Cognitive workload and anticipatory processes during pedestrian time-to-arrival estimation: Evidence from EEG in an immersive virtual reality road-crossing scenario
Identificadores
URI: http://hdl.handle.net/20.500.12020/2033DOI: http://dx.doi.org/10.2139/ssrn.6179590
Fecha
2026-02-04Tipo de documento
articleMateria/s Unesco
61 PsicologíaResumen
Time-to-arrival (TTA) estimation tasks offer valuable insight into temporal processing and cognitive–motor preparatory processes involved in pedestrian safety. Using electroencephalography (EEG) and immersive virtual reality (VR), we investigated the neural dynamics underlying pedestrian TTA estimation in a simulated road-crossing scenario. Event-related potentials (ERPs), including the contingent negative variation (CNV) and the P2 component, as well as oscillatory activity in the Alpha and Theta bands, were examined as markers of anticipation, attention, and cognitive workload. Thirty-four participants completed four experimental TTA estimation conditions defined by vehicle speed (30 vs. 50 km/h) and vehicle disappearance time (3 vs. 5 seconds). Behavioural results showed that vehicle speed significantly influenced TTA estimation accuracy, with larger estimation deviations and reduced accuracy under higher-speed conditions. At the neurophysiological level, P2 amplitude was primarily modulated by interval-timing, with larger amplitudes for longer disappearance intervals. CNV activity showed sensitivity to both urgency and temporal structure, with Early CNV larger in slower-speed and shorter-interval conditions and Late CNV enhanced for shorter intervals. Time–frequency analyses revealed early modulations of occipital Alpha and frontal-midline Theta activity as a function of task demands. Occipital Alpha was associated with behavioural accuracy, whereas frontal-midline theta varied with vehicle speed. Subjective mental workload correlated with neural measures across conditions, despite no direct association with performance. Together, these findings indicate that cognitive workload and associated neural dynamics play a central role in pedestrian TTA estimation. By integrating EEG, VR and subjective measures this study advances the understanding of pedestrian cognition in road safety research





