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Active Versus Passive Acquisition of Spatial Knowledge While Controlling a Vehicle in a Virtual Urban Space in Drivers and Non-Drivers:

机译:在驾驶员和非驱动程序中控制虚拟城市空间中的车辆时,活跃与空间知识的被动获取:

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Historically, real-world studies have indicated a spatial learning advantage for people who actively explore the environment they inhabit as opposed to those whose experience is more passive. A common contrast is made between the spatial learning of car drivers and passengers. However, compared with walking and other forms of transportation, car-driving experience per se has a special status. An experiment was conducted to explore the dual hypotheses that active explorers learn more about the layout of a virtual environment (VE) than passive observers and that real-world car drivers will learn more regardless of their experimental active/passive status. Participants explored a virtual model of a small town in active/passive, pairs. Active exploration was self-directed and goal driven, and all learning tasks were explicit. Consistent with many earlier studies in VEs, there was no benefit from activity (controlling exploration/movement), arguably because input control competes with spatial information acquisition. When participants were divided according to whether they were licensed drivers or not, the results showed that drivers were significantly more accurate than non-drivers at indicating the positions of target locations on a map, in both the active and passive conditions. An interaction showed that in the active condition, drivers had significantly better route scores than non-drivers, and better than drivers in the passive condition. Driving may therefore be beneficial for spatial abilities over and above the general benefits of a??activitya?? and when spatial skills are examined in VEs, driver experience is an important criterion that should be taken into account.
机译:从历史上看,真实世界的研究表明,对于积极探索他们居住的环境而不是那些经历更具被动的人来表示空间学习优势。在汽车司机和乘客的空间学习之间进行了共同的对比。然而,与行走和其他形式的运输相比,汽车驾驶经验本身具有特殊状态。进行了一个实验,探讨了活跃探险者比被动观察者更多地了解有关虚拟环境(VE)的更多关于虚拟环境的布局的双假设,并且无论其实验性主动/被动状态如何,都会学到更多内容。参与者探索了一个主动/被动,对的一个小镇的虚拟模型。积极的探索是自我导向的,目标驱动,所有学习任务都是明确的。与ves中的许多早期研究一致,活动(控制探索/运动)没有受益,可以说是因为输入控制与空间信息采集竞争。当参与者根据他们是否被许可的司机而分开时,结果表明,在主动和被动条件下,驾驶员比在地图上的目标位置的位置明显比非司机更准确。互动显示,在积极的情况下,驱动程序比非驱动程序的路线分数明显更好,而且比被动条件下的驱动器更好。因此,驾驶可能对空间能力和高于a的普遍益处是有益的??当在VES中审查空间技能时,驾驶员经验是应考虑的重要标准。

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