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Trang Nguyen, John Defant, Steven Schultz, Thomas Sanocki; Does Scene Perception Involve an Active Schema?. Journal of Vision 2016;16(12):321. doi: https://doi.org/10.1167/16.12.321.
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Results indicating rapid integration of the multiple types of information in a scene have led a number of investigators to propose that scene perception involves a schema (e.g., Fei-Fei, Greene, Intraub, Palmer). What is a schema, and what is an active schema? A schema can be viewed as a network for accepting information about scenes, with nodes representing important entities (e.g., object, surfaces, functions). Previous results indicated that the network can be primed by presenting scenes with similar entities (scenes with active people, or with building structures), resulting in more rapid subsequent perception of prime-consistent details (Sanocki & Schultz, VSS 2015). Here we ask how active this network is: Can it speed the perception of entities that are abstractly related to the primes? For example, when primed for building structures, will observers more rapidly encode information about art structures that are not buildings? Can scenes depicting different groups of animals prime an abstract identity schema that rapidly encodes people? Observers viewed a series of pictures and wrote brief descriptions of what they saw after each picture. After 5 prime pictures, the scenes on critical trials 6 and 7 contained both art structures and people (150 ms durations). Results indicate that the scene schema is moderately abstract but not highly abstract. Building structures primed art structures, and a mixture of animals and adults primed children. But a mixture of animals did not prime children. The pattern of results was observed for first mention (of people or structure), consistent with the idea that a schema serves to prioritize scene information. A similar pattern was observed for total correct words. An additional finding was that the full reports were quite accurate overall; perception was determined by scene content, not by general expectations.
Meeting abstract presented at VSS 2016
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