动态时间曲线与矢量自回归模型对心理过程的网络分析
Floor van der Does1, Wessel van Eeden1, Laura F Bringmann2
1Department of Psychiatry, Leiden University Medical Center (LUMC), Leiden, The Netherlands.
Scientific reports
|April 5, 2025
概括
动态时间扭曲 (DTW) 与多级向量自回归 (mlVAR) 相比,为心理数据提供了更可靠的网络分析,特别是当假设被违反时. DTW擅长揭示复杂动态系统中的时间关系.
科学领域:
- 心理学 心理学 心理学
- 网络科学 网络科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 精神病理障碍越来越多地被视为由相互连接的症状网络所代表的复杂的动态系统.
- 多级向量自回归 (mlVAR) 模型通常用于这些网络分析.
- 然而,心理数据往往违反了mlVAR的假设.
研究的目的:
- 评估动态时间扭曲 (DTW) 和mlVAR用于分析心理数据中的时间关系的应用,优点和缺点.
- 在违反mlVAR假设的条件下比较DTW和mlVAR的稳定性.
主要方法:
- 利用了来自荷兰抑郁和焦虑研究的376名参与者的生态瞬间评估数据.
- 在20个情绪和身体状况项目上使用mlVAR和DTW技术构建项目网络.
- 使用模拟数据在各种假设违规的情况下测试网络可靠性,包括滞后关系和碰撞器变量.
主要成果:
- 模拟数据分析表明,mlVAR网络比DTW网络更容易发生虚假连接.
- 在违反mlVAR假设的情况下,DTW在构建网络方面表现出更高的可靠性.
- 当假设成立时,mlVAR对于因果推断是有效的,而DTW对于检查并发和同步是强大的.
结论:
- 动态时间扭曲 (DTW) 是多级向量自回归 (mlVAR) 的强大替代方案,用于分析心理数据中的复杂时间动态,特别是当数据违反标准模型假设时.
- DTW提供了一种可靠的方法来评估同步和滞后的连接在现实世界的心理评估.
- 这些发现表明,DTW是理解精神病理学中症状的动态相互作用的宝贵工具.
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