Bayesian perceptual inference in linear Gaussian models

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.