Bayesian Analysis of Perceived Eye Level

To accurately perceive the world, people must efficiently combine internal beliefs and external sensory cues. We introduce a Bayesian framework that explains the role of internal balance cues and visual stimuli on perceived eye level (PEL)-a self-reported measure of elevation angle. This framework p...

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Bibliographic Details
Published inFrontiers in computational neuroscience Vol. 10; p. 135
Main Authors Orendorff, Elaine E, Kalesinskas, Laurynas, Palumbo, Robert T, Albert, Mark V
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Research Foundation 15.12.2016
Frontiers
Frontiers Media S.A
Subjects
Bayesian Analysis
Behavior
Brain research
Computer Science
Cue Combination
Elevation estimation
Experiments
Lasers
Life Sciences
Localization
Mathematical models
Neurons and Cognition
Neuroscience
Perceived Eye Level
Perceptions
Psychophysics
Visual psychophysics
Visual stimuli
New York
United States > US
Chicago Illinois
elevation estimation
Bayesian analysis
perceived eye level
visual psychophysics
cue combination
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Summary:To accurately perceive the world, people must efficiently combine internal beliefs and external sensory cues. We introduce a Bayesian framework that explains the role of internal balance cues and visual stimuli on perceived eye level (PEL)-a self-reported measure of elevation angle. This framework provides a single, coherent model explaining a set of experimentally observed PEL over a range of experimental conditions. Further, it provides a parsimonious explanation for the additive effect of low fidelity cues as well as the averaging effect of high fidelity cues, as also found in other Bayesian cue combination psychophysical studies. Our model accurately estimates the PEL and explains the form of previous equations used in describing PEL behavior. Most importantly, the proposed Bayesian framework for PEL is more powerful than previous behavioral modeling; it permits behavioral estimation in a wider range of cue combination and perceptual studies than models previously reported.
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Edited by: Si Wu, Beijing Normal University, China
Reviewed by: He Wang, Hong Kong University of Science and Technology, Hong Kong; Wenhao Zhang, Carnegie Mellon University, USA
ISSN:1662-5188
1662-5188
DOI:10.3389/fncom.2016.00135