Bayesian Analysis of Perceived Eye Level - Orendorff, Elaine E.; Kalesinskas, Laurynas; Palumbo, Robert T.; Albert, Mark V. (Frontiers in Computational Neuroscience, 2016)
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 psychophysics studies. Our model accurately estimates the PEL with standard errors of ±0.78 degrees for short line, low fidelity cues and ±4.81 degrees for long line, high fidelity cues 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.