Proceedings of the Annual Meeting of the Cognitive Science Society

Aesthetic appreciation is inherently multidimensional: many different stimulus dimensions (e.g., colors, shapes, sizes) contribute to our aesthetic experience. However, most studies in empirical aesthetics used either non-parametrically controlled multidimensional or parametrically controlled unidimensional stimuli, preventing insight into the relative contribution of each stimulus dimension or any potential interactions between them to perceptual and aesthetic evaluations. To adress this gap we combined two recent developments: the Order & Complexity Toolbox for Aesthetics (Van Geert, Bossens, & Wagemans, 2023) for generating multidimensional parametrically controlled stimuli, and Gibbs Sampling with People (Harrison et al., 2020) for efficiently characterizing subjective evaluations in multidimensional stimulus space. We show the advantages of this new approach by estimating multidimensional probability distributions for both aesthetic (pleasure and interest) and perceptual evaluations (order and complexity) in two visual multidimensional parametric stimulus spaces, and we compare our results with findings from earlier studies that used either non-parametric or unidimensional stimuli.