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

2026-01-25

AI Summary

This study addresses how time pressure and item difficulty in web-based questionnaires can induce stress and cognitive overload, thereby compromising data quality and user experience. Traditional low-frequency self-report methods are insufficient for capturing dynamic state changes during brief tasks. To overcome this limitation, the authors conducted a 2×2 within-subjects experiment manipulating these factors while simultaneously collecting multimodal physiological and behavioral data—including eye-tracking, electrocardiography (ECG), electrodermal activity (EDA), and mouse trajectories. By integrating statistical modeling with machine learning techniques, they identified distinct, short-latency patterns of physiological and behavioral responses that reliably differentiate cognitive-affective states. The findings demonstrate the feasibility of real-time detection of such states in digital environments and provide critical technical foundations for developing adaptive, user-aware online questionnaire systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• stress
• cognitive load
• web-based surveys
• physiological modeling
• behavioral modeling

Innovation

• multimodal sensing
• cognitive load detection
• stress modeling
• mouse dynamics
• adaptive surveys