Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization
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{"title"=>"Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization", "type"=>"journal", "authors"=>[{"first_name"=>"Imri", "last_name"=>"Sofer", "scopus_author_id"=>"55371820000"}, {"first_name"=>"Sébastien M.", "last_name"=>"Crouzet", "scopus_author_id"=>"56915599800"}, {"first_name"=>"Thomas", "last_name"=>"Serre", "scopus_author_id"=>"6601960306"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"606367752", "sgr"=>"84943560758", "issn"=>"15537358", "pmid"=>"26335683", "scopus"=>"2-s2.0-84943560758", "doi"=>"10.1371/journal.pcbi.1004456", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"54211024-008f-3781-9bcd-d0098a18d310", "abstract"=>"Observers can rapidly perform a variety of visual tasks such as categorizing a scene as open, as outdoor, or as a beach. Although we know that different tasks are typically associated with systematic differences in behavioral responses, to date, little is known about the underlying mechanisms. Here, we implemented a single integrated paradigm that links perceptual processes with categorization processes. Using a large image database of natural scenes, we trained machine-learning classifiers to derive quantitative measures of task-specific perceptual discriminability based on the distance between individual images and different categorization boundaries. We showed that the resulting discriminability measure accurately predicts variations in behavioral responses across categorization tasks and stimulus sets. We further used the model to design an experiment, which challenged previous interpretations of the so-called \"superordinate advantage.\" Overall, our study suggests that observed differences in behavioral responses across rapid categorization tasks reflect natural variations in perceptual discriminability.", "link"=>"http://www.mendeley.com/research/explaining-timing-natural-scene-understanding-computational-model-perceptual-categorization", "reader_count"=>43, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>14, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>14, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>3, "Neuroscience"=>6, "Philosophy"=>1, "Psychology"=>22, "Computer Science"=>7}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Neuroscience"=>{"Neuroscience"=>6}, "Psychology"=>{"Psychology"=>22}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>7}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "United States"=>2, "United Kingdom"=>1, "France"=>1, "Switzerland"=>1, "Germany"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2252130", "https://ndownloader.figshare.com/files/2252131", "https://ndownloader.figshare.com/files/2252132", "https://ndownloader.figshare.com/files/2252133"], "description"=>"<div><p>Observers can rapidly perform a variety of visual tasks such as categorizing a scene as open, as outdoor, or as a beach. Although we know that different tasks are typically associated with systematic differences in behavioral responses, to date, little is known about the underlying mechanisms. Here, we implemented a single integrated paradigm that links perceptual processes with categorization processes. Using a large image database of natural scenes, we trained machine-learning classifiers to derive quantitative measures of task-specific perceptual discriminability based on the distance between individual images and different categorization boundaries. We showed that the resulting discriminability measure accurately predicts variations in behavioral responses across categorization tasks and stimulus sets. We further used the model to design an experiment, which challenged previous interpretations of the so-called “superordinate advantage.” Overall, our study suggests that observed differences in behavioral responses across rapid categorization tasks reflect natural variations in perceptual discriminability.</p></div>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534764, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004456.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004456.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004456.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004456.s004"], "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Explaining_the_Timing_of_Natural_Scene_Understanding_with_a_Computational_Model_of_Perceptual_Categorization_/1534764", "title"=>"Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-03 14:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2252125"], "description"=>"<p>(A) Attribute-level categories are labeled in blue and basic-level categories in red. (B) Basic-level categories are labeled in blue and superordinate-level categories in red.</p>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534759, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004456.g005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Re_drawing_of_Fig_4_with_labels_indicating_the_taxonomic_level_of_individual_categorization_tasks_/1534759", "title"=>"Re-drawing of Fig 4 with labels indicating the taxonomic level of individual categorization tasks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-03 14:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2252124"], "description"=>"<p>(A) Negative correlation between the model predicted task discriminability and participants’ presentation time threshold [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004456#pcbi.1004456.ref004\" target=\"_blank\">4</a>]. (B) Positive correlation between the model predicted task discriminability and participants sensitivity [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004456#pcbi.1004456.ref011\" target=\"_blank\">11</a>] (a small jitter was added to the display in (B) to improve visualization).</p>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534758, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004456.g004", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_the_model_predicted_task_discriminability_against_human_accuracy_on_multiple_scene_categorization_tasks_based_on_two_representative_studies_/1534758", "title"=>"Comparison between the model predicted task discriminability against human accuracy on multiple scene categorization tasks based on two representative studies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-03 14:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2252121"], "description"=>"<p>(A) For an individual image and a specific categorization task (e.g., task 1), discriminability values are derived from the model by considering the distance <i>d</i><sub>1</sub> between the image and the categorization boundary associated with task 1. Here we tested the hypothesis that for a given stimulus and task, discriminability values drive participants’ average categorization accuracy and reaction times. (B) Discriminability values can also be computed for arbitrary sets of target (green) and distractor (brown) images. The normalized distance between these two distributions will determine how easy or difficult the task, as a whole, will be for human participants.</p>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534755, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004456.g002", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computing_discriminability_values_/1534755", "title"=>"Computing discriminability values.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-03 14:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2252129"], "description"=>"<p>We created many different image datasets to train and test the model on both a basic level categorization task (forest vs. natural stimuli) and a superordinate categorization task (man-made vs. natural stimuli). This was done by considering all possible combinations of 3 basic categories from a larger set of natural categories and all possible combinations of 3 basic categories from a larger set of man-made categories. We computed discriminability values for all the corresponding categorization tasks and chose natural and man-made combination sets of stimuli to create 2 experimental conditions: (1) A <i>superordinate advantage</i> condition for which the model predicted high perceptual discriminability for superordinate-level categorization but low discriminability for basic-level categorization (blue line). The combination set included ‘beach,’ ‘cultivated field,’ and ‘coral reef’ for natural categories and ‘alley,’ ‘street,’ and ‘skyscraper’ for man-made stimuli. (2) A <i>basic advantage</i> condition for which the model predicted the opposite trend (low perceptual discriminability for superordinate-level categorization and high discriminability for basic-level categorization, red line). The combination set included: ‘beach,’ ‘iceberg,’ and ‘desert’ for the natural category while the man-made category included ‘alley,’ ‘amphitheater,’ and ‘highway.’</p>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534763, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004456.g007", "stats"=>{"downloads"=>3, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reversal_category_picking_framework_/1534763", "title"=>"Reversal category picking framework.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-03 14:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2252128"], "description"=>"<p>(A). Model discriminability values were used to sample stimulus sets to yield a high discriminability for superordinate categorization and a low discriminability for basic categorization to try to replicate the superordinate advantage (“superordinate advantage” condition) as well as a low discriminability for superordinate categorization and a high discriminability for basic categorization to try to reverse the superordinate advantage (“basic advantage” condition). (B) Representative images used in the experiment. Note that the original stimuli could not be shown because of copyright issues. Instead, shown are visually similar images from Flickr with a Creative Common licence. (C) Experimental results: The model correctly predicted higher accuracy and lower mean RTs for the superordinate vs. basic categorization task in the superordinate advantage condition and the opposite trend in the basic advantage condition.</p>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534762, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004456.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_2_Reversing_the_superordinate_advantage_/1534762", "title"=>"Experiment 2: Reversing the superordinate advantage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-03 14:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2252123"], "description"=>"<p>(A). Overview of the experimental design: Each trial began with a fixation cross followed by the subsequent brief presentation of an image (7 ms). Participants were required to respond within 500 ms. (B) Representative scenes sampled at five distinct discriminability value levels for a natural vs. man-made categorization task. Note that the original stimuli used could not be shown because of copyright and were replaced instead by visually similar images found on Flickr under the creative common. (C) Average results across all participants: Accuracy (percentage of correct responses, blue) and mean reaction time (RT) for correct responses (red) as a function of discriminability values as predicted by the model. Curves correspond to a GLMM fit and error bars to the standard deviation of the mean. (D) Results for individual participants.</p>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534757, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004456.g003", "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_1_Model_discriminability_values_predicted_participants_mean_accuracy_and_RTs_/1534757", "title"=>"Experiment 1: Model discriminability values predicted participants mean accuracy and RTs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-03 14:46:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2252119"], "description"=>"<p>(A) Perceptual space: Visual features are first extracted from individual images, which can then be represented as datapoints in an <i>N</i>-dimensional space. (B–D) Categorization boundaries: The model assumes that different categorization tasks carve up the same perceptual space and correspond to different categorization boundaries (shown for hypothetical tasks: Superordinate level—‘natural’ vs. ‘man-made’ in (B), basic level—‘beach’ vs. ‘forest’ in (C) and scene attribute—‘easy’ vs. ‘hard’ to navigate in (D).</p>", "links"=>[], "tags"=>["image", "Perceptual Categorization Observers", "discriminability", "variation", "response", "categorization tasks"], "article_id"=>1534753, "categories"=>["Biological Sciences", "Science Policy", "Ecology"], "users"=>["Imri Sofer", "Sébastien M. Crouzet", "Thomas Serre"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004456.g001", "stats"=>{"downloads"=>6, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principles_of_scene_categorization_/1534753", "title"=>"Principles of scene categorization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-03 14:46:36"}

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