There is beauty in gist: An investigation of aesthetic perception in rapidly presented scenes

While an artfully crafted painting can evoke profound aesthetic experience, the same applies to a grand ballroom or sunset. Like fine art, everyday scenes contain aesthetic qualities, with some scenes preferred over others. The meaning or semantic label of a scene, known as scene gist, is extracted rapidly and automatically, with just a brief glance, computed mainly using low spatial frequencies (LSF) in the image. Although we can easily identify a scene, the question remains, if an accurate aesthetic impression can be formed from such rapid and coarse overall representation. We investigated the characteristics of scene gist to determine if aesthetic preference can be extracted with such short display durations. Furthermore, given that scene gist is based on an initial coarse representation, we asked whether LSF renderings of these scenes would elicit similar aesthetic judgments. Using a between-groups design, we found a significant positive correlation between aesthetic judgments on real-world scenes for images viewed for an unlimited amount of time and those viewed for only 45ms, but no significant correlation with the LSF set. This demonstrates that aesthetic judgments can be extracted rapidly and are relatively stable across display durations but do not survive image degradation, suggesting that image content outweighs structure. We performed the Implicit Associations Test by using aesthetically pleasing and non-pleasing images from the previous experiment paired with aesthetically pleasing and non-pleasing words, to examine whether these aesthetic judgments are also made automatically when they are irrelevant to the task. Participants made significantly more classification errors and were slower when pleasing scenes were paired with non-pleasing words. This suggests that participants could not help but make aesthetic judgments on real-world scenes. Additionally, we found that the most pleasing and non-pleasing scenes differed significantly on self-similarity and anisotropy, measures of image statistics relating to computational aesthetics. Meeting abstract presented at VSS 2015