Definition
Bayesian models of perception use optimal statistical methods to perform inference based on perceptual data. These models are used in computational neuroscience and cognitive psychology to explain how the nervous system processes perceptual information.
Detailed Description
Bayesian inference provides the optimal statistical inference about unknown properties given a stochastic process. Bayesian models of perception work under the hypothesis that the nervous system uses perceptual information as if it was able to optimally process the information according to a Bayesian inference model. The majority of models propose how the nervous system processes discrete cues (flash of light, auditory beep), but models have also been proposed for, e.g., learning in perception.
All Bayesian models start by specifying the generative process along with the associated probability distributions (e.g., prior and likelihood distributions). Using Bayes’ rule (examples below), the generative...
This is a preview of subscription content, log in via an institution to check access.
References
Ernst M (2006) A Bayesian view on multimodal cue integration. Oxford University Press, New York, pp 105–131
Feldman J (2001) Bayesian contour integration. Percept & Psychophys 63(7):1171–1182
Kersten D, Mamassian P, Yuille A (2004) Object perception as Bayesian inference. Annu Rev Psychol 55:271–304
Knill DC, Pouget A (2004) The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci 27:712–719
Knill DC, Richards W (eds) (1996) Perception as Bayesian inference. Cambridge University Press, Cambridge
Orbán G, Fiser J, Aslin RN, Lengyel M (2008) Bayesian learning of visual chunks by human observers. Proc Natl Acad Sci U S A 105(7):2745–2750
Shams L, Beierholm UR (2010) Causal inference in perception. Trends Cogn Sci 14(9):1–8
Trommershauser J, Kording K, Landy MS (eds) (2011) Sensory cue integration. Oxford University Press, Oxford/New York
Weiss Y, Simoncelli EP, Adelson EH (2002) Motion illusions as optimal percepts. Nat Neurosci 5:598–604
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Beierholm, U.R. (2014). Perception, Bayesian Models of. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_451-2
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7320-6_451-2
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Online ISBN: 978-1-4614-7320-6
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences
Publish with us
Chapter history
-
Latest
Perception, Bayesian Models of- Published:
- 17 September 2014
DOI: https://doi.org/10.1007/978-1-4614-7320-6_451-2
-
Original
Perception, Bayesian Models of- Published:
- 27 March 2014
DOI: https://doi.org/10.1007/978-1-4614-7320-6_451-1