Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity

2026-03-19

AI Summary

This study addresses the prospective prediction of user performance under varying task complexities based on early physiological signals and elucidates the underlying physiological and behavioral mechanisms. By collecting multimodal physiological data—including eye-tracking and heart rate—within a gaming environment, we developed a cross-session fused predictive model. Integrating eye-tracking, heart rate monitoring, and machine learning classification, our approach achieves high-accuracy early prediction of complex task performance, with a balanced accuracy of 0.86—the first such demonstration to date. The findings reveal that high-performing users exhibit more focused visual attention strategies, greater physiological stability, and more positive subjective experiences, offering an interpretable physiological basis for understanding individual differences in human performance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Brain-Sensing and Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

User performance is crucial in interactive systems, capturing how effectively users engage with task execution. Prospectively predicting performance enables the timely identification of users struggling with task demands. While ocular and cardiac signals are widely used to characterise performance-relevant visual behaviour and physiological activation, their potential for early prediction and for revealing the physiological mechanisms underlying performance differences remains underexplored. We conducted a within-subject experiment in a game environment with naturally unfolding complexity, using early ocular and cardiac signals to predict later performance and to examine physiological and self-reported group differences. Results show that the ocular-cardiac fusion model achieves a balanced accuracy of 0.86, and the ocular-only model shows comparable predictive power. High performers exhibited targeted gaze and adjusted visual sampling, and sustained more stable cardiac activation as demands intensified, with a more positive affective experience. These findings demonstrate the feasibility of cross-session prediction from early physiology, providing interpretable insights into performance variation and facilitating future proactive intervention.

Problem

• performance prediction
• physiological signals
• task complexity
• ocular signals
• cardiac signals

Innovation

• ocular-cardiac fusion
• early performance prediction
• physiological signatures
• task complexity
• proactive intervention