Adaptation is ubiquitous in the human visual system, allowing recalibration to the statistical regularities of its input. Previous work has shown that global scene properties such as openness and mean depth are informative dimensions of natural scene variation useful for human and machine scene categorization (Greene & Oliva, 2009b; Oliva & Torralba, 2001). A visual system that rapidly categorizes scenes using such statistical regularities should be continuously updated, and therefore is prone to adaptation along these dimensions. Using a rapid serial visual presentation paradigm, we show aftereffects to several global scene properties (magnitude 8-21%). In addition, aftereffects were preserved when the test image was presented 10 degrees away from the adapted location, suggesting that the origin of these aftereffects is not solely due to low-level adaptation. We show systematic modulation of observers' basic-level scene categorization performances after adapting to a global property, suggesting a strong representational role of global properties in rapid scene categorization.
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