A Task-Based Taxonomy of Cognitive Biases for Information Visualization

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 lim...

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Vydáno v:IEEE transactions on visualization and computer graphics Ročník 26; číslo 2; s. 1413 - 1432
Hlavní autoři: Dimara, Evanthia, Franconeri, Steven, Plaisant, Catherine, Bezerianos, Anastasia, Dragicevic, Pierre
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Témata:
Bias
classification
Cognition
Cognition - classification
Cognition - physiology
Cognitive bias
Cognitive science
Computer Graphics
Computer Science
Data analysis
Data visualization
Decision analysis
Decision making
Decision Making - physiology
Design
Empirical analysis
Empirical Research
Human-Computer Interaction
Humans
Photic Stimulation
Psychology
Scientific visualization
Systematics
Task analysis
Taxonomy
Visualization
visualization
taxonomy
classification
cognitive bias
decision making
Index Terms-cognitive bias
ISSN:1077-2626, 1941-0506, 1941-0506
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Popis
Shrnutí: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.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2018.2872577