Physiological and Behavioral Modeling of Stress and Cognitive Load in Web-Based Question Answering
Ailin Liu, Francesco Chiossi, Felix Henninger, Lisa Bondo Andersen, Tobias Wistuba, Sonja Greven, Frauke Kreuter, Fiona Draxler

TL;DR
This study demonstrates that rapid detection of stress and cognitive load in web-based tasks is feasible using multimodal physiological and behavioral data, improving adaptive survey interfaces.
Contribution
It introduces a multimodal approach combining physiological and behavioral data to detect stress and cognitive load in real-time during online tasks.
Findings
Distinct physiological and behavioral patterns identified within seconds.
Machine learning models effectively classify stress and question difficulty.
Feasibility of rapid, real-time detection of cognitive-affective states in digital environments.
Abstract
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…
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Taxonomy
TopicsSurvey Methodology and Nonresponse · Expert finding and Q&A systems · Reliability and Agreement in Measurement
