One point that definitions of art experience disagree about is whether this kind of experience is qualitatively different from experiences related to ordinary objects and everyday contexts. Here, we follow an ecological approach assuming that art experience has its own specific quality that is, not least, determined by typical contexts of art presentation. Practically, we systematically observe typical phenomena of experiencing art in ecologically valid or real-world settings such as museum contexts. Based on evidences gained in this manner, we emulate (...) and implement essential properties of ecological contexts (e.g., free choice of viewing distance and time, large scale of artworks and exhibition-like context) in controlled laboratory experiments. We found, for instance, that for large scale paintings by Pollock and Rothko preferred viewing distances as well as distances inducing most intense art experiences including Aesthetic Aha insights were much larger than typical viewing distances realized in lab studies. Following Carbon’s (2019) terminology of measurement strategies of art experience, the combined use of “Path #1” (real-world context) and “Path #2” (mildly controlled, still ecologically valid settings and contexts), enables us to understand and investigate much closer what is really happening when people experience art. (shrink)

3 In live performances seated audiences have restricted opportunities for response. Some 4 responses are obvious, such as applause and cheering, but there are also many apparently 5 incidental movements including posture shifts, fixing hair, scratching and adjusting glasses. 6 Do these movements provide clues to people’s level of engagement with a performance? Our 7 basic hypothesis is that audience responses are part of a bi-directional system of audience- 8 performer communication. This communication is part of what distinguishes live from (...) recorded 9 performance and underpins live performers’ moment-to-moment sense of how well a performance 10 is going. Here we investigate the range of visible real-time movements of audiences in four live 11 contemporary dance performances. Video recordings of performers and audiences were analysed 12 using computer vision techniques for extracting face, hand and body movement data. The 13 meaning of audience movements were analysed by comparing clips of the audience at moments 14 of maximum and minimum movement to expert and novice judges. The results show that audience 15 clips with the lowest overall movement are judged as displaying the highest engagement. In 16 addition, we found that while there is no systematic relationship between audience and dancers 17 movement, hands seem to play an especially significant role since they move significantly more 18 compared to the rest of the body. We draw on these findings to argue that collective stillness is 19 an especially salient signal of audience engagement. (shrink)