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2010 NIPS NeurIPS 2010

Learning the context of a category

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary
🧭 Keyword Pioneer — context-specific representations
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning
🐣 Hot Topic Early Bird — probabilistic modeling

Authors

Dan Navarro

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Interdisciplinary > Cognitive Science > Cognitive Modeling Machine Learning > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Learning Types > Representation Learning

Keywords

human learning probabilistic modeling category learning context modeling context-specific representations context specific probabilistic model hierarchical bayesian model conceptual representation
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