在交互式装置艺术中隐藏的情绪状态过渡的马尔科夫建模
Xiaowei Chen1,2, Jinlei Li3
1College of Arts, Zhejiang Shuren University, Hangzhou, China. 2021859926@student.uitm.edu.my.
Scientific reports
|October 17, 2025
概括
交互式艺术装置可以促进集体福祉. 隐藏的马尔科夫模型 (HMMs) 显示,积极的情绪状态是公共艺术环境中最稳定和最主导的,促进整体的积极性.
科学领域:
- 心理学 心理学 心理学
- 计算社会科学 计算社会科学
- 艺术与博物馆研究 艺术与博物馆研究
背景情况:
- 传统的情感研究通常使用不切实际的实验室或纵向数据.
- 像艺术装置这样的公共文化环境为集体情感研究提供了独特的背景.
- 交互式艺术装置为研究现实世界的情感动态提供了一个新的环境.
研究的目的:
- 应用隐藏的马尔科夫模型 (HMMs) 来分析集体情绪在一个交互式艺术装置.
- 从横截面幸福数据推断潜在的情感状态及其概率模式.
- 在公共艺术背景下调查情绪状态的稳定性,差异化和融合.
主要方法:
- 利用隐藏的马尔科夫模型 (HMMs) 来分析自我报告的福利数据.
- 将HMM应用到来自苏格兰国家美术馆"HappyHere"参与式灯光装置的数据上.
- 分析横截面数据以识别潜伏的情绪状态及其动态特性.
主要成果:
- 确定了三种不同的潜在情绪状态:低/负,中度积极和高度积极.
- 发现积极的情绪状态是最稳定的,表现出最高的自我转换概率和最长的停留时间.
- 观察到中立状态不稳定,倾向于转向积极性,积极影响的总体占优势 (86.3%).
结论:
- 隐藏的马尔科夫模型 (HMM) 能够从横截面的文化数据集中获得有意义的见解.
- 参与式艺术装置可以有效调节情绪,促进集体福祉.
- 积极影响在交互式公共艺术环境中作为一个稳定且占主导地位的吸引力.
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