Bayesian perceptual inference in linear Gaussian models
Author(s)Battaglia, Peter W.
Computational Cognitive Science
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The aim of this paper is to provide perceptual scientists with a quantitative framework for modeling a variety of common perceptual behaviors, and to unify various perceptual inference tasks by exposing their common computational underpinnings. This paper derives a model Bayesian observer for perceptual contexts with linear Gaussian generative processes. I demonstrate the relationship between four fundamental perceptual situations by expressing their corresponding posterior distributions as consequences of the model's predictions under their respective assumptions.
cue integration, cue combination, explaining away, discounting
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