Perception as Bayesian Inference
Abstract
Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. This 1996 book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modelling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a dialogue between different perspectives both within chapters, which draw on insights from experimental and computational work, and between chapters, through commentaries written by the contributors on each others' work. Students and researchers in cognitive and visual science will find much to interest them in this thought-provoking collection.
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Bayesian decision theory provides a general and often quantitatively accurate account of perceptual decisions in a wide variety of tasks.
“Bayesian decision theory provides a general and often quantitatively accurate account of perceptual decisions in a wide variety of tasks [16–18].”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [16]
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“Bayesian models have also been used to explain classic perceptual illusions, such as Adelson’s checker shadow illusion or motion illusions under ambiguous input (Knill and Richards 1996).”
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 1996-09-13
- OpenAlex
- W1562206072
- Cited by
- 1,425
- References
- 0
- Citations by year
- 2026: 23 · 2025: 57 · 2024: 45 · 2023: 50 · 2022: 59 · 2021: 66 · 2020: 81 · 2019: 94 · 2018: 65 · 2017: 64 · 2016: 49 · 2015: 53 · 2014: 56 · 2013: 49 · 2012: 48
- FWCI
- 6.39
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- Bayesian inference, visual perception, computational models of vision, vision science, cognitive science
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