Classifying Categorization Using State Trace Analysis and Hierarchical Bayesian Modeling

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Humans must categorize the world in order to understand it. Categories like healthy versus poisonous, valuable versus overpriced, or even apples versus oranges coordinate our thinking and guide our actions in the world. In line with their centrality in cognition, current theories of categorization and category learning are numerous and diverse. The goal of this research project is to gather together these diverse theories and subject them to direct comparison using new and innovative statistical theories, computer implementations, and experimental designs. This project aims to serve Socrate's goal of carving nature at its joints by finding out why different people, at different times, learn different kinds of categories in different ways. This project has many potential scientific and practical ramifications. In particular, the techniques to be developed are directly relevant to research in the psychology of memory, judgment, and decision-making. More broadly, knowing if different category types are reliably different in the way they are learned, or if people are reliably different in the way they learn categories, will allow development of programs to help people learn difficult categories (like cancerous vs benign radiographs) more quickly and accurately.