Learning the context of a category

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

Authors

Dan Navarro

Abstract

This paper outlines a hierarchical Bayesian model for human category learning that learns both the organization of objects into categories, and the context in which this knowledge should be applied. The model is fit to multiple data sets, and provides a parsimonious method for describing how humans learn context specific conceptual representations.