Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TVCG.2018.2872577 | IEEE transactions on visualization and computer graphics |
Keywords | Field | DocType |
Taxonomy,Data visualization,Task analysis,Decision making,Visualization,Cognition,Systematics | Data science,Cognitive bias,Data visualization,Imperfect,Information visualization,Computer science,Visualization,Theoretical computer science,Human judgment,Cognition,Empirical research | Journal |
Volume | Issue | ISSN |
26 | 2 | 1077-2626 |
Citations | PageRank | References |
8 | 0.43 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evanthia Dimara | 1 | 54 | 6.68 |
| Steven Franconeri | 2 | 263 | 17.77 |
| Catherine Plaisant | 3 | 4689 | 622.05 |
| Anastasia Bezerianos | 4 | 674 | 37.75 |
| Pierre Dragicevic | 5 | 1639 | 73.69 |