Gaze patterns predict preference and confidence in pairwise AI image evaluation
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 ...
| Authors | Nikolas Papadopoulos, Shreenithi Navaneethan, Sheng Bai et al. |
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| Primary category | cs.HC |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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