感知
撞车
危害
心理学
驾驶模拟器
视频反馈
毒物控制
心理干预
应用心理学
危险系数
计算机科学
模拟
社会心理学
医学
统计
置信区间
数学
医疗急救
物理
神经科学
有机化学
光学
化学
精神科
程序设计语言
作者
Mark S. Horswill,Megan Garth,Andrew Hill,Marcus Watson
标识
DOI:10.1016/j.aap.2017.02.009
摘要
Drivers' hazard perception ability has been found to predict crash risk, and novice drivers appear to be particularly poor at this skill. This competency appears to develop only slowly with experience, and this could partially be a result of poor quality performance feedback. We report an experiment in which we provided high-quality artificial feedback on individual drivers' performance in a validated video-based hazard perception test via either: (1) a graph-based comparison of hazard perception response times between the test-taker, the average driver, and an expert driver; (2) a video-based comparison between the same groups; or (3) both. All three types of feedback resulted in both an improvement in hazard perception performance and a reduction in self-rated hazard perception skill, compared with a no-feedback control group. Video-based and graph-based feedback combined resulted in a greater improvement in hazard perception performance than either of the individual components, which did not differ from one another. All three types of feedback eliminated participants' self-enhancement bias for hazard perception skill. Participants judged both interventions involving video feedback to be significantly more likely to improve their real-world driving than the no feedback control group. While all three forms of feedback had some value, the combined video and graph feedback intervention appeared to be the most effective across all outcome measures.
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