have to reason about things at varying levels of abstraction.