眼动轨迹能提前1秒预测图像偏好与判断信心
Gaze patterns predict preference and confidence in pairwise AI image evaluation
- 通过眼动追踪分析人类在对比AI图像时的注视模式
- 注视时间、注视次数和回看次数可预测选择结果（准确率68%）
- 眼动切换频率区分高自信与低自信决策（准确率66%）
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）依赖成对的人类判断，但人们对这些判断背后的认知过程了解甚少。我们研究了眼动追踪是否能在成对AI生成图像评估中揭示偏好形成过程。30名参与者完成1800次试验并记录眼动数据。我们复现了注视级联效应：在决策前约1秒，目光会转向被选中的图像。级联动态在不同信心水平下保持一致。注视特征可预测二元选择（准确率68%），被选图像获得更长注视时间、更多注视点和回看次数。注视转移可区分高信心与不确定决策（准确率66%），低信心试次每秒图像切换次数更高。结果表明，眼动模式能预测成对图像评估中的选择与信心，提示眼动提供了偏好标注质量相关的隐式信号。
原文摘要 · Abstract (English)
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.
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