Abstract
The notion that perception involves Bayesian inference is an increasingly popular position taken by many researchers. Bayesian models have provided insights into many perceptual phenomena, but their description and practical implementation does not always convey their theoretical appeal or conceptual elegance. This tutorial provides an introduction to core concepts in Bayesian modelling and should help a wide variety of readers to more deeply understand, or to generate their own Bayesian models of perception. Core theoretical and implementational issues are covered, using the 2 alternative-forced-choice task as a case study. Supplementary code is available to help bridge the gap between model description and practical implementation (see Appendix B).
Original language | English |
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Pages (from-to) | 103-114 |
Number of pages | 12 |
Journal | Journal of Mathematical Psychology |
Volume | 66 |
Early online date | 13 Mar 2015 |
DOIs | |
Publication status | Published - Jun 2015 |
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Keywords
- Alternative forced choice
- Bayesian inference
- Bayesian network
- Ideal observer
- MCMC
- Probabilistic generative model
- Psychometric function
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A tutorial on Bayesian models of perception. / Vincent, Benjamin T. (Lead / Corresponding author).
In: Journal of Mathematical Psychology, Vol. 66, 06.2015, p. 103-114.Research output: Contribution to journal › Article
TY - JOUR
T1 - A tutorial on Bayesian models of perception
AU - Vincent, Benjamin T.
PY - 2015/6
Y1 - 2015/6
N2 - The notion that perception involves Bayesian inference is an increasingly popular position taken by many researchers. Bayesian models have provided insights into many perceptual phenomena, but their description and practical implementation does not always convey their theoretical appeal or conceptual elegance. This tutorial provides an introduction to core concepts in Bayesian modelling and should help a wide variety of readers to more deeply understand, or to generate their own Bayesian models of perception. Core theoretical and implementational issues are covered, using the 2 alternative-forced-choice task as a case study. Supplementary code is available to help bridge the gap between model description and practical implementation (see Appendix B).
AB - The notion that perception involves Bayesian inference is an increasingly popular position taken by many researchers. Bayesian models have provided insights into many perceptual phenomena, but their description and practical implementation does not always convey their theoretical appeal or conceptual elegance. This tutorial provides an introduction to core concepts in Bayesian modelling and should help a wide variety of readers to more deeply understand, or to generate their own Bayesian models of perception. Core theoretical and implementational issues are covered, using the 2 alternative-forced-choice task as a case study. Supplementary code is available to help bridge the gap between model description and practical implementation (see Appendix B).
KW - Alternative forced choice
KW - Bayesian inference
KW - Bayesian network
KW - Ideal observer
KW - MCMC
KW - Probabilistic generative model
KW - Psychometric function
UR - http://www.scopus.com/inward/record.url?scp=84924674784&partnerID=8YFLogxK
U2 - 10.1016/j.jmp.2015.02.001
DO - 10.1016/j.jmp.2015.02.001
M3 - Article
VL - 66
SP - 103
EP - 114
JO - Journal of Mathematical Psychology
JF - Journal of Mathematical Psychology
SN - 0022-2496
ER -