Gaze patterns predict preference and confidence in pairwise AI image evaluation
This provides insights into preference formation for researchers in human feedback methods like RLHF and DPO, though it is incremental as it applies existing eye-tracking techniques to a new context.
The study tackled the problem of understanding cognitive processes in pairwise human judgments for AI image evaluation by using eye-tracking, finding that gaze patterns predicted binary choice with 68% accuracy and distinguished confidence levels with 66% accuracy.
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.