How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?
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{"title"=>"How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?", "type"=>"journal", "authors"=>[{"first_name"=>"Holly E.", "last_name"=>"Gerhard", "scopus_author_id"=>"36466389500"}, {"first_name"=>"Felix A.", "last_name"=>"Wichmann", "scopus_author_id"=>"6603676955"}, {"first_name"=>"Matthias", "last_name"=>"Bethge", "scopus_author_id"=>"6603439763"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"368294211", "sgr"=>"84873514336", "issn"=>"1553734X", "pmid"=>"23358106", "scopus"=>"2-s2.0-84873514336", "doi"=>"10.1371/journal.pcbi.1002873", "isbn"=>"1553-7358"}, "id"=>"be8ab844-696b-3854-8dc7-df61bdcb9c91", "abstract"=>"A key hypothesis in sensory system neuroscience is that sensory representations are adapted to the statistical regularities in sensory signals and thereby incorporate knowledge about the outside world. Supporting this hypothesis, several probabilistic models of local natural image regularities have been proposed that reproduce neural response properties. Although many such physiological links have been made, these models have not been linked directly to visual sensitivity. Previous psychophysical studies of sensitivity to natural image regularities focus on global perception of large images, but much less is known about sensitivity to local natural image regularities. We present a new paradigm for controlled psychophysical studies of local natural image regularities and compare how well such models capture perceptually relevant image content. To produce stimuli with precise statistics, we start with a set of patches cut from natural images and alter their content to generate a matched set whose joint statistics are equally likely under a probabilistic natural image model. The task is forced choice to discriminate natural patches from model patches. The results show that human observers can learn to discriminate the higher-order regularities in natural images from those of model samples after very few exposures and that no current model is perfect for patches as small as 5 by 5 pixels or larger. Discrimination performance was accurately predicted by model likelihood, an information theoretic measure of model efficacy, indicating that the visual system possesses a surprisingly detailed knowledge of natural image higher-order correlations, much more so than current image models. We also perform three cue identification experiments to interpret how model features correspond to perceptually relevant image features.", "link"=>"http://www.mendeley.com/research/sensitive-human-visual-system-local-statistics-natural-images", "reader_count"=>106, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>7, "Researcher"=>26, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>4, "Student > Master"=>6, "Other"=>5, "Student > Bachelor"=>10, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>7, "Researcher"=>26, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>4, "Student > Master"=>6, "Other"=>5, "Student > Bachelor"=>10, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>8, "Agricultural and Biological Sciences"=>32, "Philosophy"=>1, "Computer Science"=>16, "Decision Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Engineering"=>8, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Neuroscience"=>5, "Physics and Astronomy"=>1, "Psychology"=>26, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>26}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>8}, "Engineering"=>{"Engineering"=>8}, "Neuroscience"=>{"Neuroscience"=>5}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>32}, "Computer Science"=>{"Computer Science"=>16}, "Linguistics"=>{"Linguistics"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Canada"=>3, "Austria"=>1, "Netherlands"=>1, "Hungary"=>1, "United States"=>8, "United Kingdom"=>1, "Belarus"=>1, "France"=>1, "Chile"=>1, "Portugal"=>1, "Switzerland"=>2, "Germany"=>6}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/501222"], "description"=>"<p>The natural image models we tested along with the neural response properties they mimic: “BF” is bandpass filtering, “OS” is orientation selectivity, “DN” is divisive normalization, and “CP” is complex cell pooling. We also show cited likelihood estimates. MEC is the mixture of elliptically contoured distributions model <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002873#pcbi.1002873-Bethge1\" target=\"_blank\">[33]</a>. All models are described in detail in the “Models Tested” section. Higher likelihood indicates that a model captures more of the regularities present in natural images than a model with lower likelihood.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>171746, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Natural_image_model_features_and_likelihood_estimates_/171746", "title"=>"Natural image model features and likelihood estimates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 16:29:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/500195"], "description"=>"<p><b>A.</b> A white noise image free of spatial correlations between pixel gray values. <b>B.</b> A natural image. In the present work, we study sensitivity to local regularities in natural images.</p>", "links"=>[], "tags"=>["kinds"], "article_id"=>170715, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Different_kinds_of_images_/170715", "title"=>"Different kinds of images.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:24:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/501193"], "description"=>"<p>Average percent corrects are listed for each model at each patch size tested. subjects for the first six models, and subjects for the MEC models. In starred conditions the null hypothesis that performance was at chance (50%) can be rejected at the level (*), the level (), or the level (). pixel patches were tested only for the MEC models.</p>", "links"=>[], "tags"=>["discriminability"], "article_id"=>171716, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.t002", "stats"=>{"downloads"=>10, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_1_average_discriminability_for_all_models_/171716", "title"=>"Experiment 1 average discriminability for all models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 16:29:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/500874"], "description"=>"<p>The contrast fluctuations of each model sample set have been artificially matched to the contrast fluctuations across the natural samples by matching the distribution of grayscale pixel norms to that of the natural samples. Each texture is the fluctuation matched version of the corresponding stimulus in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002873#pcbi-1002873-g004\" target=\"_blank\">Figure 4</a>.</p>", "links"=>[], "tags"=>["fluctuation", "matched"], "article_id"=>171397, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g009", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_3_contrast_fluctuation_matched_model_samples_/171397", "title"=>"Experiment 3 contrast fluctuation matched model samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:28:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/500940"], "description"=>"<p>Discriminability estimates are plotted with 95% binomial confidence intervals. Nine subjects participated, and each performed 36 test trials per model per condition per patch size, so each data point in <b>A</b> and <b>B</b> is based on trials. MEC, IPS, and GPS were not included in the experiment because they perfectly captured the contrast fluctuation cue in Experiment 2. <b>A.. </b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002873#s2\" target=\"_blank\">Results</a> from the unperturbed stimulus condition. <b>B.. </b><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002873#s2\" target=\"_blank\">Results</a> from the contrast fluctuation matched stimulus condition. <b>C.</b> Discriminability estimates pooled over patch sizes and plotted in order of increasing model likelihood. The unfilled bars are for the unperturbed stimulus data in <b>A</b>, the filled bars for the data in <b>B</b>. As expected the model ordering for the data in <b>A</b> are the same as in Experiment 1, but the model ordering changed for the contrast fluctuation matched data, showing that brought performance closest to chance out of all models whereas ICA was near ceiling with the unperturbed stimuli.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>171466, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g010", "stats"=>{"downloads"=>0, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_3_results_/171466", "title"=>"Experiment 3 results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:28:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/500749"], "description"=>"<p><b>A.</b> Discriminability estimates with 95% binomial confidence intervals are shown by model as a function of patch size. Three subjects participated, and each performed 30 test trials per model per patch size per condition, so each data point is based on trials. We did not measure discriminability for MEC with pixel patches as observers were at chance with them in Experiment 1. The solid line shows these observers' data in Experiment 1, i.e. with unperturbed stimuli, the dotted line shows performance for global scrambles, and the dashed line for sample scrambles. <b>B.</b> Discriminability estimates averaged over patch size for each model are plotted in order of increasing likelihood. The colored bars are the data from Experiment 1, the translucent bars with dashed edges are for the global scrambles, and the bars with solid edges are for the sample scrambles. In all three conditions, the ordering is the same: higher likelihood is linked with lower discriminability.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>171275, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g008", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_2_results_/171275", "title"=>"Experiment 2 results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:27:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/500562"], "description"=>"<p>Discriminability estimates with 95% binomial confidence intervals are shown by model as a function of patch size, where data are pooled over subjects. Sixteen subjects participated in session one with RND, ICA, L2, LP, IPS, and GPS, and 12 participated in session two with the MEC models. Each subject performed 30 test trials per data point in the plot. Therefore, each data point for session one is based on trials, and each for session two is based on trials.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>171087, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g005", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_1_results_/171087", "title"=>"Experiment 1 results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:26:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/501016"], "description"=>"<p>To focus on regions of natural images containing shape information, we automatically selected high contrast natural image patches for use as stimuli. <b>A.</b> Grayscale stimuli for the 8 models we tested: RND, ICA, L2, LP, MEC2, MEC4, MEC8, MEC16. <b>B.</b> The binary version of <b>A</b> where the number of on and off pixels are held equal. On any only trial, the observer viewed only one set of natural image samples and one set of samples from a single model.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>171536, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g011", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_4_high_contrast_stimuli_/171536", "title"=>"Experiment 4 high contrast stimuli.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:28:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/500275"], "description"=>"<p>The left texture contains model samples, and the right texture contains only true natural image samples. Each texture is a square tiling of 64 samples, where each sample is pixels in size. The observer's task is to indicate the texture made only of natural image samples. Feedback was given, and a short training sequence was performed before every experiment.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>170801, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_stimulus_/170801", "title"=>"Example stimulus.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:24:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/500382"], "description"=>"<p><b>A.</b> A set of 64 pixel natural image patches, . <b>B.</b> The coefficients of the first two (non-DC) ICA components are plotted against each other for all 64 patches along with their marginal distributions. <b>C.</b> Histogram of the 64 patches' norms in the ICA basis. <b>D.</b> To apply the ICA independence assumption to , we shuffle the ICA coefficients across samples separately for each component. Shown are the resulting matched model patches, . <b>E.</b> The coefficients of the first two (non-DC) ICA components of . The marginal distributions are the same as those of shown in <b>B</b>. <b>F.</b> Histogram of the coefficient norms of the 64 patches in . Applying the ICA assumption has changed the radial distribution so that the variance is much lower than that of the original distribution shown in <b>C</b>.</p>", "links"=>[], "tags"=>["samples"], "article_id"=>170902, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g003", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generating_model_samples_using_ICA_/170902", "title"=>"Generating model samples using ICA.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:25:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/500673"], "description"=>"<p>Here we show example textures for each model tested in Experiment 2: RND, ICA, L2, LP, IPS, GPS, and MEC16. Both <b>A</b> and <b>B</b> are scrambled versions of the corresponding model stimuli shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002873#pcbi-1002873-g004\" target=\"_blank\">Figure 4</a>. On any single trial the observer viewed only one texture based on natural image samples and one texture based on samples from a single model. <b>A.</b> Global scrambles, where the pixels of each texture were scrambled as a final post-processing step. <b>B.</b> Sample scrambles, where the pixels of each image patch were scrambled individually to preserve variations in luminance histograms across samples.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>171194, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_2_texture_scrambles_/171194", "title"=>"Experiment 2 texture scrambles.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:26:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/501150"], "description"=>"<p>Discriminability estimates with 95% binomial confidence intervals are shown by model in order of increasing likelihood where data are pooled over subjects and patch sizes (, , , and ). Seven subjects participated, and each performed 36 test trials per model per patch size per condition, so each data point is based on trials. Unfilled bars are for the grayscale high contrast stimuli, and filled bars the binary version. Within the range of the error bars, the estimates for the grayscale stimuli followed the same ordering as in Experiment 1, yet the data for the binary stimuli show no ordering.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>171675, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g012", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_4_results_/171675", "title"=>"Experiment 4 results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:29:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/500606"], "description"=>"<p><b>A.</b> Discriminability estimates with 95% binomial confidence intervals plotted in order of increasing model likelihood. Data is pooled over subjects and patch sizes , , and pixels. Each data point for RND, ICA, L2, and LP contains 1,440 trials, and 1,080 trials for the MEC models. MEC models are identified by the number of mixtures. Chance performance was 50%. <b>B.</b> Discriminability estimates with 95% binomial confidence intervals for one subject who performed 5 sessions of a four alternative choice version of the experiment. Each data point for RND, ICA, L2, and LP contains 576 trials, and 144 for the MEC models. Chance was 25%. <b>C.</b> Discriminability ranks of the models from most difficult to easiest are plotted against likelihood ranks from lowest likelihood to highest. Diamonds show group average data from <b>A</b>, and circles show the individual subject's data from <b>B</b>. The group data contain more trials and show a clear decrease in discriminability with increased likelihood. The same order is shown in the individual subject data within the range of the 95% confidence intervals, which overlap for L2, LP, and MEC .</p>", "links"=>[], "tags"=>["discriminability"], "article_id"=>171132, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_discriminability_and_likelihood_/171132", "title"=>"Model discriminability and likelihood.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:26:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/500498"], "description"=>"<p>In Experiment 1, we tested six models in one session (RND, ICA, L2, LP, IPS, GPS) and the four mixture models in a separate session (MEC2, MEC4, MEC8, MEC16). Shown are example textures for each model. The 64 samples comprising each model texture are matched to the 64 natural image samples shown on the left. Patch size here is pixels. On any single trial, observers viewed only one set of natural image samples and one set of model samples (e.g. as shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002873#pcbi-1002873-g002\" target=\"_blank\">Figure 2</a>).</p>", "links"=>[], "tags"=>["examples"], "article_id"=>171018, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Holly E. Gerhard", "Felix A. Wichmann", "Matthias Bethge"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002873.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Image_patch_examples_from_Experiment_1_/171018", "title"=>"Image patch examples from Experiment 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 16:25:57"}

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Relative Metric

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