使用机器学习来预测心理反应对社会互动的个体差异
Ole Hätscher1, Johannes L Klinz1, Niclas Kuper1
1Department of Psychology, University of Munster.
Journal of personality and social psychology
|January 26, 2026
概括
使用机器学习预测心理反应的个体差异显示出有限的成功. 机器学习模型略高于基线预测,凸显了对个人级别变量进行复制和进一步研究的需要.
科学领域:
- 心理学 心理学 心理学
- 计算社会科学 计算社会科学
背景情况:
- 在社会互动研究中,心理反应的个体差异至关重要.
- 之前的研究发现了显著的个体差异,但缺乏强大的预测因素.
研究的目的:
- 为了确定是否可以预测对社会相互作用的反应的个体差异.
- 识别用于预测这些反应性的关键个人级别变量.
主要方法:
- 应用机器学习对三大经验采样数据集 (N=5,047) 进行了应用.
- 使用多级建模 (随机斜率) 提取了反应性的个体差异.
- 使用社会人口统计学,人格和态度预测的反应性,通过交叉验证和独立样本进行验证.
主要成果:
- 机器学习模型显示,对于反应性的个体差异的总体预测性能有限.
- 在大多数分析中,复杂模型的表现略高于基线模型.
- 沙普利的添加式解释确定了预测模式和相互作用.
结论:
- 在不同的结果和样本中复制机器学习发现是必不可少的.
- 鉴定到的预测模式需要进一步调查.
- 结果为人格的动态概念化提供信息,并限制了当前的ML对个人内部数据的局限性.
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