Recent developments in technology encourage the use of head-mounted displays (HMDs) as a medium to explore visualizations in virtual realities (VRs). VR environments (VREs) enable new, more immersive visualization design spaces compared to traditional computer screens. Previous studies in different domains, such as medicine, psychology, and geology, report a positive effect of immersion, e.g., on learning performance or phobia treatment effectiveness. Our work presented in this paper assesses the applicability of those findings to a common task from the information visualization (InfoVis) domain. We conducted a quantitative user study to investigate the impact of immersion on cluster identification tasks in scatterplot visualizations. The main experiment was carried out with 18 participants in a within-subjects setting using four different visualizations, (1) a 2D scatterplot matrix on a screen, (2) a 3D scatterplot on a screen, (3) a 3D scatterplot miniature in a VRE and (4) a fully immersive 3D scatterplot in a VRE. The four visualization design spaces vary in their level of immersion, as shown in a supplementary study. The results of our main study indicate that task performance differs between the investigated visualization design spaces in terms of accuracy, efficiency, memorability, sense of orientation, and user preference. In particular, the 2D visualization on the screen performed worse compared to the 3D visualizations with regard to the measured variables. The study shows that an increased level of immersion can be a substantial benefit in the context of 3D data and cluster detection.

中文翻译：

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沉浸对群集识别任务的影响。

技术的最新发展鼓励使用头戴式显示器（HMD）作为探索虚拟现实（VR）可视化的媒介。与传统的计算机屏幕相比，VR环境（VRE）启用了新的，更加身临其境的可视化设计空间。先前在不同领域（例如医学，心理学和地质学）的研究报告了浸入的积极作用，例如对学习成绩或恐惧症治疗效果的积极影响。本文介绍的我们的工作评估了这些发现对信息可视化（InfoVis）领域中常见任务的适用性。我们进行了定量的用户研究，以研究沉浸对散点图可视化中的群集识别任务的影响。主要实验是使用四种不同的可视化方法在对象内部设置中由18位参与者进行的，（1）屏幕上的2D散点图矩阵，（2）屏幕上的3D散点图，（3）屏幕上的3D散点图VRE和（4）VRE中的完全沉浸式3D散点图。如补充研究所示，四个可视化设计空间的沉浸度有所不同。我们的主要研究结果表明，在所调查的可视化设计空间之间，任务性能在准确性，效率，记忆力，方向感和用户偏爱方面有所不同。特别是，与3D可视化相比，屏幕上的2D可视化在测量变量方面表现较差。