Data Abstraction Elephants: The Initial Diversity of Data Representations and Mental Models

Data Abstraction Elephants: The Initial Diversity of Data Representations and Mental Models
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数据抽象大象:数据表示和心理模型的初始多样性

DOI:
10.1145/3544548.3580669
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发表时间:
2023
期刊:
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
影响因子:
--
通讯作者:
Isaacs, Katherine E.
Isaacs, Katherine E.
中科院分区:
--
文献类型:
--
作者:
Williams, Katy;Bigelow, Alex;Isaacs, Katherine E.

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两个人看着相同的数据集将创建不同的心理模型,确定不同的属性的优先顺序,并连接不同的可视化效果。我们试图了解与心理模型相关的数据抽象空间,以及人们在绘制草图时如何很好地沟通他们的心理模型。数据抽象对可视化设计有着深远的影响,但当它们最初不受表示影响时,还不清楚它们的普适性有多大。我们进行了一项关于人们如何根据数据集创建他们的心理模型的研究。我们没有展示表格数据,而是以段落的形式向每个参与者展示了三个数据集中的一个,以避免对数据抽象和心理模型的偏见。我们观察了同一数据集中不同的心理模型、数据抽象和描述,以及这些概念如何受到交流和目标追求的影响。我们的结果对可视化设计有意义,特别是在发现和数据收集阶段。
Two people looking at the same dataset will create different mental models, prioritize different attributes, and connect with different visualizations. We seek to understand the space of data abstractions associated with mental models and how well people communicate their mental models when sketching. Data abstractions have a profound influence on the visualization design, yet it’s unclear how universal they may be when not initially influenced by a representation. We conducted a study about how people create their mental models from a dataset. Rather than presenting tabular data, we presented each participant with one of three datasets in paragraph form, to avoid biasing the data abstraction and mental model. We observed various mental models, data abstractions, and depictions from the same dataset, and how these concepts are influenced by communication and purpose-seeking. Our results have implications for visualization design, especially during the discovery and data collection phase.
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