Theory-based Bayesian Models of Inductive Inference.

摘要

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.