Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity
This work addresses the need for timely identification of struggling users in interactive systems, though it is incremental in applying existing physiological signals to a new context.
The study tackled the problem of predicting human performance in interactive systems by using early ocular and cardiac signals, achieving a balanced accuracy of 0.86 with a fusion model and revealing that high performers show targeted gaze and stable cardiac activation as task demands increase.
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.