亲密关系伙伴之间的情绪动态模式预测了两年后他们的分离:一种机器学习方法.
Peter Hilpert1, Matthew R Vowels1,2, Merijn Mestdagh3
1Department of Psychology, University of Lausanne, Lausanne, Switzerland.
PloS one
|July 6, 2023
概括
关系的稳定性是预测如何合作伙伴的情绪在积极的互动,而不是消极的情绪不同,并伴侣在一起. 这一发现强调了情感动态在预测关系长寿方面的重要性.
科学领域:
- 心理学 心理学 心理学
- 关系科学科学 关系科学
- 情感科学是一种情感科学.
背景情况:
- 当代情感理论表明,伴侣的情感合预测了关系的运作.
- 有限的研究已经探索了个人和双层情感动态如何预测关系分离.
- 了解这些模式对于关系科学和临床干预至关重要.
研究的目的:
- 调查互动期间的个体和双层情绪模式是否预测关系分离.
- 为了比较正面与负面相互作用期间情绪的预测能力.
- 利用机器学习来识别复杂的关系模式.
主要方法:
- 利用机器学习分析了101对夫妇 (N=202) 在积极和消极互动期间的情绪数据.
- 评估了个体内情绪变化和个体间情绪合.
- 追踪了两年的关系稳定性,注意到17次分手.
主要成果:
- 在负面互动期间的情绪并不能预测关系的分离.
- 在积极互动期间的个体内情绪变化预测了分离.
- 在积极互动期间,伴侣的情绪之间的合也预测了分离.
结论:
- 积极的互动情感动态,特别是个体内部的变化和双向合,是关系分离的关键预测因素.
- 机器学习方法可以增强对人际关系中复杂情感模式的理解.
- 这些发现有助于情感理论,并为关系稳定提供了洞察力.
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