Laboratoire Perceptions, Interactions, Comportements and Simulations des usagers de la route, COSYS-PICS-L, Université Gustave Eiffel, 14-20 Boulevard Newton, Cité Descartes Champs sur Marne, 77454, Marne la Vallée Cedex 2, France.
Département Neurosciences et Sciences Cognitives, Institut de Recherche Biomédicale des Armées, Brétigny-sur-Orge, France.
Sci Rep. 2024 Nov 22;14(1):28991. doi: 10.1038/s41598-024-79392-1.
Studies have shown that adaptation to a virtual reality driving simulator takes time and that individuals differ widely in the time they need to adapt. The present study examined the relationship between attentional capacity and driving-simulator adaptation, with the hypothesis that individuals with better attentional capacity would exhibit more efficient adaptation to novel virtual driving circumstances. To this end, participants were asked to steer in a driving simulator through a series of 100 bends while keeping within a central demarcated zone. Adaptation was assessed from changes in steering behavior (steering performance: time spent within the zone, steering stability, steering reversal rate) over the course of the bends. Attentional capacity was assessed with two dynamic visual attention tasks (Multiple Object Tracking, MOT; Multiple Object Avoidance, MOA). Results showed effective adaptation to the simulator with repetition, as all steering-behavior variables improved. Both MOT and MOA scores significantly predicted adaptation, with MOT being a stronger predictor. Further analyses revealed that higher-capacity participants, but not their lower-capacity counterparts, produced more low-amplitude steering-wheel corrections early in the task, resulting in finer vehicle control and better performance later on. These findings provide new insights into adaptation to virtual reality simulators through the lens of attentional capacity.
研究表明，适应虚拟现实驾驶模拟器需要时间，并且个体在适应所需的时间上存在很大差异。本研究考察了注意力容量与驾驶模拟器适应之间的关系，假设具有更好注意力容量的个体将表现出更有效的适应新的虚拟驾驶环境。为此，要求参与者在驾驶模拟器中通过一系列 100 个弯道，同时保持在中央划定的区域内。通过在弯道过程中转向行为（转向性能：区域内花费的时间、转向稳定性、转向反转率）的变化来评估适应情况。注意力容量通过两个动态视觉注意力任务（多目标跟踪，MOT；多目标回避，MOA）进行评估。结果表明，随着重复的进行，对模拟器的适应有效，所有转向行为变量都得到了改善。MOT 和 MOA 分数都显著预测了适应，其中 MOT 的预测能力更强。进一步的分析表明，高能力的参与者，而不是低能力的参与者，在任务的早期阶段产生更多低幅度的方向盘修正，从而在后期实现更精细的车辆控制和更好的性能。这些发现通过注意力容量的视角为虚拟现实模拟器的适应提供了新的见解。
