My research is focused on answering the questions: What do human mental representations look like, and how do we learn them? I employ both empirical and formal (developing process level computational models) methods to investigate these issues. Broadly, I am interested in how humans learn structured representations, specifically relational representations (like “above”, “chases”, “ameliorates”), from unstructured examples, and use these representations in the service of solving problems. More specifically, my research has explored how children and adults learn relational concepts, how children develop the ability to reason by analogy, and how children and adults learn to recognize melody. More recently I have started work exploring how children and adults learn to reason about mathematical operations like addition and multiplication, and developing training regimens (motivated by predictions from a computational model for relation learning I have developed) to teach college age students fractions and how to reason from deductive syllogisms.