使用ML来预测认知功能的性能使用ML的陷
Gianna Kuhles1,2, Sami Hamdan3,4, Stefan Heim5,6,7
1Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. g.kuhles@fz-juelich.de.
Scientific reports
|October 30, 2025
概括
机器学习对认知能力的预测可能会因混变量而有缺陷. 这项研究表明,年龄,性别和教育如何膨胀执行职能预测的准确性,突出了需要仔细控制ML管道的需要.
科学领域:
- 认知神经科学 认知神经科学
- 计算语言学 计算语言学
- 心理测量 心理测量 心理测量
背景情况:
- 机器学习 (ML) 经常用于预测认知能力.
- 机器学习模型的实施和解释带有风险,特别是来自混变量.
研究的目的:
- 为了说明由于混变量导致的ML预测错误结论的风险.
- 为了证明潜在的错误漏洞在预测执行功能 (EF) 使用prosodic特征.
主要方法:
- 健康的参与者 (n=231) 完成了语言任务和EF测试.
- ML模型预测了使用264个体特征的EF性能,控制年龄,性别和教育.
- 深入分析检查了预测准确度的潜在漏洞.
主要成果:
- ML模型最初显示了执行功能 (EF) 变量 (Trail Making Test) 的合理预测性能.
- 深入分析显示,由于混杂因素 (年龄,性别,教育) 和目标EF性能之间的显著关系,预测准确度被膨胀.
- 确定了"混泄漏"的证据,扭曲了体特征的真正预测能力.
结论:
- 混变量在ML驱动的认知能力预测中存在重大风险.
- 在ML管道中,严格控制混变量至关重要,以避免错误的结论.
- 研究人员必须小心潜在的陷,并仔细解释ML预测结果.
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