IEEE Trans Vis Comput Graph. 2020 Jan;26(1):1246-1255. doi: 10.1109/TVCG.2019.2934556. Epub 2019 Aug 22.
Latency in a visualization system is widely believed to affect user behavior in measurable ways, such as requiring the user to wait for the visualization system to respond, leading to interruption of the analytic flow. While this effect is frequently observed and widely accepted, precisely how latency affects different analysis scenarios is less well understood. In this paper, we examine the role of latency in the context of visual search, an essential task in data foraging and exploration using visualization. We conduct a series of studies on Amazon Mechanical Turk and find that under certain conditions, latency is a statistically significant predictor of visual search behavior, which is consistent with previous studies. However, our results also suggest that task type, task complexity, and other factors can modulate the effect of latency, in some cases rendering latency statistically insignificant in predicting user behavior. This suggests a more nuanced view of the role of latency than previously reported. Building on these results and the findings of prior studies, we propose design guidelines for measuring and interpreting the effects of latency when evaluating performance on visual search tasks.
人们普遍认为，可视化系统中的延迟会以可衡量的方式影响用户行为，例如使用户需要等待可视化系统响应，从而中断分析流程。虽然这种影响经常被观察到并且被广泛接受，但延迟究竟如何影响不同的分析场景，人们的理解还不够深入。在本文中，我们研究了延迟在视觉搜索中的作用，视觉搜索是使用可视化进行数据采集和探索的基本任务。我们在亚马逊 Mechanical Turk 上进行了一系列研究，发现在某些条件下，延迟是视觉搜索行为的一个统计学上显著的预测因素，这与先前的研究一致。然而，我们的结果还表明，任务类型、任务复杂性和其他因素可以调节延迟的影响，在某些情况下，延迟在预测用户行为方面变得统计学上不显著。这表明了延迟的作用比之前报告的更为复杂。基于这些结果和先前研究的发现，我们提出了在评估视觉搜索任务性能时测量和解释延迟影响的设计准则。
