Abstract art paintings, global image properties, and verbal descriptions: An empirical and computational investigation

Abstract While global image properties (GIPs) relate to preference ratings in many categories of visual stimuli, this relationship is typically not seen for abstract art paintings. Using computational network science and empirical methods, we further investigated GIPs and subjective preferences. First, we replicated the earlier observation that GIPs do not relate to preferences for abstract art. Next, we estimated the network structure of abstract art paintings using two approaches: the first was based on verbal descriptions and the second on GIPs. We examined the extent to which network measures computed from these two networks (1) related to preference for abstract art paintings and (2) determined affiliation of images to specific art styles. Only semantic-based network predicted the subjective preference ratings and art style. Finally, preference and GIPs differed for sub-groups of abstract art paintings. Our results demonstrate the importance of verbal descriptors in evaluating abstract art, and that it is not useful in empirical aesthetics to treat abstract art paintings as a single category.