Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.

We measured perceived depth from the optic flow (a) when showing a stationary physical or virtual object to observers who moved their head at a normal or slower speed, and (b) when simulating the same optic flow on a computer and presenting it to stationary observers. Our results show that perceived...

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Main Authors: Corrado Caudek, Carlo Fantoni, Fulvio Domini
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2011-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3077406?pdf=render
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spelling doaj-1c0395ab834a4e3ebe286f35d3d921552020-11-25T02:39:29ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0164e1873110.1371/journal.pone.0018731Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.Corrado CaudekCarlo FantoniFulvio DominiWe measured perceived depth from the optic flow (a) when showing a stationary physical or virtual object to observers who moved their head at a normal or slower speed, and (b) when simulating the same optic flow on a computer and presenting it to stationary observers. Our results show that perceived surface slant is systematically distorted, for both the active and the passive viewing of physical or virtual surfaces. These distortions are modulated by head translation speed, with perceived slant increasing directly with the local velocity gradient of the optic flow. This empirical result allows us to determine the relative merits of two alternative approaches aimed at explaining perceived surface slant in active vision: an "inverse optics" model that takes head motion information into account, and a probabilistic model that ignores extra-retinal signals. We compare these two approaches within the framework of the bayesian theory. The "inverse optics" bayesian model produces veridical slant estimates if the optic flow and the head translation velocity are measured with no error; because of the influence of a "prior" for flatness, the slant estimates become systematically biased as the measurement errors increase. The bayesian model, which ignores the observer's motion, always produces distorted estimates of surface slant. Interestingly, the predictions of this second model, not those of the first one, are consistent with our empirical findings. The present results suggest that (a) in active vision perceived surface slant may be the product of probabilistic processes which do not guarantee the correct solution, and (b) extra-retinal signals may be mainly used for a better measurement of retinal information.http://europepmc.org/articles/PMC3077406?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Corrado Caudek
Carlo Fantoni
Fulvio Domini
spellingShingle Corrado Caudek
Carlo Fantoni
Fulvio Domini
Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.
PLoS ONE
author_facet Corrado Caudek
Carlo Fantoni
Fulvio Domini
author_sort Corrado Caudek
title Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.
title_short Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.
title_full Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.
title_fullStr Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.
title_full_unstemmed Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.
title_sort bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2011-01-01
description We measured perceived depth from the optic flow (a) when showing a stationary physical or virtual object to observers who moved their head at a normal or slower speed, and (b) when simulating the same optic flow on a computer and presenting it to stationary observers. Our results show that perceived surface slant is systematically distorted, for both the active and the passive viewing of physical or virtual surfaces. These distortions are modulated by head translation speed, with perceived slant increasing directly with the local velocity gradient of the optic flow. This empirical result allows us to determine the relative merits of two alternative approaches aimed at explaining perceived surface slant in active vision: an "inverse optics" model that takes head motion information into account, and a probabilistic model that ignores extra-retinal signals. We compare these two approaches within the framework of the bayesian theory. The "inverse optics" bayesian model produces veridical slant estimates if the optic flow and the head translation velocity are measured with no error; because of the influence of a "prior" for flatness, the slant estimates become systematically biased as the measurement errors increase. The bayesian model, which ignores the observer's motion, always produces distorted estimates of surface slant. Interestingly, the predictions of this second model, not those of the first one, are consistent with our empirical findings. The present results suggest that (a) in active vision perceived surface slant may be the product of probabilistic processes which do not guarantee the correct solution, and (b) extra-retinal signals may be mainly used for a better measurement of retinal information.
url http://europepmc.org/articles/PMC3077406?pdf=render
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