通过潜伏马尔科夫因子分析来探索定性负面和正面情绪细粒度的内在人体变化
Marcel C Schmitt1, Leonie V D E Vogelsmeier2, Yasemin Erbas3,4
1Department of Psychology, RPTU Kaiserslautern-Landau, Landau, Germany.
Multivariate behavioral research
|April 11, 2024
概括
研究人员可以使用潜伏马尔科夫因子分析 (LMFA) 来研究随时间推移的情感细粒度 (EG) 模式. 这种方法揭示了不同的情绪状态以及压力等因素如何影响它们之间的转移,为个人情绪体验提供了更深入的见解.
科学领域:
- 心理学 心理学 心理学
- 量化心理学 量化心理学
- 情感科学是一种情感科学.
背景情况:
- 情绪细粒度 (EG) 描述了区分和标记情绪具有特异性的能力.
- 了解EG的个体内变异性对于全面了解情感体验至关重要.
- 现有的方法可能无法完全捕捉EG随时间的动态,定性模式.
研究的目的:
- 引入和验证潜伏马尔科夫因子分析 (LMFA) 作为一种用于调查定性情感细粒度的新方法.
- 探索人体内对负面和积极情绪的情感细粒度的变化.
- 检查情绪细粒度状态和个体轨迹之间的过渡的预测因素.
主要方法:
- 隐性马尔科夫因子分析 (LMFA) 用于分析经验抽样研究中的数据.
- 该研究包括139名参与者的11,662次测量,评估随时间推移的情绪.
- 用LMFA来识别情绪细粒度的潜在状态和状态轨迹中的个体差异.
主要成果:
- 对于负面和积极的情绪,有三个不同的情绪细粒度的潜在状态被确定为负面和积极的情绪.
- 瞬间的压力显著预测了这些情绪细粒度状态之间的过渡.
- 状态轨迹的个体差异与神经病症和情绪调节与负面情绪有关.
结论:
- 隐性马尔科夫因子分析 (LMFA) 提供了一种强大的工具,用于详细调查定性情感细粒度.
- 这些发现强调了情绪细粒度的动态性质及其对情境因素 (如压力) 的易感性.
- 这种方法提供了对个人情绪处理及其潜在心理因素的更细致的理解.
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