用机器学习从瞳孔数据自动识别认知事件，提升人机协同效率。
Auto Detecting Cognitive Events Using Machine Learning on Pupillary Data
- 基于1秒瞳孔数据，用CNN模型做刺激出现的二分类检测。
- 不同任务下准确率（马修相关系数）达0.47至0.80。
- 适用于个性化学习与神经认知负荷实时管理场景。
评估认知负荷对人类表现至关重要，因其影响信息处理、决策和任务执行。瞳孔大小是认知负荷的重要指标，反映自主神经系统调控下的注意力与唤醒水平。认知事件与认知负荷密切相关，会激活心理过程并引发认知反应。本研究探索利用机器学习自动检测个体经历的认知事件。将问题建模为二分类任务，聚焦于四类认知任务中刺激出现时刻的检测，使用卷积神经网络（CNN）模型分析1秒瞳孔数据。结果以马修相关系数衡量，范围在0.47至0.80之间，具体取决于任务类型。本文讨论了泛化与专精之间的权衡、模型对未见刺激时间的响应行为、不同任务间的结构差异、影响预测的因素及实时仿真表现。研究结果表明，基于瞳孔与眼动反应的机器学习方法在认知事件检测方面具有潜力，有助于推动个性化学习和神经认知负荷管理优化。
原文摘要 · Abstract (English)
Assessing cognitive workload is crucial for human performance as it affects information processing, decision making, and task execution. Pupil size is a valuable indicator of cognitive workload, reflecting changes in attention and arousal governed by the autonomic nervous system. Cognitive events are closely linked to cognitive workload as they activate mental processes and trigger cognitive responses. This study explores the potential of using machine learning to automatically detect cognitive events experienced using individuals. We framed the problem as a binary classification task, focusing on detecting stimulus onset across four cognitive tasks using CNN models and 1-second pupillary data. The results, measured by Matthew's correlation coefficient, ranged from 0.47 to 0.80, depending on the cognitive task. This paper discusses the trade-offs between generalization and specialization, model behavior when encountering unseen stimulus onset times, structural variances among cognitive tasks, factors influencing model predictions, and real-time simulation. These findings highlight the potential of machine learning techniques in detecting cognitive events based on pupil and eye movement responses, contributing to advancements in personalized learning and optimizing neurocognitive workload management.
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