Attention-Driven Rendering And Interface Adaptation Using Eye-Tracking Data

This study investigates computational attention mechanisms in digital luxury interfaces using eye-tracking and predictive modeling. Across three experiments (N = 180), ocular biometric measures, including fixation duration, saccadic frequency, and fixation density, were analyzed during exposure to high-fidelity fashion stimuli containing aesthetic and brand-specific elements. Results showed that aesthetic fixations were the strongest predictors of brand attention, accounting for 40.3% of variance in brand engagement, while materialism dimensions selectively influenced attention toward symbolic status cues. The findings support a gaze-driven Human–Computer Interaction (HCI) framework in which aesthetic salience functions as a primary attentional signal within visually dense digital environments. From a systems-design perspective, the study proposes an attention-adaptive interface model integrating real-time ocular metrics into rendering and layout optimization processes. The research contributes a computational framework linking biometric attention sensing with adaptive interface engineering for mobile commerce, social media, and immersive digital environments.