Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception
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{"title"=>"Model cortical association fields account for the time course and dependence on target complexity of human contour perception", "type"=>"journal", "authors"=>[{"first_name"=>"Vadas", "last_name"=>"Gintautas", "scopus_author_id"=>"23100051500"}, {"first_name"=>"Michael I.", "last_name"=>"Ham", "scopus_author_id"=>"16021897600"}, {"first_name"=>"Benjamin", "last_name"=>"Kunsberg", "scopus_author_id"=>"54401239500"}, {"first_name"=>"Shawn", "last_name"=>"Barr", "scopus_author_id"=>"54400739100"}, {"first_name"=>"Steven P.", "last_name"=>"Brumby", "scopus_author_id"=>"36162233900"}, {"first_name"=>"Craig", "last_name"=>"Rasmussen", "scopus_author_id"=>"8259968900"}, {"first_name"=>"John S.", "last_name"=>"George", "scopus_author_id"=>"7403557845"}, {"first_name"=>"Ilya", "last_name"=>"Nemenman", "scopus_author_id"=>"6602699412"}, {"first_name"=>"Luís M.A.", "last_name"=>"Bettencourt", "scopus_author_id"=>"7003549424"}, {"first_name"=>"Garret T.", "last_name"=>"Kenyon", "scopus_author_id"=>"7102602432"}], "year"=>2011, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"362834471", "sgr"=>"80055095016", "issn"=>"1553734X", "arxiv"=>"1102.0566", "pmid"=>"21998562", "scopus"=>"2-s2.0-80055095016", "doi"=>"10.1371/journal.pcbi.1002162", "isbn"=>"1553-7358"}, "id"=>"69e8e6c7-7e63-3bfa-b5ac-e231bf418378", "abstract"=>"A model of iterative lateral interactions that followed association field like statistics were compared to human performance in a contour integrration task.The fit of the model to the human data worked well, and it is suggested that one iteration takes about 40ms.", "link"=>"http://www.mendeley.com/research/model-cortical-association-fields-account-time-course-dependence-target-complexity-human-contour-per", "reader_count"=>36, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>6, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>6, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Materials Science"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>17, "Medicine and Dentistry"=>2, "Neuroscience"=>3, "Physics and Astronomy"=>2, "Psychology"=>3, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>5}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United States"=>4, "United Kingdom"=>2, "Mexico"=>1, "Portugal"=>1}, "group_count"=>6}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/727004"], "description"=>"<p>The stimulus consisted of one target image and one distractor image (randomly positioned with equal probability on the left or right), presented simultaneously for an SOA between ms and ms, followed by an optimized ms mask generated from randomly rotated groups of target and distractor segments. Subjects indicated which side contained the target object (amoeba) using a computer mouse to click along a horizontal slider bar. Clicking far to the left or right indicated strong confidence that the corresponding side contained the target; clicking close to the center indicated weak confidence. A narrow gap in the center forced subjects to choose between left and right.</p>", "links"=>[], "tags"=>["neuroscience"], "article_id"=>397356, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g005", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Psychophysical_experiment_schematic_/397356", "title"=>"Psychophysical experiment schematic.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:00:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/726925"], "description"=>"<p>Red bins: Total activity histograms for all test target images. Blue bins: Total activity histograms for all test distractor images. The degree that the two distributions overlap is shown as the gray shaded area, which provides a measure of whether total luminance can be used to distinguish targets from distractors. The percentage in each shaded area shows the approximate lower bound amount of overlap of the two histograms, for comparison. Top row: Total summed activity over all retinal pixels. Little, if any bias between target and distractor images was evident in the input black and white images as there is nearly complete overlap between the distributions. Subsequent rows: Total activity histograms summed over all orientation-selective elements. Second row: Bottom-up responses prior to any lateral interactions. Third - sixth rows: Total activity histograms after - iterations of the multiplicative ODD kernel, respectively. Total summed activity became progressively more separable with additional iterations, as evinced by a decrease in the overlapping areas.</p>", "links"=>[], "tags"=>["luminance", "distractor", "images"], "article_id"=>397269, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g004", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histograms_of_total_luminance_in_target_and_distractor_images_as_a_function_of_the_number_of_iterations_/397269", "title"=>"Histograms of total luminance in target and distractor images as a function of the number of iterations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:00:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/726720"], "description"=>"<p>Top Row: For a single short line segment oriented approximately horizontally at the center (not drawn), the co-occurrence-based support of other edges at different relative orientations and spatial locations is depicted. Axes were rotated by () from vertical to mitigate aliasing effects. The color of each edge was set proportional to its co-occurrence-based support. The color scale ranges from blue (negative values) to white (zero) to red (positive values). Left panel: Co-occurrence statistics compiled from target images. Center panel: Co-occurrence statistics compiled from distractor images. Right panel: ODD kernel, given by the difference in co-occurrence statistics between target and distractor kernels. Bottom Row: Subfields extracted from the middle of the upper left quadrant (as indicated by black boxes in the top row figures), shown on an expanded scale to better visualize the difference in co-occurrence statistics between target and distractor images. Alignment of edges in target images is mostly cocircular whereas alignment is mostly random in distractor images, accounting for the fine structure in the corresponding section of the ODD kernel.</p>", "links"=>[], "tags"=>["neuroscience"], "article_id"=>397072, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g002", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ODD_kernels_/397072", "title"=>"ODD kernels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 13:59:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/727077"], "description"=>"<p>Top two rows: ROC curves averaged over four different human test subjects using reported confidence (points). The dashed diagonal line in each plot indicates the curve corresponding to chance. Red, blue, green, black correspond to , respectively. Bottom two rows: ROC curves for model cortical association fields computed from total activity histograms.</p>", "links"=>[], "tags"=>["curves", "comparing", "amoeba"], "article_id"=>397422, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g006", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_comparing_human_and_model_performance_on_the_amoeba_no_amoeba_task_/397422", "title"=>"ROC curves comparing human and model performance on the amoeba/no amoeba task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:01:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/727236"], "description"=>"<p>Left: Average human performance for different SOA in milliseconds. Right: Performance of model cortical association fields for increasing numbers of iterations. Both panels: Accuracy, which is equivalent to area under the ROC curve, (error bars) fitted to single sigmoidal functions (solid lines). The four curves from top to bottom correspond to radial frequencies.</p>", "links"=>[], "tags"=>["2afc", "amoeba"], "article_id"=>397586, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g007", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_of_human_and_model_performance_on_the_2AFC_amoeba_no_amoeba_task_/397586", "title"=>"A comparison of human and model performance on the 2AFC amoeba/no amoeba task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:02:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/727297"], "description"=>"<p>Line width and marker size denote values on kernel strength, which was the main free parameter in the model. Kernel strength is a dimensionless constant. Black lines: ODD kernel performance. Blue lines: “Bowtie” kernel performance. Qualitative behavior was similar for both kernels, demonstrating that multiplicative lateral interactions act robustly to reinforce smooth closed contours.</p>", "links"=>[], "tags"=>["simpler", "kernel", "amoeba", "plotted", "iterations"], "article_id"=>397642, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g008", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_of_ODD_and_simpler_Bowtie_kernel_performance_on_the_on_the_2AFC_amoeba_no_amoeba_task_plotted_as_a_function_of_the_number_of_iterations_for_a_range_of_different_kernel_strengths_/397642", "title"=>"A comparison of ODD and simpler “Bowtie” kernel performance on the on the 2AFC, amoeba/no amoeba task plotted as a function of the number of iterations for a range of different kernel strengths.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:02:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/726828"], "description"=>"<p>Left column: black and white amoeba-target image (). Right column: Gray-scale natural image (the standard computer vision test image “Lena”) after applying a hard Difference of Gaussians (DoG) filter to enhance edges. Top row: Raw retinal input. Second row: Responses of orientation-selective elements before any lateral interactions (). To aid visualization, the activity of the maximally responding orientation-selective element at each pixel location is depicted as a gray-scale intensity. Rows 3-6: Activity after iterations of the multiplicative ODD kernel, as labeled. For each iteration, activity was multiplied by the local support, computed via linear convolution of the previous output activity with the ODD kernel. Lateral interactions tended to support smooth contours, particularly those arising from amoeba segments, while suppressing clutter or background detail.</p>", "links"=>[], "tags"=>["lateral", "interactions"], "article_id"=>397174, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_lateral_interactions_on_example_images_/397174", "title"=>"The effect of lateral interactions on example images.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/726604"], "description"=>"<p>From left to right: . Top row: Targets; amoeba complexity increases with increasing numbers of radial frequencies. Clutter was constructed by randomly rotating groups of amoeba contour fragments. Bottom row: Distractors; only clutter fragments are present.</p>", "links"=>[], "tags"=>["targets", "distractors"], "article_id"=>396946, "categories"=>["Neuroscience"], "users"=>["Vadas Gintautas", "Michael I. Ham", "Benjamin Kunsberg", "Shawn Barr", "Steven P. Brumby", "Craig Rasmussen", "John S. George", "Ilya Nemenman", "Luís M. A. Bettencourt", "Garret T. Kenyon"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002162.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_targets_and_distractors_from_the_amoeba_no_amoeba_image_set_for_different_/396946", "title"=>"Examples of targets and distractors from the amoeba/no-amoeba image set for different .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 13:58:47"}

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Relative Metric

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