Learning the context of a category
Part of: Advances in Neural Information Processing Systems 23 (NIPS 2010)
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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.