Chapter

Approaching Color with Bayesian Algorithms

Sarah Allred

in Visual Experience

Published in print July 2012 | ISBN: 9780199597277
Published online September 2012 | e-ISBN: 9780191741883 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199597277.003.0012
Approaching Color with Bayesian Algorithms

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What is the goal of color vision? How ought we to think of color appearance? Under one view, the goal of vision is to maintain a stable representation of object properties across changes in the environment. This poses a challenge to the visual system, because the sensory signal on which visual perception is based is ambiguous with respect to the physical properties of objects in the world. Thus, to maintain stable color appearance, the visual system must estimate what object was most likely to have caused the ambiguous sensory signal. This chapter presents a Bayesian approach to solving this estimation problem that relies on statistical regularities in the world to resolve the sensory ambiguity. The chapter argues that this is a sensible idea: the human visual system evolved in this world, and thus its statistical regularities are likely to be of functional importance to vision.

Keywords: color appearance; Bayesian algorithms; statistical regularities; human visual system; ambiguity; visual perception

Chapter.  10630 words.  Illustrated.

Subjects: Cognitive Psychology

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