Physiological and Behavioral Modeling of Stress and Cognitive Load in Web-Based Question Answering

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 demonstrates the feasibility of rapidly detecting cognitive-affective states in digital environments, paving the way for more adaptive, ethical, and user-aware survey interfaces.