Perceptual tasks in visualizations often involve comparisons. Of two sets of values depicted in two charts, which set had values that were the highest overall? Which had the widest range? Prior empirical work found that the performance on different visual comparison tasks (e.g., "biggest delta", "biggest correlation") varied widely across different combinations of marks and spatial arrangements. In this paper, we expand upon these combinations in an empirical evaluation of two new comparison tasks: the "biggest mean" and "biggest range" between two sets of values. We used a staircase procedure to titrate the difficulty of the data comparison to assess which arrangements produced the most precise comparisons for each task. We find visual comparisons of biggest mean and biggest range are supported by some chart arrangements more than others, and that this pattern is substantially different from the pattern for other tasks. To synthesize these dissonant findings, we argue that we must understand which features of a visualization are actually used by the human visual system to solve a given task. We call these perceptual proxies. For example, when comparing the means of two bar charts, the visual system might use a "Mean length" proxy that isolates the actual lengths of the bars and then constructs a true average across these lengths. Alternatively, it might use a "Hull Area" proxy that perceives an implied hull bounded by the bars of each chart and then compares the areas of these hulls. We propose a series of potential proxies across different tasks, marks, and spatial arrangements. Simple models of these proxies can be empirically evaluated for their explanatory power by matching their performance to human performance across these marks, arrangements, and tasks. We use this process to highlight candidates for perceptual proxies that might scale more broadly to explain performance in visual comparison.

中文翻译：

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视觉比较的感知代理。

可视化中的感知任务通常涉及比较。在两个图表中描述的两组值中，哪一组的值是最高的？哪个范围最广？先前的经验工作发现，在不同的视觉比较任务（例如，“最大增量”，“最大相关性”）上的性能在标记和空间排列的不同组合之间差异很大。在本文中，我们在两个新比较任务的实证评估中扩展了这些组合：两组值之间的“最大均值”和“最大范围”。我们使用阶梯法确定了数据比较的难度，以评估哪种安排对每个任务产生了最精确的比较。我们发现某些图表排列比其他图表排列更能支持最大平均数和最大范围的视觉比较，并且这种模式与其他任务的模式大不相同。为了综合这些不一致的发现，我们认为我们必须了解可视化的哪些功能实际上是人类视觉系统用来解决给定任务的。我们称这些感知代理。例如，当比较两个条形图的均值时，视觉系统可能会使用“平均长度”代理来隔离条形的实际长度，然后在这些长度上构造出真实的平均值。或者，它可以使用“船体面积”代理，该代理感知每个图表的条形图所界定的隐含船体，然后比较这些船体的面积。我们提出了一系列跨不同任务，标记和空间排列的潜在代理。通过将代理的性能与人类在这些标记，布置和任务上的表现相匹配，可以凭经验评估这些代理的简单模型的解释能力。我们使用此过程来突出显示感知代理的候选对象，这些代理可能会更广泛地扩展以解释视觉比较中的性能。