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Abstract：
We compared the performance of two smooth-pursuit-based object selection algorithms in Virtual Reality (VR). To assess the best algorithm for a range of configurations, we systematically varied the number of targets to choose from, their distance, and their movement pattern (linear and circular). Performance was operationalized as the ratio of hits, misses and non-detections. Averaged over all distances, the correlation-based algorithm performed better for circular movement patterns compared to linear ones (F(1,11) = 24.27, p < .001, η² = .29). This was not found for the difference-based algorithm (F(1,11) = 0.98, p = .344, η² = .01). Both algorithms performed better in close distances compared to larger ones (F(1,11) = 190.77, p < .001, η² = .75 correlation-based, and F(1,11) = 148.20, p < .001, η² = .42, difference-based). An interaction effect for distance x movement emerged. After systematically varying the number of targets, these results could be replicated, with a slightly smaller effect. Based on performance levels, we introduce the concept of an optimal threshold algorithm, suggesting the best detection algorithm for the individual target configuration. Learnings of adding the third dimension to the detection algorithms and the role of distractors are discussed and suggestions for future research added.
摘要：
我们比较了虚拟现实（VR）中两种基于平滑追踪的对象选择算法的性能。要评估一系列配置的最佳算法，我们系统地改变了可供选择的目标的数量，他们的距离,以及它们的运动模式（线性和圆形）。性能被操作为命中率，错过和未被发现。所有距离的平均值，与线性运动模式（F（1,11）=24.27，p<.001，η²=.29）相比，基于相关性的算法对于圆形运动模式表现更好。这对于基于差异的算法(F(1,11)=0.98,p=.344,η²=.01)没有发现。与较大的算法（F（1,11）=190.77，p<.001，η²=.75相关，和F（1,11）=148.20，p<.001，η²=.42，基于差异）。出现了距离x运动的相互作用效应。在系统地改变了目标的数量之后，这些结果可以复制，效果略小。根据性能水平，我们引入了最优阈值算法的概念，建议针对单个目标配置的最佳检测算法。讨论了在检测算法中添加第三维的学习以及干扰物的作用，并为未来的研究提供了建议。
