CHS: Medium: Collaborative Research: Empirically Validated Perceptual Tasks for Data Visualization

CHS：媒介：协作研究：数据可视化的经验验证感知任务

• 批准号: 2236644
• 负责人: Enrico Bertini
• 金额: $ 40.24万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236644&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Research; Empirically

Understanding quantitative data is a foundation of science, education, and the public communication of information about public policy and health. Our brains process and understand numbers far more efficiently when we can rely on data visualizations, allowing us to process patterns in data by leveraging the 40% of our brain that processes visual patterns in the real world. Decades of research in data visualization has produced evidence-backed guidelines for how to design the best data visualization for a given data analysis or communication task. But this process is limited by our incomplete understanding of the process by which we recognize patterns in visualized data. When people see a weather map color-coded by temperature, are they processing the hot and cold colors at the same perceptual moment, or just one? When they inspect a scatterplot, are people processing individual points, or the shape of the whole collection? This project will combine past research in the study of human vision, research in data visualization, and new research at the intersection of those two fields to create a model of how the visual system pulls patterns and statistics from visualized data. This model will lead to a more complete understanding of how to best harness the power of human vision to analyze a given dataset and to communicate a critical pattern clearly to an audience; this model will then be used to improve existing visualization tools.Data visualization research has sought to find the best visualization for a given data analysis task. For example, scatterplots allow relatively precise judgment of correlations, while line graphs are a powerful way to inspect trends over time. But systematically testing the performance of many tasks across many visualizations has not revealed systematic patterns of performance that would allow us to predict why some matches lead to better performance, what design changes might alter that performance, or how novel visualizations might perform. One problem is that current work is limited to focusing on what viewers want to accomplish, without being able to capture how viewers actually perform these tasks. The goal of the proposed research is to refine and empirically evaluate a lower-level model of "perceptual tasks" that underlie higher level tasks (e.g. "What is the average value in the dataset?") based on established results in perceptual psychology. First, the team will conduct a qualitative study that documents how people break a high-level task down into perceptual tasks, followed by an empirical evaluation of those qualitative findings. Next, the team will measure the precision and operation of the proposed perceptual tasks -- Filter Image, Judge Shape, Compute Distributions and Compute Ratio -- along with other tasks identified in the first study; together, these will provide a set of empirically-backed design guidelines to improve visualization effectiveness. Finally, the team will validate the model by comparing its predictions to findings from previous literature, then integrate new guidelines as constraints into the Draco visualization recommender system, which should improve its ability to predict the performance of different visualization designs. The resulting guidelines, model, and integration into Draco promise in turn to improve visualization education and practice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解定量数据是科学、教育以及公共政策和健康信息的公共传播的基础。当我们依靠数据可视化时，我们的大脑会更有效地处理和理解数字，从而使我们能够利用处理现实世界中视觉模式的 40% 的大脑来处理数据模式。数十年的数据可视化研究已经为如何为给定的数据分析或通信任务设计最佳数据可视化提供了有证据支持的指南。但这个过程受到我们对可视化数据模式识别过程的不完全理解的限制。当人们看到按温度进行颜色编码的天气图时，他们是在同一感知时刻处理冷热颜色，还是仅处理一个？当他们检查散点图时，人们是在处理单个点，还是整个集合的形状？该项目将结合过去在人类视觉研究、数据可视化研究以及这两个领域交叉领域的新研究，创建一个模型，展示视觉系统如何从可视化数据中提取模式和统计数据。该模型将使人们更全面地了解如何最好地利用人类视觉的力量来分析给定的数据集并向观众清楚地传达关键模式；然后，该模型将用于改进现有的可视化工具。数据可视化研究致力于为给定的数据分析任务找到最佳的可视化效果。例如，散点图可以相对精确地判断相关性，而折线图是检查随时间变化的趋势的有效方法。但是，系统地测试许多可视化中许多任务的性能并没有揭示系统的性能模式，从而使我们能够预测为什么某些匹配会带来更好的性能，哪些设计更改可能会改变该性能，或者新颖的可视化可能会如何执行。一个问题是，当前的工作仅限于关注观众想要完成的任务，而无法捕捉观众实际如何执行这些任务。拟议研究的目标是根据感知心理学的既定结果，完善和实证评估较低级别的“感知任务”模型，该模型是较高级别任务的基础（例如“数据集中的平均值是多少？”）。首先，该团队将进行一项定性研究，记录人们如何将高级任务分解为感知任务，然后对这些定性发现进行实证评估。接下来，该团队将测量所提出的感知任务（过滤图像、判断形状、计算分布和计算比率）以及第一项研究中确定的其他任务的精度和操作；这些将共同提供一套有经验支持的设计指南，以提高可视化效果。最后，该团队将通过将模型的预测与之前文献的发现进行比较来验证模型，然后将新的指南作为约束集成到 Draco 可视化推荐系统中，这将提高其预测不同可视化设计性能的能力。由此产生的指南、模型以及与 Draco 的集成有望改善可视化教育和实践。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing, and Coloring in Bar Charts

标记的排列影响所提供的消息：条形图中的排序、分区、间距和着色

• DOI: 10.1109/tvcg.2023.3326590
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Fygenson, Racquel;Franconeri, Steven;Bertini, Enrico
• 通讯作者: Bertini, Enrico