Theory-based Bayesian Models of Inductive Inference

Abstract

Abstract : The proposed research had the goal of developing computational models of human inferences that bridge the gap between human and machine learning. This research focused on two components of cognition: learning about categories and their properties, and learning about causal and social relations. Research was completed successfully in both of these areas, resulting in a new unifying framework for models of category learning, new models for how people form and use high-level generalizations in causal learning, and new methods for predicting people's preferences and the relationships between them. The grant supported a total of 29 publications over three years, including one conference paper that won a best student paper prize, and provided support for five graduate students.