Definition
Bayesian approaches to color category learning formalize learning as a problem of Bayesian inference, requiring the learner to form generalizations that go beyond observed examples of members of a category. This formal framework can be used to make predictions about both individual judgments and how populations form color categories.
Color Category Learning
One of the challenges that children face as they acquire a language is discovering how words are used to refer to different colors. While human languages demonstrate variation in how they partition the space of colors, there are also clear regularities in the kinds of systems of color categories that are used [1, 2]. This raises two important questions: How might color categories be learned? And how might regularities in systems of color categories across languages be explained?
Learning color categories is an inductive...
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Griffiths, T.L., Zaslavsky, N. (2021). Bayesian Approaches to Color Category Learning. In: Shamey, R. (eds) Encyclopedia of Color Science and Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27851-8_60-9
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DOI: https://doi.org/10.1007/978-3-642-27851-8_60-9
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Latest
Bayesian Approaches to Color Category Learning- Published:
- 01 January 2021
DOI: https://doi.org/10.1007/978-3-642-27851-8_60-9
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Original
Bayesian Approaches to Color Category Learning- Published:
- 02 July 2015
DOI: https://doi.org/10.1007/978-3-642-27851-8_60-8