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.

翻译：用户表现是交互系统中的关键因素，它反映了用户有效执行任务的程度。前瞻性地预测表现能够及时识别在任务要求中挣扎的用户。虽然眼动和心电信号被广泛用于表征与表现相关的视觉行为和生理激活，但它们在早期预测以及揭示表现差异背后生理机制方面的潜力仍未得到充分探索。我们在一个具有自然展开复杂度的游戏环境中开展了被试内实验，利用早期眼动和心电信号来预测后续表现，并考察生理与自我报告的组间差异。结果表明，眼动-心电融合模型达到了0.86的平衡准确率，而仅基于眼动的模型也展现了相当的预测能力。表现优异的参与者表现出定向注视和调整视觉采样，在任务需求增强时维持更稳定的心电激活，并伴有更积极的情感体验。这些发现证明了从早期生理信号进行跨会话预测的可行性，提供了关于表现变化的可解释性洞见，并为未来主动干预奠定了基础。