What Makes a Scene? Fast Scene Categorization as a Function of Global Scene Information at Different Resolutions

• Published in: Journal of experimental psychology. Human perception and performance 2022-08, Vol.48 (8), p.871-888
• Main Authors: Wiesmann, Sandro L., Võ, Melissa Le-Hoa
• Format: Article
• Language: English
• Subjects: Auditory Scene Analysis; Classification; Classification (Cognitive Process); Experiments; Female; Human; Male; Object Recognition; Spatial discrimination; Stimulus; Texture Perception
• ISSN: 0096-1523; 1939-1277
• DOI: 10.1037/xhp0001020

The ability to quickly and accurately categorize our environment into meaningful scene categories has been linked to the fast processing of global scene properties. Here, we causally tested this claim by reducing scene images to different sets of global scene properties with varying degrees of spatial resolution. In Experiment 1, human observers reached above-chance categorization accuracy for most stimulus conditions, indicating that images reduced to global scene properties indeed allow for correct categorization. In Experiment 2, we demonstrated that these features can be extracted from only 30 ms of stimulus presentation. However, accuracy in both experiments was far below performance on unmanipulated images and differed strongly between conditions. Images with higher resolutions yielded better performance than those with lower resolutions. In Experiment 3, we related this performance advantage to the identification of single objects in stimuli with higher resolutions which supports the notion that object information is used for scene categorization. Taken together, we show that global scene properties are useful but not sufficient for fast scene categorization. Instead, localized information is crucial in this process, presumably because it conveys object identities. Public Significance Statement We can categorize images of different environments within the glimpse of an eye. In this study, we show that, while global, unlocalized information is sufficient for above-chance categorization performance, the localization of visual information within an image is crucial for reaching the full efficiency of this task.