Investigating the Emotional Responses to Generative Art

Background Generative art, which includes the development of artworks using algorithms and emergent behaviour, is receiving recognition for its tendency to evoke emotion. A vast spectrum of emotional responses may be elicited by generative art by applying dynamic patterns, compositional decisions, and movement. However, a lack of understanding currently exists regarding the emotional impact of creative expression. Methods In the first stage, data collection followed a research methodology that utilised a campus-based survey conducted with eighteen undergraduate design students who were reached through different university recruitment methods. The survey used a mix of quantitative and qualitative data collection methods since it involved viewing generative art stimuli, choosing relevant emotional descriptors and providing novel understandings of participants’ emotions through open-ended response approaches. The analysis adopted a descriptive statistical approach to determine the patterns and trends of the reported emotional responses. In the second stage, a group of six participants assessed three generated artworks, utilising techniques such as word cloud analysis and eye-tracking technology. The visual attention patterns and audio responses were investigated to gain insight into emotional experiences utilising eye-tracking technology. Participants employed word clouds to represent their emotional state. Results The analysis of eye-tracking data and verbal responses established that participants experienced different emotional reactions to the artworks. Different emotions — including engagement, optimism, analytical stance, neutrality, mixed emotions, and introspection — were experienced during phases of participants’ interactions with the system. The generated artworks prompted emotions of interest and excitement through their dynamic transitions and visual attractiveness. The resizing of graphical components and manipulations and movement of grid units prompted neutral emotions in certain cases. Conclusions The researchers also noticed that the patterns output by the algorithms, colour combinations, and symbolic representations had the potential to induce specific emotional states. This research could also lead to the development of more sophisticated communication applications and improved audience engagement. Future research and collaboration between scientists and artists are needed to fully understand the neural underpinnings of emotional reactions to generative art.