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A Task-Based Taxonomy of Cognitive Biases for Information Visualization.

Evanthia Dimara, Steven Franconeri, Catherine Plaisant

    IEEE Transactions on Visualization and Computer Graphics
    |October 4, 2018
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    This study introduces a task-based taxonomy of 154 cognitive biases to help researchers understand how biases impact data visualization and decision-making, aiming to improve data analysis and reduce errors.

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    Area of Science:

    • Information Visualization
    • Cognitive Psychology
    • Human-Computer Interaction

    Background:

    • Human decision-making is prone to cognitive biases, affecting data analysis.
    • Limited research exists on how these biases impact visual data analysis.
    • Current bias taxonomies are theory-based, not task-oriented for visualization.

    Purpose of the Study:

    • To propose a novel, task-based taxonomy of cognitive biases relevant to information visualization.
    • To organize 154 identified cognitive biases into 7 main categories.
    • To facilitate the design of more robust and less bias-prone data visualizations.

    Main Methods:

    • Conducted a comprehensive survey of existing literature on cognitive biases and information visualization.
    • Developed a new taxonomy categorizing biases based on visualization tasks.
    • Organized 154 distinct cognitive biases within this framework.

    Main Results:

    • A task-based taxonomy of 154 cognitive biases has been developed.
    • The taxonomy is structured into 7 main categories, linking biases to specific visualization activities.
    • This provides a framework for understanding bias in visual data analysis.

    Conclusions:

    • The proposed taxonomy aids visualization researchers in identifying potential biases in their designs.
    • It encourages new research into detecting and mitigating biased judgment in data visualization.
    • This work aims to improve the reliability of data-driven decision-making.