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• W2005486148 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 85y) and viewed the clips in the lab, for 2,400 responses, and by 99 crowdsourced participants (age range 20 to 66y) 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." @default.
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• W2005486148 date "2014-04-02" @default.
• W2005486148 modified "2023-10-11" @default.
• W2005486148 title "Measuring Information Acquisition from Sensory Input Using Automated Scoring of Natural-Language Descriptions" @default.
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• W2005486148 doi "https://doi.org/10.1371/journal.pone.0093251" @default.
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