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.

中文翻译：

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基于任务的认知偏见分类法，用于信息可视化

信息可视化设计师致力于设计数据显示，以便有效地探索，分析和传达数据中的模式，从而做出明智的决策。不幸的是，人类的判断和决策是不完善的，并且常常受到认知偏差的困扰。有限的经验研究记录了这些偏见如何影响视觉数据分析活动。现有的分类法是由认知理论组织的，这些理论很难与可视化任务关联。根据对文献的调查，我们提出了以任务为基础的分类法，该分类法将154个认知偏差分类为7个主要类别。我们希望分类法将帮助可视化研究人员将其设计与相应的可能偏见相关联，并导致进行新的研究，以发现并解决数据可视化中的偏见判断和决策。