基于Eysenck个性特征和性别预测诚实的行为:使用SHAP分析进行可解释的机器学习研究
Yu Meng1, Zili Peng1, Zhitong Zhang1
1College of Flight Technology, Civil Aviation Flight University of China, Deyang, China.
Frontiers in psychology
|April 24, 2025
概括
神经症和男性性别预测航空学员的不诚实. 这项研究使用机器学习来识别人格特征和影响诚实行为的性别,帮助安全干预.
科学领域:
- 心理学 心理学 心理学
- 航空安全航空安全
- 机器学习 机器学习
背景情况:
- 有限的研究将Eysenck的个性特征应用于航空业中的诚实预测.
- 航空领域的不诚实行为,如事故报告不足,会对安全产生严重影响.
研究的目的:
- 调查艾森克的个性特征 (神经病,精神病,外向) 和性别作为航空不诚实行为的预测因素.
- 应用可解释的机器学习来建模这些关系,并确定关键的诚实预测因素.
主要方法:
- 从102名飞行和空中交通管制学员的行为数据采集,使用硬币抛任务.
- XGBoost (可解释的机器学习) 模拟了人格,性别和诚实之间的非线性关系.
- SHAP分析确定了关键预测因素,并通过AUC评估模型性能.
主要成果:
- XGBoost的预测准确度很高 (AUC = 0.802).
- 神经症是不诚实的最强有力的预测因素.
- 男人表现出更高的不诚实率,并观察到精神病症和外向的门效应.
结论:
- 这项研究系统地将Eysenck模型应用于航空诚信预测.
- 性别显著调节了诚实倾向.
- 一个由SHAP驱动的框架将机器学习见解与数据驱动的学员查和航空安全干预的心理理论联系起来.
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