使用机器学习和理解模型对人类认知的解释,自动检测认知事件
Quang Dang1, Murat Kucukosmanoglu2, Michael Anoruo3
1Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, 21250, USA. qdang1@umbc.edu.
Scientific reports
|August 20, 2025
概括
这项研究使用机器学习从瞳孔反应中检测认知事件. 分析瞳孔直径和凝视位置的模型显示了快速,个性化的认知状态监测的潜力.
科学领域:
- 认知神经科学 认知神经科学
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 由自主神经系统调节的瞳孔反应反映了认知工作量,注意力和兴奋.
- 标志着精神过程的开始的认知事件与认知工作负载密切相关,并触发可观察到的反应.
- 了解和检测认知事件对于监测心理状态和优化人机交互至关重要.
研究的目的:
- 开发和评估机器学习模型,以使用任务唤起的瞳孔反应来检测认知事件.
- 为了比较一般化和特定任务模型在识别不同认知领域的认知事件的性能.
- 调查瞳孔直径和凝视位置对认知事件检测的贡献.
主要方法:
- 以二进制分类问题为框架,使用1秒段的瞳孔直径和凝视位置数据检测认知事件.
- 在警,情绪处理,数值推理和短期记忆任务中训练了一种通用模型和四种特定任务模型.
- 利用换功能重要性分析来理解模型预测,并使用马修斯相关系数 (MCC) 评估性能.
主要成果:
- 五个模型实现了马修斯相关系数 (MCC) 从0.43到0.75.
- 与特定任务模型相比,通用模型显示了特异性和灵敏性之间的权衡.
- 瞳孔膨胀和凝视位置都是显著的预测因素,模型利用特定任务的模式或一般人类的认知反应.
结论:
- 应用于瞳孔信号的机器学习模型显示了对快速和个性化检测认知事件的巨大潜力.
- 该研究强调了整合瞳孔直径和目光数据的实用性,以进行强大的认知状态监测.
- 研究结果表明,通用模型可以有效地捕捉人类的认知反应,为认知事件检测提供了一种多功能方法.
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