Rapid ensemble encoding of average scene features

Visual ensemble perception involves the rapid global extraction of summary statistics (e.g., average features) from groups of items, without requiring single-item recognition and working memory resources. One theory that helps explain global visual perception is the principle of feature diagnosticity. This is when informative bottom-up visual features are preferentially processed to complete the task at hand by being consistent with one's top-down expectations. Past literature has studied ensemble perception using groups of objects and faces and has shown that both low-level (e.g., average color, orientation) and high-level visual statistics (e.g., average crowd animacy, object economic value) can be efficiently extracted. However, no study has explored whether summary statistics can be extracted from stimuli higher in visual complexity, necessitating global, gist-based processing for perception. To investigate this, across five experiments we had participants extract various summary statistical features from ensembles of real-world scenes. We found that average scene content (i.e., perceived naturalness or manufacturedness of scene ensembles) and average spatial boundary (i.e., perceived openness or closedness of scene ensembles) could be rapidly extracted within 125 ms, without reliance on working memory. Interestingly, when we rotated the scenes, average scene orientation could not be extracted, likely because the perception of diagnostic edge information (i.e., cardinal edges for typically encountered upright scenes) was disrupted when rotating the scenes. These results suggest that ensemble perception is a flexible resource that can be used to extract summary statistical information across multiple stimulus types but also has limitations based on the principle of feature diagnosticity in global visual perception.