Measuring Information Acquisition from Sensory Input Using Automated Scoring of Natural-Language Descriptions
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{"title"=>"Measuring information acquisition from sensory input using automated scoring of natural-language descriptions", "type"=>"journal", "authors"=>[{"first_name"=>"Daniel R.", "last_name"=>"Saunders", "scopus_author_id"=>"35338084700"}, {"first_name"=>"Peter J.", "last_name"=>"Bex", "scopus_author_id"=>"7007010524"}, {"first_name"=>"Dylan J.", "last_name"=>"Rose", "scopus_author_id"=>"56120484900"}, {"first_name"=>"Russell L.", "last_name"=>"Woods", "scopus_author_id"=>"7401707048"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84898880427", "issn"=>"19326203", "scopus"=>"2-s2.0-84898880427", "pmid"=>"24695546", "pui"=>"372870067", "doi"=>"10.1371/journal.pone.0093251"}, "id"=>"d9041aad-5a60-3da0-bd3f-a55640d9bbe0", "abstract"=>"Information acquisition, the gathering and interpretation of sensory information, is a basic function of mobile organisms. We describe a new method for measuring this ability in humans, using free-recall responses to sensory stimuli which are scored objectively using a \"wisdom of crowds\" approach. As an example, we demonstrate this metric using perception of video stimuli. Immediately after viewing a 30 s video clip, subjects responded to a prompt to give a short description of the clip in natural language. These responses were scored automatically by comparison to a dataset of responses to the same clip by normally-sighted viewers (the crowd). In this case, the normative dataset consisted of responses to 200 clips by 60 subjects who were stratified by age (range 22 to 85 y) and viewed the clips in the lab, for 2,400 responses, and by 99 crowdsourced participants (age range 20 to 66 y) who viewed clips in their Web browser, for 4,000 responses. We compared different algorithms for computing these similarities and found that a simple count of the words in common had the best performance. It correctly matched 75% of the lab-sourced and 95% of crowdsourced responses to their corresponding clips. We validated the measure by showing that when the amount of information in the clip was degraded using defocus lenses, the shared word score decreased across the five predetermined visual-acuity levels, demonstrating a dose-response effect (N = 15). This approach, of scoring open-ended immediate free recall of the stimulus, is applicable not only to video, but also to other situations where a measure of the information that is successfully acquired is desirable. Information acquired will be affected by stimulus quality, sensory ability, and cognitive processes, so our metric can be used to assess each of these components when the others are controlled.", "link"=>"http://www.mendeley.com/research/measuring-information-acquisition-sensory-input-using-automated-scoring-naturallanguage-descriptions-3", "reader_count"=>15, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>4, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>1, "Student > Bachelor"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>4, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>1, "Student > Bachelor"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>7, "Agricultural and Biological Sciences"=>1, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Psychology"=>1, "Computer Science"=>2, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>7}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>2}, "Linguistics"=>{"Linguistics"=>1}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>2}, "group_count"=>0}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1446572"], "description"=>"<p>Mean number of words shared with responses to the same clip in the lab-sourced dataset, for different levels of visual acuity as achieved with defocus lenses. Error bars indicate 95% confidence intervals.</p>", "links"=>[], "tags"=>["neuroscience", "Cognitive neuroscience", "Cognitive neurology", "cognitive science", "cognition", "memory", "Recall (memory)", "Cognitive psychology", "Sensory systems", "Visual system", "Sensory perception", "psychology", "Diagnostic medicine", "Mental health and psychiatry", "neurology", "mathematics", "Applied mathematics", "algorithms", "Information technology", "Natural Language Processing", "acuity"], "article_id"=>984331, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Daniel R. Saunders", "Peter J. Bex", "Dylan J. Rose", "Russell L. Woods"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093251.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_visual_acuity_on_shared_word_score_/984331", "title"=>"Effect of visual acuity on shared word score.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-02 02:43:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1446570"], "description"=>"<p>Results of simulations on the two datasets for the percent correctly classified when the size of the normative dataset was reduced from its full size of either 20 or 12 responses per video clip. Each point is based on 100 random samples. The solid line indicates the best fit of a two-parameter exponential function, while the dashed line indicates the point at which the function reached 99% of the asymptote.</p>", "links"=>[], "tags"=>["neuroscience", "Cognitive neuroscience", "Cognitive neurology", "cognitive science", "cognition", "memory", "Recall (memory)", "Cognitive psychology", "Sensory systems", "Visual system", "Sensory perception", "psychology", "Diagnostic medicine", "Mental health and psychiatry", "neurology", "mathematics", "Applied mathematics", "algorithms", "Information technology", "Natural Language Processing", "classification", "smaller", "dataset"], "article_id"=>984329, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Daniel R. Saunders", "Peter J. Bex", "Dylan J. Rose", "Russell L. Woods"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093251.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Response_classification_rate_for_smaller_dataset_sizes_/984329", "title"=>"Response classification rate for smaller dataset sizes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-02 02:43:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1446571"], "description"=>"<p>Error of the mean word score, relative to the score for the full normative dataset, as a function of the size of the A) lab-sourced dataset, or B) crowdsourced dataset. There were 100 random samples for each set size. Boxes indicate the interquartile range (IQR), the whiskers contain the range +/− 1.5 * IQR beyond the limits of the boxes (corresponding to 99.7% of the area of a normal distribution).</p>", "links"=>[], "tags"=>["neuroscience", "Cognitive neuroscience", "Cognitive neurology", "cognitive science", "cognition", "memory", "Recall (memory)", "Cognitive psychology", "Sensory systems", "Visual system", "Sensory perception", "psychology", "Diagnostic medicine", "Mental health and psychiatry", "neurology", "mathematics", "Applied mathematics", "algorithms", "Information technology", "Natural Language Processing", "scores", "smaller", "dataset"], "article_id"=>984330, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Daniel R. Saunders", "Peter J. Bex", "Dylan J. Rose", "Russell L. Woods"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093251.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Error_distribution_of_shared_word_scores_for_smaller_dataset_sizes_/984330", "title"=>"Error distribution of shared word scores for smaller dataset sizes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-02 02:43:44"}

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Relative Metric

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