[2603.24849] Gaze patterns predict preference and confidence in pairwise AI image evaluation

Computer Science > Human-Computer Interaction arXiv:2603.24849 (cs) [Submitted on 25 Mar 2026] Title:Gaze patterns predict preference and confidence in pairwise AI image evaluation Authors:Nikolas Papadopoulos, Shreenithi Navaneethan, Sheng Bai, Ankur Samanta, Paul Sajda View a PDF of the paper titled Gaze patterns predict preference and confidence in pairwise AI image evaluation, by Nikolas Papadopoulos and 3 other authors View PDF HTML (experimental) 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 prov...