Generative Models for the Psychology of Art and Aesthetics

Abstract

This paper describes how computational generative models can describe aspects of the artistic process, and how these generative models can provide tools for formulating and testing psychological theories of art. The term “generative models” here refers to algorithms that can generate artistic imagery, video, text, or other artistic media, including techniques developed in both computer graphics and AI research. Generative models can both describe artistic processes and offer useful experimental tools. This paper first outlines different ways to understand the types of research in generative models. It then surveys several recent examples of using generative models to develop theories and to perform experiments. The paper then discusses misleading uses of the concept of “AI-generated art” in psychological studies, and the need for study of our relationship with new artistic technologies. Finally, the paper offers a few remarks on pursuing interdisciplinary research across psychology and computer graphics.