Statistical image properties and aesthetic judgments on abstract paintings by Robert Pepperell.

Abstract

In this exploratory study, we asked whether objective statistical image properties can predict subjective aesthetic ratings for a set of 48 abstract paintings created by the artist Robert Pepperell. Ruta and colleagues (2021) used the artworks previously to study the effect of curved/angular contour on liking and wanting decisions. We related a predefined set of statistical image properties to the eight different dimensions of aesthetic judgments from their study. Our results show that the statistical image properties can predict a large portion of the variance in the different aesthetic judgments by Ruta and colleagues. For example, adjusted R2 values for liking, attractiveness, visual comfort, and approachability range between 0.52 and 0.60 in multiple linear regression models with four predictors each. For wanting judgments in an (imagined) gallery context, the explained variance is even higher (adjusted R2 of 0.78). To explain these findings, we hypothesize that differences in cognitive processing of Pepperell's abstract paintings are minimized because this set of stimuli has no apparent content and is of uniform artistic style and cultural context. Under this condition, the aesthetic ratings by Ruta and colleagues are largely based on perceptual processing that systematically varies along a relatively small set of objective image properties.