Four types of ensemble coding in data visualizations
Four types of ensemble coding in data visualizations
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DOI：
10.1167/16.5.11
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发表时间：
2016-03-01
影响因子：
1.8
通讯作者：
Franconeri, Steven
中科院分区：
文献类型：
--
作者：
Szafir, Danielle Albers;Haroz, Steve;Franconeri, Steven
集成编码支持快速提取有关分布式视觉信息的视觉统计数据。研究人员通常研究这种能力，目的是得出有关这种编码如何从自然场景中提取信息的结论。在这里，我们认为第二个领域可以作为理解集成编码的另一个强大灵感：图形、地图和其他数据的视觉呈现。数据可视化允许观察者利用其对空间或特征视觉信息的分布执行视觉集成统计的能力来估计数据的实际统计数据。我们调查了日常示例中数据可视化中发生的视觉统计任务的类型，例如散点图，以及更专业的图像，例如天气图或文本模式描述。我们将这些任务分为四类：值集的识别、这些值的汇总、集合的分割和结构的估计。我们指出了每个类别中尚未解答的问题，并给出了当前文献中这种异花授粉的例子。数据可视化和感知心理学研究社区之间加强合作可以激发可视化挑战的新解决方案，同时暴露感知研究中未解决的问题。
Ensemble coding supports rapid extraction of visual statistics about distributed visual information. Researchers typically study this ability with the goal of drawing conclusions about how such coding extracts information from natural scenes. Here we argue that a second domain can serve as another strong inspiration for understanding ensemble coding: graphs, maps, and other visual presentations of data. Data visualizations allow observers to leverage their ability to perform visual ensemble statistics on distributions of spatial or featural visual information to estimate actual statistics on data. We survey the types of visual statistical tasks that occur within data visualizations across everyday examples, such as scatterplots, and more specialized images, such as weather maps or depictions of patterns in text. We divide these tasks into four categories: identification of sets of values, summarization across those values, segmentation of collections, and estimation of structure. We point to unanswered questions for each category and give examples of such cross-pollination in the current literature. Increased collaboration between the data visualization and perceptual psychology research communities can inspire new solutions to challenges in visualization while simultaneously exposing unsolved problems in perception research.