在不同阶段的知觉和认知信息处理中,识别和评估心理工作量,使用多式联络方法
Haizhe Jin1, Lin Zhu1, Mingming Li2
1Department of Industrial Engineering, School of Business Administration, Northeastern University, Shenyang, China.
Ergonomics
|June 8, 2023
概括
这项研究揭示了感知和认知负载如何使用多式联络指标影响心理工作负载. 一个k-最近邻近算法准确地分类了工作负载状态,在苛刻的操作中实现了优化任务分配.
科学领域:
- 认知神经科学 认知神经科学
- 人与计算机的交互
- 心理生理学 心理生理学
背景情况:
- 心理工作量评估对于优化绩效和防止高要求任务中的错误至关重要.
- 了解感知负载 (P-load) 和认知负载 (C-load) 之间的相互作用是准确工作负载监控的关键.
研究的目的:
- 研究P负荷和C负荷对各种心理生理学指标的心理工作负荷的影响.
- 开发一种多式调节分类方法,用于识别不同的心理工作负载状态.
主要方法:
- 利用与事件相关的潜力 (ERP) 和眼动指标 (眼频率,瞳孔直径,眼持续时间) 与NASA-TLX和任务性能一起.
- 应用反复测量ANOVA来分析指标对P负荷和C负荷的敏感性.
- 开发了一个k-最近邻居 (KNN) 算法用于工作负载状态分类.
主要成果:
- 特定的ERP振幅 (P1,N1,N2,P3) 显示在大脑区域对P负荷和C负荷的敏感性差异.
- 眼动指标,包括眼频率,瞳孔直径和眼持续时间,反映了P负荷和C负荷.
- 基于KNN的分类实现了97.89%的高准确性,用于区分四个心理工作负载状态.
结论:
- 多模式指标为评估受感知和认知需求影响的心理工作量提供了坚实的基础.
- 开发的KNN分类方法为实时心理工作负载监控提供了一个精确的工具.
- 研究结果支持在高工作负载环境中优化任务分配和人机交互设计.
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