剖析情绪的评价和多元特征:自然主义刺激期间多层次注释的证据
Elenor Morgenroth1, Rukshani Somarathna2, Dimitri Van De Ville1
1Neuro-X Institute, Ecole Polytechnique Federale de Lausanne.
Emotion (Washington, D.C.)
|January 12, 2026
概括
这项研究揭示了离散的情绪是由评估过程等级组织的. 机器学习模型从它们的基础组件中准确预测了所有13种情绪,突出了评估评估.
科学领域:
- 心理学 心理学 心理学
- 认知科学 认知科学
- 情感科学是一种情感科学.
背景情况:
- 理论模型提出,情绪是从评估情境维度中产生的.
- 组件过程模型强调评估在塑造动机,表达,生理和感觉方面.
- 了解情绪组成部分和分立的情绪体验之间的联系至关重要.
研究的目的:
- 调查离散情绪及其潜在组成部分之间的关系.
- 探索基于评估维度的情绪等级组织.
- 应用预测建模来确定组件对离散情绪类别的贡献.
主要方法:
- 在观看短片时收集了50多个情感变量的连续注释.
- 利用评估维度 (价值,自我相关性) 来分析情绪组织.
- 采用机器学习预测模型来评估组件对离散情绪的贡献.
主要成果:
- 观察到一层次的分离情绪组织,由价值和自我相关性评估驱动.
- 数据集中的所有13种离散情绪都通过情绪组件的特定组合可靠地预测.
- 评估过程被认为是区分情感体验的关键因素.
结论:
- 评估过程在区分分开的情感体验中起着核心作用.
- 机器学习通过分析其组成部分,有效地剖析了情绪的复杂性质.
- 研究结果支持情绪的适应功能,并为其复杂性提供了洞察力.
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