Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations
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{"title"=>"Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations", "type"=>"journal", "authors"=>[{"first_name"=>"Lucie", "last_name"=>"Sawides", "scopus_author_id"=>"25227029600"}, {"first_name"=>"Carlos", "last_name"=>"Dorronsoro", "scopus_author_id"=>"6602481957"}, {"first_name"=>"Andrew M.", "last_name"=>"Haun", "scopus_author_id"=>"8515026800"}, {"first_name"=>"Eli", "last_name"=>"Peli", "scopus_author_id"=>"7005237694"}, {"first_name"=>"Susana", "last_name"=>"Marcos", "scopus_author_id"=>"7006647818"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"369566999", "scopus"=>"2-s2.0-84881529686", "pmid"=>"23967123", "sgr"=>"84881529686", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0070856"}, "id"=>"5aabe8d8-0836-3a7c-aa85-491cde95cdc0", "abstract"=>"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.\\n\\nMETHODS 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).\\n\\nCONCLUSIONS: 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.", "link"=>"http://www.mendeley.com/research/using-pattern-classification-measure-adaptation-orientation-high-order-aberrations", "reader_count"=>10, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>4, "Student > Master"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>4, "Student > Master"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>1, "Physics and Astronomy"=>3, "Psychology"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}}, "reader_count_by_country"=>{"Mexico"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1169807"], "description"=>"<p>Normalized Point Spread Function (PSF) of the 5 subjects.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery"], "article_id"=>772568, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Normalized_Point_Spread_Function_PSF_of_the_5_subjects_/772568", "title"=>"Normalized Point Spread Function (PSF) of the 5 subjects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169809"], "description"=>"<p>Presentation of two pairs of blurred images out of a total of 500 pairs followed with a response on the 6 buttons box.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "psychophysical"], "article_id"=>772570, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_psychophysical_experimental_sequence_/772570", "title"=>"Illustration of the psychophysical experimental sequence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169810"], "description"=>"<p>(a) Subject's PSF; (b) Sampled PSF Map in 72 angular sectors. The integrated intensity values are normalized to 1; (c) Corresponding polar plot of the Sampled PSF Map (Orientation Plot); (d) The orientation of the PSF is given by the axis of the fitting ellipse (where the angle represents the main axis of the ellipse and the line length the eccentricity e of the ellipse (e = 0.98). Data are for S4).</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "psf"], "article_id"=>772571, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_PSF_orientation_analysis_/772571", "title"=>"Illustration of the PSF orientation analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169814"], "description"=>"<p>(1) Construction of the Positive and Negative Classification Maps from the total Classification Map, (2) considering absolute values, (3) Polar plot representation of Positive and Negative Orientation Classification Plots, (4) main axis of the fitting ellipse and eccentricities (e = 0.88 for positive and 0.98 for negative). Example is shown for subject S4.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "classification", "maps"], "article_id"=>772575, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_Classification_Maps_orientation_analysis_/772575", "title"=>"Illustration of the Classification Maps orientation analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169817"], "description"=>"<p>The labels show the score and the parameters of the fitted ellipse (axis, eccentricity).</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "10", "positively", "negatively", "ranked", "psfs"], "article_id"=>772578, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_the_10_top_positively_and_negatively_ranked_PSFs_for_subject_S4_/772578", "title"=>"Example of the 10 top positively and negatively ranked PSFs for subject S4.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169819"], "description"=>"<p>Subject's natural PSF (first row), averaged PSFs of the 10 best positive (middle row) and of the 10 best negative (last row) for each subject. The corresponding coefficients of correlation (r) between Subject's natural PSF and the Averaged Positive and Negative PSFs are shown in each panel.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "psf", "averaged"], "article_id"=>772580, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Subject_s_natural_PSF_and_Positive_and_Negative_averaged_PSFs_/772580", "title"=>"Subject's natural PSF and Positive and Negative averaged PSFs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169824"], "description"=>"<p>Upper row: Correlations of the averaged Positive or Negative PSFs with the subject's PSF a) for the highest ranked only and b) for all PSFs. Lower row: Correlations of the individual Positive or Negative PSFs with the subject's PSF c) for the highest ranked PSFs only and d) for all PSFs. The red crosses show the average of all the individual correlations of the 100 PSFs with the subject's natural PSF of the eye under test. Significant differences between Positive and Negative PSFs were found in all cases; * stands for significance at p<0.05; ** p<0.005 (t-test). Dashed lines and symbols correspond to the simulated ideal responses, based on correlations with the subject's PSF. Positive responses in blue, and negative responses in yellow.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery"], "article_id"=>772585, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_with_subjects_PSF_/772585", "title"=>"Correlations with subjects' PSF.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169827"], "description"=>"<p>(1) Subjects' natural PSF; (2) Subject's Sampled PSF in angular sectors; (3) Corresponding PSF Orientation Plot (along with the axis of the fitted ellipses and eccentricities) and (4) the Classification Maps obtained from the subject's responses and all the 100 PSFs. Correlations between the Classification Map and the subject's Sampled PSF are shown in insets.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "psf", "classification"], "article_id"=>772588, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Subject_s_PSF_and_Classification_Maps_/772588", "title"=>"Subject's PSF and Classification Maps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169829"], "description"=>"<p>Positive (green) and Negative (red) Classification Orientation Plots, along with subject's natural PSF Orientation Plot (blue) for all subjects and the representation of the orientation of the fitting ellipses for the subject's PSF (blue), the positive internally coded PSF (green) and the negative internally coded PSF (dashed-red). The angle (φ) for each fitted ellipse is depicted in the corresponding graph.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery"], "article_id"=>772590, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_Orientation_Plots_from_subjects_responses_/772590", "title"=>"Classification Orientation Plots from subjects' responses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169831"], "description"=>"<p>Ideal Positive (green, upper row) and Negative (red, mid row) Classification Orientation Plots computed from the simulated responses for each subject. The axis of the fitting ellipses and their corresponding angles φ are also shown (lower row, green for positive and dashed-red for negative). The subject's natural PSF Orientation Plot (blue) and axis are shown for reference.</p>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "simulated"], "article_id"=>772592, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856.g010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_Orientation_Plots_from_simulated_responses_/772592", "title"=>"Classification Orientation Plots from simulated responses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-14 03:10:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1169833"], "description"=>"<div><p>Background</p><p>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.</p><p>Methods and Findings</p><p>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).</p><p>Conclusions</p><p>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.</p></div>", "links"=>[], "tags"=>["neuroscience", "computational neuroscience", "Sensory systems", "Sensory perception", "Psychophysics", "Visual system", "Anatomy and physiology", "Neurological system", "Sensory physiology", "Mental health", "psychology", "neurology", "Neuro-ophthalmology", "ophthalmology", "surgery", "classification", "adaptation"], "article_id"=>772594, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Lucie Sawides", "Carlos Dorronsoro", "Andrew M. Haun", "Eli Peli", "Susana Marcos"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0070856"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Using_Pattern_Classification_to_Measure_Adaptation_to_the_Orientation_of_High_Order_Aberrations_/772594", "title"=>"Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-14 03:10:46"}

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Relative Metric

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