首页> 外文会议>Human Factors and Ergonomics Society international annual meeting;Human Factors and Ergonomics Society >Can Stroboscopic Training Improve Time-to-Collision Judgments of Approaching Objects?
【24h】

Can Stroboscopic Training Improve Time-to-Collision Judgments of Approaching Objects?

机译:频闪训练可以改善对接近物体的碰撞时间判断吗?

获取原文

摘要

Prior studies have shown that training with stroboscopic viewing improvedperformance on visual tasks, such as motion coherence thresholds, and performance oncoincident anticipation tasks (Appelbaum, Schroeder, Cain, & Mitroff, 2011; Smith &Mitroff, 2012). In stroboscopic viewing, individuals wear occlusion goggles whichpresent an intermittent view of the environment. It is assumed that training during“degraded” viewing will enhance subsequent performance during unimpaired viewing.We examined whether training with stroboscopic viewing can improve time-to-collision(TTC) judgments, which have importance in real-world tasks such as driving, using aprediction motion (PM) task (Schiff & Detwiler, 1979). The PM task is particularly wellsuitedfor stroboscopic training because the task involves extrapolation of the object’smotion after it disappears (DeLucia & Liddell, 1998; Schiff & Oldak, 1990). Instroboscopic viewing, the object appears and then disappears, but does so repeatedlythroughout the object’s approach. During periods of occlusion, observers putativelyextrapolate the object’s motion. When the object reappears, observers get feedback ontheir extrapolation. Thus, they get feedback on their extrapolation throughout the object’sentire approach.
机译:先前的研究表明,通过频闪观察进行训练可以改善视觉任务的性能,例如运动连贯性阈值以及重合预期任务的性能(Appelbaum,Schroeder,Cain,&Mitroff,2011; Smith&Mitroff,2012)。在频闪观察中,个体戴上遮挡护目镜,代表了间歇性的环境观察。假定“降级”观看期间的训练将增强无障碍观看期间的后续性能。我们检查了频闪观察是否可以改善碰撞时间(TTC)判断,这在诸如驾驶,使用,预测运动(PM)任务(Schiff&Detwiler,1979)。 PM任务特别适合频闪训练,因为该任务涉及物体消失后的运动推断(DeLucia&Liddell,1998; Schiff&Oldak,1990)。在频闪观测下,物体出现然后消失,但是在整个物体接近过程中重复出现。在遮挡期间,观察者假定推断物体的运动。当物体重新出现时,观察者会得到关于其外推的反馈。因此,他们在整个对象的整个方法中都得到有关其推断的反馈。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号