用眼动与心率早期预测用户表现，揭示高绩效者生理特征。
Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity
- 融合眼动与心率信号进行早期性能预测
- 眼动模型准确率达0.86，高绩效者更专注且心率稳定
- 适合人机交互、认知负荷研究与主动干预场景
用户表现对交互系统至关重要，提前预测表现可及时识别任务困难者。尽管眼动与心率信号广泛用于表征视觉行为和生理激活，其在早期预测及揭示表现差异机制方面的潜力仍待挖掘。本研究在具有自然复杂度演进的游戏环境中开展被试内实验，利用早期眼动与心率信号预测后续表现，并分析生理与自评组间差异。结果表明，眼动-心率融合模型平衡准确率达0.86，仅眼动模型也表现出相当预测能力。高绩效者呈现聚焦注视与调整后的视觉采样，在任务需求增强时维持更稳定的心率激活，且具有更积极的情感体验。研究证明从早期生理信号实现跨会话预测的可行性，为表现差异提供可解释洞见，助力未来主动干预。
原文摘要 · Abstract (English)
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.
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