使用心电图测量估计认知负载:人类-人工智能协作任务
Xiangyi Lyu1, Jun Wu2, Zhigang Ma3
1School of Economics and Management, Jiangsu University of Science and Technology.
Journal of visualized experiments : JoVE
|December 22, 2025
概括
这项研究使用了心电图 (ECG) 数据来客观地测量人类-人工智能合作期间的认知负载. 研究结果揭示了任务难度和授权权如何实时影响认知负载.
科学领域:
- 人与计算机的交互
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 认知负载显著影响工作场所的性能和安全.
- 实时,对认知负载的客观测量对于人类-AI合作至关重要.
- 传统的认知负载评估方法 (如问卷) 缺乏实时准确性.
研究的目的:
- 通过使用生理学数据,研究人类-人工智能合作期间的认知负载动态.
- 检查任务难度和授权权如何影响认知负载.
- 在人类-人工智能合作期间探索内部状态动态.
主要方法:
- 实施了可泛化的人类-人工智能协作任务,难度不同.
- 参与者可以选择自己完成任务或将任务委托给AI.
- 收集了心电图 (ECG) 数据,以进行持续,客观的认知负载监测.
主要成果:
- 与传统方法相比,生理信号,特别是心电图,提供了更客观的实时认知负载测量方法.
- 任务难度和将任务委托给AI的权力会影响参与者的认知负载.
- 在合作期间,通过生理数据确定了不同的内部状态动态.
结论:
- 电脑心电图数据可以有效地跟踪人类-人工智能协作任务中的认知负载波动.
- 了解这些动态对于优化人类-人工智能协作模型至关重要.
- 通过利用人类和人工智能的优势,研究结果支持提高效率和资源整合.
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