Gaze patterns predict preference and confidence in pairwise AI image evaluation

arXiv cs.AI / 3/27/2026

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Key Points

研究は、RLHFやDPOのような「ペアワイズの嗜好学習」が、人の判断過程でどのように形成されるかをアイ・トラッキングで解明しようとしています。
30人の参加者が1800試行でペアのAI生成画像を評価し、選好が決まる約1秒前に「選ばれた画像へ視線が移る」ガゼ・カスケード効果が再現されました。
視線特徴は2値の選択を予測でき、選ばれた画像は滞在時間・注視・再注視が多い一方で、選好の形成に視線が関与していることが示唆されます。
さらに、視線遷移は高確信判断と不確実判断を識別でき、低確信では画像スイッチ頻度が高いことが報告されています。
これらより、アイ・トラッキングは選択だけでなく確信度の「暗黙シグナル」を提供し、より良い嗜好アノテーション品質に関係し得ると結論づけています。

Abstract

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.