Abstract

The brain has access to visual information via a variety of neural codes, e.g. there are cells tuned to edge orientation, spatial frequency, edges, corners, contrasts and colour. Higher cortical areas of the brain are thought to then further process visual information as well as integrate a visual scene with other senses and the conscious mind. How this visual information is combined with experience to form a holistic experience that elicits an emotional response of pleasure, distress and/or other forms of meaning is an active research question. The human perception of art and the question of what constitutes art touch upon all these elements of perception and integration. We shall show how topology provides a vital new lens in examining these questions.   We discuss recent work where methods from applied topology, namely persistent homology of certain cubical complexes, are applied to images produced by both a human artist and a neural network shown in two exhibitions in Torun, Poland. The images shown in the two exhibitions were matched for certain information-theoretic pixel-based characteristics. Experimental measurements of EEG , tracking of eye movement, as well as conscious perception/appreciation of these paintings were collected from a selection of visitors to these exhibitions, namely second-year art students. Our topological approach provided highly effective methods of disambiguation not only between artist vs. AI-generated art but also provided an easily computable, information-dense method to quantify the characteristics of art produced by famous modern abstract painters. We speculate on how aspects of our topological approach may be insightful to Gestalt psychologists and other theories of perceptual integration.