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Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations

BACKGROUND: The image formed by the eye's optics is blurred by the ocular aberrations, specific to each eye. Recent studies demonstrated that the eye is adapted to the level of blur produced by the high order aberrations (HOA). We examined whether visual coding is also adapted to the orientatio...

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Autores principales: Sawides, Lucie, Dorronsoro, Carlos, Haun, Andrew M., Peli, Eli, Marcos, Susana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3743894/
https://www.ncbi.nlm.nih.gov/pubmed/23967123
http://dx.doi.org/10.1371/journal.pone.0070856
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author Sawides, Lucie
Dorronsoro, Carlos
Haun, Andrew M.
Peli, Eli
Marcos, Susana
author_facet Sawides, Lucie
Dorronsoro, Carlos
Haun, Andrew M.
Peli, Eli
Marcos, Susana
author_sort Sawides, Lucie
collection PubMed
description BACKGROUND: The image formed by the eye's optics is blurred by the ocular aberrations, specific to each eye. Recent studies demonstrated that the eye is adapted to the level of blur produced by the high order aberrations (HOA). We examined whether visual coding is also adapted to the orientation of the natural HOA of the eye. METHODS AND FINDINGS: Judgments of perceived blur were measured in 5 subjects in a psychophysical procedure inspired by the “Classification Images” technique. Subjects were presented 500 pairs of images, artificially blurred with HOA from 100 real eyes (i.e. different orientations), with total blur level adjusted to match the subject's natural blur. Subjects selected the image that appeared best focused in each random pair, in a 6-choice ranked response. Images were presented through Adaptive Optics correction of the subject's aberrations. The images selected as best focused were identified as positive, the other as negative responses. The highest classified positive responses correlated more with the subject's Point Spread Function, PSF, (r = 0.47 on average) than the negative (r = 0.34) and the difference was significant for all subjects (p<0.02). Using the orientation of the best fitting ellipse of angularly averaged integrated PSF intensities (weighted by the subject's responses) we found that in 4 subjects the positive PSF response was close to the subject's natural PSF orientation (within 21 degrees on average) whereas the negative PSF response was almost perpendicularly oriented to the natural PSF (at 76 degrees on average). CONCLUSIONS: The Classification-Images inspired method is very powerful in identifying the internally coded blur of subjects. The consistent bias of the Positive PSFs towards the natural PSF in most subjects indicates that the internal code of blur appears rather specific to each subject's high order aberrations and reveals that the calibration mechanisms for normalizing blur also operate using orientation cues.
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spelling pubmed-37438942013-08-21 Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations Sawides, Lucie Dorronsoro, Carlos Haun, Andrew M. Peli, Eli Marcos, Susana PLoS One Research Article BACKGROUND: The image formed by the eye's optics is blurred by the ocular aberrations, specific to each eye. Recent studies demonstrated that the eye is adapted to the level of blur produced by the high order aberrations (HOA). We examined whether visual coding is also adapted to the orientation of the natural HOA of the eye. METHODS AND FINDINGS: Judgments of perceived blur were measured in 5 subjects in a psychophysical procedure inspired by the “Classification Images” technique. Subjects were presented 500 pairs of images, artificially blurred with HOA from 100 real eyes (i.e. different orientations), with total blur level adjusted to match the subject's natural blur. Subjects selected the image that appeared best focused in each random pair, in a 6-choice ranked response. Images were presented through Adaptive Optics correction of the subject's aberrations. The images selected as best focused were identified as positive, the other as negative responses. The highest classified positive responses correlated more with the subject's Point Spread Function, PSF, (r = 0.47 on average) than the negative (r = 0.34) and the difference was significant for all subjects (p<0.02). Using the orientation of the best fitting ellipse of angularly averaged integrated PSF intensities (weighted by the subject's responses) we found that in 4 subjects the positive PSF response was close to the subject's natural PSF orientation (within 21 degrees on average) whereas the negative PSF response was almost perpendicularly oriented to the natural PSF (at 76 degrees on average). CONCLUSIONS: The Classification-Images inspired method is very powerful in identifying the internally coded blur of subjects. The consistent bias of the Positive PSFs towards the natural PSF in most subjects indicates that the internal code of blur appears rather specific to each subject's high order aberrations and reveals that the calibration mechanisms for normalizing blur also operate using orientation cues. Public Library of Science 2013-08-14 /pmc/articles/PMC3743894/ /pubmed/23967123 http://dx.doi.org/10.1371/journal.pone.0070856 Text en © 2013 Sawides et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Sawides, Lucie
Dorronsoro, Carlos
Haun, Andrew M.
Peli, Eli
Marcos, Susana
Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations
title Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations
title_full Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations
title_fullStr Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations
title_full_unstemmed Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations
title_short Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations
title_sort using pattern classification to measure adaptation to the orientation of high order aberrations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3743894/
https://www.ncbi.nlm.nih.gov/pubmed/23967123
http://dx.doi.org/10.1371/journal.pone.0070856
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