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John Defant, Thomas Sanocki, Steven Schultz, Trang Nguyen; Visualizing the Percept of a Scene. Journal of Vision 2017;17(10):560. doi: 10.1167/17.10.560.
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© ARVO (1962-2015); The Authors (2016-present)
Scene perception is a powerful, remarkable process that handles an immense variety of possible scenes efficiently. But the question of "what people see" remains largely unanswered. What do people see in a briefly presented, novel but high-integrity scene? How does the percept change across individuals? And can the percept be primed by general abstract concepts or by repetitions of words? The present research extends a full report method in which observers make a written report of what they saw in a briefly presented, novel scene. There were two critical scenes, each involving a mixture of people-related and structure-related content. Overall, the written reports were perceptually accurate, with high hit rates for content unique to the scenes and few inaccuracies. The priming manipulation was that, before the two critical scenes, observers saw scenes containing either people or structural content. The priming manipulation increased reports of prime-consistent details. We use these results to create scene percepts: Descriptions of what people saw in the scene. Most of the descriptions fall into a people-activity-structure schema. The major effect of priming was to increase the amount of structure-related information reported by the structure-primed group. People information was similar in absolute amounts between priming groups, suggesting that there is a more absolute priority for people information. Further analyses quantify how much of the priming was due to repetition of particular words versus abstract concepts.
Meeting abstract presented at VSS 2017
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