A Task-Based Taxonomy of Cognitive Biases for Information Visualization
A Task-Based Taxonomy of Cognitive Biases for Information Visualization
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DOI：
10.1109/tvcg.2018.2872577
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发表时间：
2020-02-01
影响因子：
5.2
通讯作者：
Dragicevic, Pierre
中科院分区：
文献类型：
--
作者：
Dimara, Evanthia;Franconeri, Steven;Dragicevic, Pierre
信息可视化设计人员努力设计数据显示，允许对数据中的模式进行有效的探索、分析和交流，从而做出明智的决策。不幸的是，人类的判断和决策并不完美，经常受到认知偏见的困扰。记录这些偏差如何影响视觉数据分析活动的实证研究有限。现有的分类是由认知理论组织的，这些理论很难与可视化任务联系在一起。在文献综述的基础上，我们提出了一种基于任务的分类方法，将154种认知偏向分为7个主要类别。我们希望分类将帮助可视化研究人员将他们的设计与相应的可能偏差联系起来，并导致新的研究，以检测和解决数据可视化中的偏见判断和决策。
Information visualization designers strive to design data displays that allow for efficient exploration, analysis, and communication of patterns in data, leading to informed decisions. Unfortunately, human judgment and decision making are imperfect and often plagued by cognitive biases. There is limited empirical research documenting how these biases affect visual data analysis activities. Existing taxonomies are organized by cognitive theories that are hard to associate with visualization tasks. Based on a survey of the literature we propose a task-based taxonomy of 154 cognitive biases organized in 7 main categories. We hope the taxonomy will help visualization researchers relate their design to the corresponding possible biases, and lead to new research that detects and addresses biased judgment and decision making in data visualization.