Dang Quang, Kucukosmanoglu Murat, Anoruo Michael, Kargosha Golshan, Conklin Sarah, Brooks Justin
Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, 21250, USA.
D-Prime LLC, McLean, VA, 22101, USA.
Sci Rep. 2025 Aug 20;15(1):30506. doi: 10.1038/s41598-025-16165-4.
The pupillary response is a valuable indicator of cognitive workload, capturing fluctuations in attention and arousal governed by the autonomic nervous system. Cognitive events, defined as the initiation of mental processes, are closely linked to cognitive workload as they trigger cognitive responses. In this study, we detect cognitive events for the task-evoked pupillary response across four domains (vigilance, emotion processing, numerical reasoning, and short-term memory). The problem is framed as a binary classification. We train one generalized model and four task-specific models on 1-s pupil diameter and gaze position segments. Five models achieve MCC between 0.43 and 0.75. We report three key findings: (1) the generalized model reduces the specificity to enhance the sensitivity, illustrating the trade-off from specialization to generalization; (2) the permutation feature importance analyses show that both pupil dilation and gaze position contribute to model predictions, with task-specific models focusing on task-specific structure patterns to predict while the generalized model is using human cognitive responses; and (3) in an online simulation environment, models performance decreases by approximately 0.05 on MCC. The findings highlight the potential of machine learning applied to pupillary signals for rapid, individualized detection of cognitive events.
瞳孔反应是认知工作量的一个重要指标，它能捕捉由自主神经系统控制的注意力和唤醒水平的波动。认知事件被定义为心理过程的启动，由于它们引发认知反应，所以与认知工作量密切相关。在本研究中，我们针对任务诱发的瞳孔反应在四个领域（警觉、情绪处理、数字推理和短期记忆）检测认知事件。该问题被构建为一个二元分类问题。我们在1秒的瞳孔直径和注视位置片段上训练一个通用模型和四个特定任务模型。五个模型的马修斯相关系数（MCC）在0.43至0.75之间。我们报告了三个关键发现：（1）通用模型降低了特异性以提高敏感性，说明了从专业化到泛化的权衡；（2）排列特征重要性分析表明，瞳孔扩张和注视位置都对模型预测有贡献，特定任务模型专注于特定任务的结构模式进行预测，而通用模型则利用人类认知反应；（3）在在线模拟环境中，模型的MCC性能下降约0.05。这些发现突出了将机器学习应用于瞳孔信号以快速、个性化检测认知事件的潜力。
