지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.7
- 수록면
- 65 - 79 (15page)
- DOI
- 10.15187/adr.2024.07.37.3.65
이용수
초록· 키워드
오류제보하기
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 campusbased 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 eyetracking 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.
Methods : In the first stage, data collection followed a research methodology that utilised a campusbased 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 eyetracking 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.
목차
Abstract
1. Introduction
2. Literature Review
3. Research Objectives
4. Conceptual Framework that Supports Generative Art and the Emotional Response
5 Methodology
6. Result
7. Conclusion
References
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