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

理解定量数据是科学、教育以及公共政策和卫生信息的公共交流的基础。当我们可以依靠数据可视化时，我们的大脑处理和理解数字的效率要高得多，这使得我们可以利用大脑中处理现实世界中视觉模式的40%来处理数据中的模式。几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的数据可视化提供了有证据支持的指导方针。但是，这个过程受到我们对识别可视化数据模式的过程的不完整理解的限制。当人们看到一张以温度为颜色编码的天气图时，他们是在同一感知时刻处理冷热色，还是只处理一个？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？该项目将结合过去的人类视觉研究、数据可视化研究以及这两个领域交叉的新研究，创建一个视觉系统如何从可视化数据中提取模式和统计数据的模型。该模型将使我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，并将关键模式清楚地传达给受众；这个模型将用于改进现有的可视化工具。数据可视化研究试图为给定的数据分析任务找到最佳的可视化。例如，散点图允许对相关性进行相对精确的判断，而线形图是检查一段时间趋势的有力方法。但是，系统地测试许多任务的性能并没有揭示系统的性能模式，这将使我们能够预测为什么某些匹配会导致更好的性能，哪些设计更改可能会改变性能，或者新颖的可视化可能会如何执行。一个问题是，目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执行这些任务的方式。本研究的目标是完善和实证评估一个较低层次的“感知任务”模型，该模型是高层次任务（例如：感知任务）的基础。“数据集的平均值是多少？”)基于知觉心理学的既定结果。首先，该团队将进行定性研究，记录人们如何将高水平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，团队将测量所提出的感知任务的精度和操作-过滤图像，判断形状，计算分布和计算比率-以及在第一项研究中确定的其他任务；总之，这些将提供一套经验支持的设计指导方针，以提高可视化效果。最后，团队将通过将模型的预测结果与先前文献的结果进行比较来验证模型，然后将新的指南作为约束整合到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。由此产生的指导方针、模型和集成到Draco的承诺依次改善可视化教育和实践。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.