CRII: CHS: Understanding cognitive potentials of interactive visualizations

CRII：CHS：理解交互式可视化的认知潜力

• 批准号: 1755957
• 负责人: Paul Parsons
• 金额: $ 16.27万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755957&HistoricalAwards=false
• 关键词: CRII; CHS; Understanding; cognitive; potentials

Visualizations are increasingly used to support analysis and understanding of data; however, little is known about just how visualizations affect people's mental models of the underlying data and their resulting influence on analysis task performance. To that end, this project will conduct a number of experiments to address two key questions that link visualization design to cognition. The first question addresses how different techniques for visualizing the same data affect people's ability to carry out common analysis tasks, as well as how the designs affect their visual perception of and attention to different aspects of the data. The second focuses on the kinds of conceptual models people form about a dataset when they explore it using different visualization designs, and how visual and interactive elements of the designs affect those models. Developing better understandings of how people reason with visualizations will advance both cognitive psychology and visualization design. Students studying information visualization will be involved in the research, directly benefiting their design abilities and having potential benefits in domains where visualizations are becoming increasingly common, including journalism, education, business, and medicine. To address questions of how visual and spatial elements of a given design affect attention and task performance, the project team will first create a taxonomy of the visuo-spatial properties of common visualizations based on existing research scattered across a variety of academic disciplines, mapping visualization features to their ability to support various analysis tasks such as comparison, interpretation, ranking, and categorization. They will validate and extend the taxonomy through a series of experiments that look at time spent, error rates, eye gaze data, and qualitative participant feedback to triangulate conclusions about how different designs support observations and inferences over the data and which kinds of visualizations provide observational advantages for which kinds of tasks. The project team will design a series of analysis tasks that emphasize different cognitive processes derived from both Bloom's taxonomy and different types of knowledge (factual, conceptual, and procedural) in order to conduct experiments that get at the kinds of mental models people form when using different visualization designs. Participants in these experiments will be given time to explore the visualizations, then perform the tasks using a think-aloud protocol while the project team collects eye gaze data. Participants will then be asked to reflect on their analysis activities through a series of interviews, and draw a series of representations of the data that capture different sorts of relationships that might compose a mental model. A followup study will look at the effect of providing multiple visualizations and levels of control of which visualizations are shown, to address how interacting with multiple visualizations of the same data affects the mental models that people form.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.

可视化越来越多地用于支持数据的分析和理解;然而，人们对可视化如何影响人们对底层数据的心理模型及其对分析任务性能的影响知之甚少。 为此，该项目将进行一些实验，以解决将可视化设计与认知联系起来的两个关键问题。 第一个问题解决了如何不同的技术可视化相同的数据影响人们的能力，进行共同的分析任务，以及如何设计影响他们的视觉感知和注意力的不同方面的数据。 第二个重点是人们在使用不同的可视化设计探索数据集时形成的概念模型的种类，以及设计的视觉和交互元素如何影响这些模型。 更好地理解人们如何用可视化进行推理，将促进认知心理学和可视化设计。 学习信息可视化的学生将参与研究，直接受益于他们的设计能力，并在可视化变得越来越普遍的领域，包括新闻，教育，商业和医学，具有潜在的好处。为了解决给定设计的视觉和空间元素如何影响注意力和任务表现的问题，项目团队将首先根据分散在各种学科的现有研究创建常见可视化的视觉空间属性的分类，将可视化功能映射到其支持各种分析任务的能力，例如比较，解释，排名和分类。 他们将通过一系列实验来验证和扩展分类法，这些实验将研究花费的时间，错误率，眼睛凝视数据和定性参与者反馈，以三角分析关于不同设计如何支持对数据的观察和推断以及哪种可视化为哪种任务提供观察优势的结论。 项目团队将设计一系列分析任务，强调来自Bloom分类法和不同类型知识（事实，概念和程序）的不同认知过程，以便进行实验，了解人们在使用不同可视化设计时形成的各种心理模型。 这些实验的参与者将有时间探索可视化，然后在项目团队收集眼睛注视数据的同时使用有声思维协议执行任务。 然后，参与者将被要求通过一系列访谈来反思他们的分析活动，并绘制一系列数据表示，这些数据表示捕捉了可能构成心理模型的不同类型的关系。 后续研究将着眼于提供多种可视化效果和可视化显示的控制级别，以解决与相同数据的多种可视化交互如何影响人们形成的心理模型。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Conceptual Metaphor Theory as a Foundation for Communicative Visualization Design

概念隐喻理论作为交流可视化设计的基础

• 发表时间: 2018
• 期刊: IEEE VIS Workshop on Visualization for Communication (VisComm 2018
• 作者: Parsons, Paul C

Understanding Data Visualization Design Practice

了解数据可视化设计实践

• DOI: 10.1109/tvcg.2021.3114959
• 发表时间: 2022
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Parsons, Paul

What Design Methods do DataVis Practitioners Know and Use?

DataVis从业者了解和使用哪些设计方法？

• DOI: 10.1145/3334480.3383048
• 发表时间: 2020
• 期刊: CHI '20 EA: Extended Abstracts of the ACM SIGCHI Conference on Human Factors in Computing Systems
• 作者: Parsons, Paul;Baigelenov, Ali;Hung, Ya-Hsin;Schrank, Connor
• 通讯作者: Schrank, Connor

Toward an Analysis of Practitioner-Oriented Resources for Visualization Design

面向实践者的可视化设计资源分析

• 发表时间: 2018
• 期刊: Poster Abstracts
• 作者: Parsons, Paul C;Hung, Ya-Hsin;Baigelenov, Ali
• 通讯作者: Baigelenov, Ali