Discriminating between multiple driving events using physiological signals remains challenging. This study examined whether gaze dynamics and pupil responses could distinguish multiple driving events and reveal age-related physiological patterns. Twenty-seven participants (12 young, 15 old) completed a simulated driving scenario featuring nine events while physiological signals were recorded. Statistical analyses revealed significant event-specific modulation of gaze dispersion metrics ( = 0.571–0.582, ). Gradient boosted trees achieved 82.4% accuracy (95% CI [74.3%, 89.8%]) classifying four events (Bicycle, DeerAlert, LowGas, StopAtGasStation) using 7 gaze-based features, with SHAP analysis identifying horizontal deviation standard deviation and median as primary discriminators. Time-series motif discovery revealed consistent gaze motif amplitudes (3.0–3.4 z-scores) across events, while pupil motifs showed selective enhancement during monitoring tasks (3.1–3.8 z-scores). Age-stratified analyses uncovered distinct physiological patterns: younger drivers exhibited greater gaze variability with broader scanning patterns, whereas older drivers demonstrated elevated pupil motif amplitudes (particularly during LowGas: 3.88 vs. 3.18 z-scores) alongside more constrained visual exploration. The convergence of statistical, machine learning, and motif discovery approaches establishes gaze dynamics as sufficient for multiclass event discrimination in simulated driving. These findings demonstrate that gaze-based features alone can reliably distinguish between specific driving events, while motif analysis reveals temporal dynamics and age-related patterns that warrant further investigation in larger samples, providing a foundation for interpretable driver monitoring systems.

Abstract The shift toward fragmented and ubiquitous use of multimedia poses several challenges to our understanding and assessment of multimedia exposure and its effects. This article focuses on multimedia advertising exposure and its impact on consumer behavior. It presents the development of Multimedia Ad Exposure Scale (MMAES) – an instrument designed to measure short-term effects of online multimedia ad exposure in terms of engagement, psychological reactance, awareness and attitude, and purchase intention. The main research challenge has been to identify core dimensions that can reliably measure such exposure, particularly in the context of ad-supported video streaming. The development of MMAES is presented through its conceptualization, operationalization, and an observational study conducted via crowdsourcing. The target group is young adults (ages 18-24, N = 360), digital natives who engage with ad-supported video streaming more than any other user group. Exploratory factor analysis revealed a well-defined four-factor structure of MMAES. The results of the validity and reliability measures show good content and construct validity as well as good overall reliability and very good internal consistency of MMAES. Overall, the results show that MMAES is a reliable instrument for measuring the short-term effects of multimedia ad exposure and its weak ground truth.