Time pressure and question difficulty can trigger stress and cognitive overload in web-based surveys, compromising data quality and user experience. Most stress detection methods are based on low-resolution self-reports, which are poorly suited for capturing fast, moment-to-moment changes during short online tasks. Addressing this gap, we conducted a 2x2 within-subjects study (N = 29), manipulating question difficulty and time pressure in a web-based multiple-choice task. Participants completed general knowledge and cognitive questions while we collected multimodal data: mouse dynamics, eye tracking, electrocardiogram, and electrodermal activity. Using condition-based and self-reported labels, we used statistical and machine learning models to model stress and question difficulty. Our results show distinct physiological and behavioral patterns within very short timeframes. This work demonstrates the feasibility of rapidly detecting cognitive-affective states in digital environments, paving the way for more adaptive, ethical, and user-aware survey interfaces.

翻译：时间压力和问题难度可能引发网络调查中的压力与认知过载，从而损害数据质量和用户体验。现有压力检测方法多基于低分辨率的自我报告，难以捕捉短期在线任务中快速、瞬时的状态变化。为填补这一空白，我们开展了一项2×2被试内设计研究（N=29），在网络选择题任务中操纵问题难度与时间压力。参与者在完成常识与认知问题的同时，我们采集了多模态数据：鼠标动力学、眼动追踪、心电图及皮肤电活动。利用基于实验条件和自我报告的标签，我们采用统计与机器学习模型对压力与问题难度进行建模。研究结果表明，在极短时间窗内存在可区分的生理与行为模式。本工作验证了在数字环境中快速检测认知情感状态的可行性，为开发更具适应性、符合伦理且具备用户感知能力的调查界面奠定了基础。