Preference learning methods, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on pairwise human judgments, yet little is known about the cognitive processes underlying these judgments. We investigate whether eye-tracking can reveal preference formation during pairwise AI-generated image evaluation. Thirty participants completed 1,800 trials while their gaze was recorded. We replicated the gaze cascade effect, with gaze shifting toward chosen images approximately one second before the decision. Cascade dynamics were consistent across confidence levels. Gaze features predicted binary choice (68% accuracy), with chosen images receiving more dwell time, fixations, and revisits. Gaze transitions distinguished high-confidence from uncertain decisions (66% accuracy), with low-confidence trials showing more image switches per second. These results show that gaze patterns predict both choice and confidence in pairwise image evaluations, suggesting that eye-tracking provides implicit signals relevant to the quality of preference annotations.

翻译：偏好学习方法，如基于人类反馈的强化学习（RLHF）和直接偏好优化（DPO），依赖于成对的人类判断，然而对这些判断背后的认知过程知之甚少。我们探究眼动追踪能否揭示成对AI生成图像评估过程中的偏好形成。三十名参与者完成了1800次试验，同时记录其注视行为。我们复现了注视级联效应，即在决策前约一秒钟，注视方向转向所选图像。级联动态在不同置信水平下保持一致。注视特征可预测二元选择（准确率68%），所选图像获得更长的注视时间、更多注视次数和回访次数。注视转换能区分高置信度与不确定决策（准确率66%），低置信度试验中每秒图像切换次数更多。这些结果表明，注视模式可预测成对图像评估中的选择与置信度，提示眼动追踪能提供与偏好标注质量相关的内隐信号。