Recent advances in computational models in vision science have considerably furthered our understanding of human visual perception. At the same time, rapid advances in convolutional deep neural networks (DNNs) have resulted in computer vision models of object recognition which, for the first time, rival human object recognition. Furthermore, it has been suggested that DNNs may not only be successful models for computer vision, but may also be good computational models of the monkey and human visual systems. The advances in computational models in both vision science and computer vision pose two challenges in two different and independent domains: First, because the latest computational models have much higher predictive accuracy, and competing models may make similar predictions, we require more human data to be able to statistically distinguish between different models. Thus we would like to have methods to acquire trustworthy human behavioural data fast and easy. Second, we need challenging experiments to ascertain whether models show similar input-output behaviour only near "ceiling" performance, or whether their performance degrades similar to human performance: only then do we have strong evidence that models and human observers may be using similar features and processing strategies. In this paper we address both challenges.
Art experience is per definition a dynamic way of processing: While perceiving the artistic object, the film, the music play, we undergo complex affective as well as cognitive experiences interactively changing the entire processing. Elaboration, understanding, aesthetic aha-insights etc. change the view on the to-be-processed entity—psychologically interpreted, the entity becomes a part of ourselves. Most methods of measuring art experience are not able to reflect on these dynamics; most of them are just object-based, e.g. correlative approaches of bringing statically assumed object-properties together with simple ratings on these "objects". Here, I will demonstrate the limitations of such approaches, accompanied by the introduction of some simple principles to be followed when art experience is the focus of research. I will then introduce some methods which can assist in unfolding the process character of art experience without interfering too much with the experience as such: For instance, by using posturography, the Continuous Evaluation Procedure (CEP) or automatic facial expression routines. When these techniques are employed with clear rationales in mind, and by deriving concrete hypotheses from a well-grounded theoretical approach, we can come much closer to the rich experience people have when encountering and elaborating art. This will assist us in our human-history-encompassing endeavor of deciphering what and how art is processed and appreciated.