What Makes an Image Interesting and How Can We Explain It.

Here, we explore the question: What makes a photograph interesting? Answering this question deepens our understanding of human visual cognition and knowledge gained can be leveraged to reliably and widely disseminate information. Observers viewed images belonging to different categories, which covered a wide, representative spectrum of real-world scenes, in a self-paced manner and, at trial's end, rated each image's interestingness. Our studies revealed the following: landscapes were the most interesting of all categories tested, followed by scenes with people and cityscapes, followed still by aerial scenes, with indoor scenes of homes and offices being least interesting. Judgments of relative interestingness of pairs of images, setting a fixed viewing duration, or changing viewing history - all of the above manipulations failed to alter the hierarchy of image category interestingness, indicating that interestingness is an intrinsic property of an image unaffected by external manipulation or agent. Contrary to popular belief, low-level accounts based on computational image complexity, color, or viewing time failed to explain image interestingness: more interesting images were not viewed for longer and were not more complex or colorful. On the other hand, a single higher-order variable, namely image uprightness, significantly improved models of average interest. Observers' eye movements partially predicted overall average interest: a regression model with number of fixations, mean fixation duration, and a custom measure of novel fixations explained >40% of variance. Our research revealed a clear category-based hierarchy of image interestingness, which appears to be a different dimension altogether from memorability or awe and is as yet unexplained by the dual appraisal hypothesis.