# Physiological and Behavioral Modeling of Stress and Cognitive Load in Web-Based Question Answering Source: [Physiological and Behavioral Modeling of Stress and Cognitive Load in Web-Based Question Answering]() Collection index: [llms.txt]() Data updated: 2026-01-28T05:00:19.604Z Authors: Ailin Liu, Francesco Chiossi, Felix Henninger, Lisa Bondo Andersen, Tobias Wistuba, Sonja Greven, Frauke Kreuter, Fiona Draxler Published: 2026-01-25T15:59:15.000Z ## 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 demonstrates the feasibility of rapidly detecting cognitive-affective states in digital environments, paving the way for more adaptive, ethical, and user-aware survey interfaces. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()