Captions help readers better understand visualizations. However, if the visualization is intended to communicate specific features, should the caption be statistical, and focus on specific values, or perceptual, and focus on general patterns? Prior work has shown that when captions mention visually salient features, users tend to recall those features. Still, we lack explicit guidelines for how to compose the appropriate caption. Further, what if the author wishes to emphasize a less salient feature? In this paper, we study how the visual salience of the feature described in a caption, and the semantic level of the caption description, affect a reader's takeaways from line charts. For each single- or multi-line chart, we generate 4 captions that 1) describe either the primary or secondary most salient feature in a chart, and 2) describe the feature either at the statistical or perceptual levels. We then show users random chart-caption pairs and record their takeaways. We find that the primary salient feature is more memorable for single-line charts when the caption is expressed at the statistical level; for secondary salient features in single- and multi-line charts, the perceptual level is more memorable. We also find that many users will tend to rely on erroneous data in the caption and not double-check its veracity against the data in the chart.
翻译:说明有助于读者更好地了解可视化。 但是, 如果可视化的目的是要传达具体特征, 标题应该是统计性的, 并关注特定值或感知性的, 并且关注一般模式? 先前的工作已经显示, 当标题提到视觉特征时, 用户往往会记得这些特征 。 但是, 我们还缺乏关于如何写出适当标题的明确指南 。 此外, 如果作者希望强调一个不那么突出的特征? 在本文中, 我们研究标题中描述的特征的视觉特征和标题描述的语义水平如何影响读者从线形图表中取出的东西 。 对于每个单线或多线图表, 我们生成了 4 个标题 说明 1, 描述图表中的第一或第二线最突出特征, 2 描述在统计或感知层面的特征 。 我们然后显示用户随机的图表配对并记录其取物 。 我们发现, 当标题在统计层面显示时, 单线图中描述的单线图的主要特征会更加令人难忘; 对于单线图中的读者的取量。 对于每个单线图的次突出特征, 我们在单线和多线图中的第二位图中会发现, 。