Zhang Xiaoying, Chang Ruosong, Sui Xue
Department of Psychology, Guangzhou University, Guangzhou, China.
The School of Psychology, Liaoning Normal University, Dalian, China.
BMC Psychol. 2025 Mar 8;13(1):216. doi: 10.1186/s40359-025-02555-w.
This study investigates the neural mechanisms underlying the inhibitory control of speed when drivers encounter varying levels of risk posed by pedestrians and motor vehicles. Two variables (risk level and risk type) were controlled in this study. The experimental materials included traffic images depicting pedestrians or motor vehicles, each associated with different risk levels. Drivers were presented with these images and tasked with adjusting their vehicle speed according to the traffic scenario. Specifically, they were instructed to either maintain their current speed or decelerate as needed. Electroencephalograms (EEGs) responses were simultaneously recorded. Results showed that in low-risk scenarios, the deceleration score was significantly higher for pedestrian risks than for motor vehicle risks. Under conditions of elevated risk, various risk types did not result in significant variations in deceleration scores. EEG data revealed that high-risk scenarios elicited a larger amplitude in the P3 component compared to low-risk scenarios. Additionally, the average amplitude of the N2 component was greater for pedestrian risks than for motor vehicle risks. These findings suggest that risk level and type do not act as independent factors influencing speed control. Specifically, when the risk originates from pedestrians, drivers tend to reduce their speed even when the risk level is low, in order to mitigate potential hazards and prioritize safety. Furthermore, high-risk situations elicit a more pronounced brain response and demand greater attentional resources compared to low-risk situations. This study provides valuable insights for establishing speed limits based on different sources of risk in driving scenarios.
本研究调查了驾驶员在遇到行人及机动车带来的不同程度风险时，对速度进行抑制控制的神经机制。本研究控制了两个变量（风险水平和风险类型）。实验材料包括描绘行人或机动车的交通图像，每个图像都与不同的风险水平相关。向驾驶员展示这些图像，并要求他们根据交通场景调整车速。具体来说，他们被指示要么保持当前速度，要么根据需要减速。同时记录脑电图（EEG）反应。结果表明，在低风险场景中，行人风险的减速得分显著高于机动车风险。在风险升高的情况下，各种风险类型的减速得分没有显著差异。EEG数据显示，与低风险场景相比，高风险场景在P3成分中引发的振幅更大。此外，行人风险的N2成分平均振幅大于机动车风险。这些发现表明，风险水平和类型并非影响速度控制的独立因素。具体而言，当风险来自行人时，即使风险水平较低，驾驶员也倾向于降低车速，以减轻潜在危害并将安全置于首位。此外，与低风险情况相比，高风险情况会引发更明显的大脑反应，并需要更多的注意力资源。本研究为基于驾驶场景中不同风险源制定速度限制提供了有价值的见解。
