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Perception of Average Value in Multiclass Scatterplots

机译:多类散点图中的平均值感知

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The visual system can make highly efficient aggregate judgements about a set of objects, with speed roughly independent of the number of objects considered. While there is a rich literature on these mechanisms and their ramifications for visual summarization tasks, this prior work rarely considers more complex tasks requiring multiple judgements over long periods of time, and has not considered certain critical aggregation types, such as the localization of the mean value of a set of points. In this paper, we explore these questions using a common visualization task as a case study: relative mean value judgements within multi-class scatterplots. We describe how the perception literature provides a set of expected constraints on the task, and evaluate these predictions with a large-scale perceptual study with crowd-sourced participants. Judgements are no harder when each set contains more points, redundant and conflicting encodings, as well as additional sets, do not strongly affect performance, and judgements are harder when using less salient encodings. These results have concrete ramifications for the design of scatterplots.
机译:视觉系统可以对一组对象进行高效的综合判断,而速度大致与所考虑的对象数量无关。尽管有大量关于这些机制及其对视觉汇总任务的影响的文献,但是该先前的工作很少考虑需要较长时间进行多次判断的更复杂的任务,并且没有考虑某些关键的聚合类型,例如均值的本地化。一组点的值。在本文中,我们使用一个常见的可视化任务作为案例研究这些问题:多类散点图中的相对均值判断。我们描述了感知文学如何对任务提供一系列预期的约束,并通过与众包参与者的大规模感知研究评估了这些预测。当每个集合包含更多点,冗余和冲突的编码以及其他集合不会严重影响性能时,判断就不会困难,而使用较少显着编码时,判断会变得更加困难。这些结果对散点图的设计有具体的影响。

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