A First Step toward the Understanding of Implicit Learning of Hazard Anticipation in Inexperienced Road Users Through a Moped-Riding Simulator

摘要

Hazard perception is considered one of the most important abilities in road safety.\udSeveral efforts have been devoted to investigating how it improves with experience\udand can be trained. Recently, research has focused on the implicit aspects of hazard\uddetection, reaction, and anticipation. In the present study, we attempted to understand\udhow the ability to anticipate hazards develops during training with a moped-riding\udsimulator: the Honda Riding Trainer (HRT). Several studies have already validated the\udHRT as a tool to enhance adolescents’ hazard perception and riding abilities. In the\udpresent study, as an index of hazard anticipation, we used skin conductance response\ud(SCR), which has been demonstrated to be linked to affective/implicit appraisal of risk.\udWe administered to a group of inexperienced road users five road courses two times a\udweek apart. In each course, participants had to deal with eight hazard scenes (except\udone course that included only seven hazard scenes). Participants had to ride along\udthe HRT courses, facing the potentially hazardous situations, following traffic rules, and\udtrying to avoid accidents. During the task, we measured SCR and monitored driving\udperformance. The main results show that learning to ride the simulator leads to both a\udreduction in the number of accidents and anticipation of the somatic response related\udto hazard detection, as proven by the reduction of SCR onset recorded in the second\udsession. The finding that the SCR signaling the impending hazard appears earlier when\udthe already encountered hazard situations are faced anew suggests that training with\udthe simulator acts on the somatic activation associated with the experience of risky\udsituations, improving its effectiveness in detecting hazards in advance so as to avoid\udaccidents. This represents the starting point for future investigations into the process of\udgeneralization of learning acquired in new virtual situations and in real-road situations.