Bayesian perceptual inference in linear Gaussian models
Name
MIT-CSAIL-TR-2010-046.pdf
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227.52 KB
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Adobe PDF
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f8bc4002815140ff62c4841dce0ea2bf
Author(s)
Battaglia, Peter W.
Advisor(s)
Joshua Tenenbaum
Date Issued
September 21, 2010
Series/Report no.
MIT-CSAIL-TR-2010-046
Abstract
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.
Subjects
cue integration
cue combination
explaining away
discounting
Terms of Use
Creative Commons Attribution-ShareAlike 3.0 Unported
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