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

TL;DR

This study demonstrates that early ocular and cardiac signals can accurately predict human performance in complex tasks, revealing physiological mechanisms and enabling proactive interventions.

Contribution

It introduces a fusion model using ocular and cardiac signals for early performance prediction and explores physiological signatures associated with performance differences.

Findings

Ocular-cardiac fusion model achieves 86% accuracy in prediction.

High performers show targeted gaze and stable cardiac activation.

Physiological signals reveal mechanisms underlying performance variation.

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.