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Virtual environments as memory training devices in navigational tasks for older adults

机译:虚拟环境作为老年人导航任务中的记忆训练设备

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摘要

Cognitive training approaches using virtual environments (VEs) might counter age-related visuospatial memory decline and associated difficulties in wayfinding. However, the effects of the visual design of a VE in route learning are not fully understood. Therefore, we created a custom-designed VE optimized for route learning, with adjusted levels of realism and highlighted landmark locations (MixedVE). Herein we tested participants’ route recall performance in identifying direction of turn at the intersection with this MixedVE against two baseline alternatives (AbstractVE, RealisticVE). An older vs. a younger group solved the tasks in two stages (immediate vs. delayed recall by one week). Our results demonstrate that the MixedVE facilitates better recall accuracy than the other two VEs for both age groups. Importantly, this pattern persists a week later. Additionally, our older participants were mostly overconfident in their route recall performance, but the MixedVE moderated this potentially detrimental overconfidence. Before the experiment, participants clearly preferred the RealisticVE, whereas after the experiment, most of the younger, and many of the older participants, preferred the MixedVE. Taken together, our findings provide insights into the importance of tailoring visualization design in route learning with VEs. Furthermore, we demonstrate the great potential of the MixedVE and by extension, of similar VEs as memory training devices for route learning, especially for older participants.
机译:使用虚拟环境(VE)的认知训练方法可以应对与年龄相关的视觉空间记忆衰退以及在寻路中的相关困难。但是,尚未完全了解VE的视觉设计在路线学习中的作用。因此,我们创建了针对路线学习优化的定制设计的VE,具有调整后的真实度和突出显示的地标位置(MixedVE)。在此,我们测试了参与者的路线召回性能,以针对两种基准替代方案(AbstractVE,realisticVE)确定了与此MixVE相交处的转弯方向。年龄较大的组和年龄较小的组分两个阶段解决任务(立即召回与延迟召回一个星期)。我们的结果表明,对于两个年龄组,MixedVE都比其他两个VE具有更好的召回准确性。重要的是,这种模式在一周后仍然存在。此外,我们的年长参与者大多对自己的路线召回表现过于自信,但是MixedVE缓解了这种潜在的有害过度自信。在实验之前,参与者显然更喜欢realisticVE,而在实验之后,大多数年轻参与者和许多年长参与者则更喜欢混合VE。综上所述,我们的发现提供了洞察在使用VE进行路线学习中定制可视化设计的重要性的见解。此外,我们展示了MixVE的广泛潜力,并通过扩展证明,类似的VE作为记忆训练设备,可以用于路线学习,特别是对于老年参与者。

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