EITM: Minimization of complexity in human concept learning

This project investigates how human learners form generalizations from examples, focusing on how conceptual complexity influences learning. When inducing concepts, as when a learner forms an abstraction of the concept "chair" after viewing only a few individual chairs, human learners have a bias towards simplicity; i.e., they tend to induce the simplest generalizations consistent with the examples. The exact meaning of the term "simple," however, is notoriously difficult to capture in a rigorous theory. This project draws on recent progress in quantifying conceptual simplicity and complexity in ways that are both mathematically sound and psychologically accurate. The project seeks to generalize this progress to apply to a wider range of human conceptual types than has previously been possible, including "fuzzy" probabilistic concepts and concepts defined over continuous features. The project involves both mathematical modeling and extensive experiments on human subjects learning a wide variety of concepts. By extending our understanding of complexity-minimization in human learning, the project aims to build a more complete account of the mechanisms underlying human learning. This project has many potential scientific benefits, including a greater understanding of human learning and the possibility of more effective automated learning mechanisms. More broadly, this project has the potential to help quantify what makes some concepts inherently easier for humans to learn than others, which could have direct applications to education practices and to treatment understanding of learning disorders.