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

理解量化数据是科学、教育以及公共政策和健康信息公共交流的基础。当我们可以依赖于数据可视化时，我们的大脑处理和理解数字的效率要高得多，这使得我们能够通过利用我们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。几十年来对数据可视化的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证据支持的指导方针。但这一过程受到我们对可视化数据中模式识别过程的不完全理解的限制。当人们看到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颜色，还是只处理一种？当他们检查散点图时，人们是在处理单个点，还是处理整个集合的形状？这个项目将结合过去在人类视觉研究中的研究，在数据可视化方面的研究，以及在这两个领域的交叉领域的新研究，以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。这个模型将导致对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晰地传达给受众的更全面的理解；然后，该模型将被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，散点图可以相对准确地判断相关性，而折线图是检查一段时间内趋势的强大方法。但是，系统地测试许多可视化中的许多任务的性能并没有揭示系统的性能模式，这些模式让我们能够预测为什么某些匹配会带来更好的性能，哪些设计变化可能会改变这种性能，或者新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是如何执行这些任务的。拟议研究的目标是提炼和经验性地评估作为较高级别任务的基础的较低级别的“知觉任务”模型(例如：“数据集中的平均值是多少？”)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，然后对这些定性结果进行实证评估。接下来，该团队将测量拟议的感知任务--过滤图像、判断形状、计算分布和计算比率--的精度和操作，以及第一项研究中确定的其他任务；这些任务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，该团队将通过将其预测与以前文献中的发现进行比较来验证该模型，然后将新的指导方针作为约束整合到Draco可视化推荐系统中，这将提高其预测不同可视化设计的性能的能力。由此产生的指导方针、模型和与DRACO的整合承诺反过来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优势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.