通过学生的情绪价值:在年轻成年人中控制视觉复杂性和情绪兴奋下进行机器学习分类
Jung Joo Lee1, Eun Seo Park2, Hwa Jin Han1
1College of Police Administration, Dongguk University, Seoul, Republic of Korea.
Physiological reports
|March 4, 2026
概括
学生的反应模式可以在控制视觉复杂性和兴奋时可靠地表明情绪价值. 这项研究增强了瞳孔测量.
科学领域:
- 认知神经科学 认知神经科学
- 心理生理学 心理生理学
- 情感科学是一种情感科学.
背景情况:
- 瞳孔测量是一种针对情绪价值的非侵入性测量方法.
- 以前的研究表明,由于混视觉和情绪因素,结果不确切.
研究的目的:
- 为了确定学生的反应模式是否可靠地区分积极和消极的情绪刺激.
- 为了明确控制视觉复杂性 (空间频率) 和情绪激发.
主要方法:
- 呈现了50个标准化图像 (25个是正面的,25个是负面的) 并记录了学生的反应.
- 利用基于时间曲线的动态聚类来分析时间瞳孔大小变化.
- 在视觉条件下控制空间频率和唤醒水平.
主要成果:
- 当控制兴奋和空间频率时,分类准确性显著提高.
- 在低兴奋,高SF条件下,物流回归实现了79%的准确性和0.88 AUC.
- 关键预测指标包括瞳孔膨胀曲线下的面积.
结论:
- 在受控的感知和情感条件下,瞳孔反应可靠地表明了情感价值.
- 严格控制视觉复杂性和唤起对于以瞳孔测量为基础的情感研究至关重要.
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