Convolution-Based Modeling of Pupil Dynamics: Integrating Luminance for Enhanced Arousal Prediction ETRA007

Pupil size is a key eye-based indicator of mental processing and internal states. Nonetheless, pupil size is also influenced by light, causing challenges for monitoring internal states with eye tracking in dynamic and naturalistic settings. We investigated how convolution-based modeling can capture the influence of luminance and emotional arousal on pupil size, and conversely, how pupil dynamics inform arousal prediction. In a lab study, we analyzed data collected from 19 participants who watched travel-themed videos with different arousal levels and their pixelated counterparts. We present and evaluate a convolution-based approach for pupil size modeling that integrates low-level visual features and higher-order emotional factors, as previous work has shown effectiveness of such methods in modeling pupil light reflex. Our results show that incorporating luminance and contrast indeed enhances arousal prediction from pupil data, although performance varies due to different user behavior and stimuli.