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    Altering interactive data visualization design can reduce decision biases. Removing distracting data points significantly decreased the attraction effect, suggesting system-level interventions for rational decision-making.

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    Area of Science:

    • Cognitive Psychology
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Human decision-making is susceptible to cognitive biases, even within data visualization contexts.
    • Existing bias mitigation strategies often target individual education with limited success.
    • The attraction effect, a bias influencing choice, can manifest in visual data exploration.

    Purpose of the Study:

    • To investigate the impact of interactive data visualization design on mitigating cognitive biases.
    • To present evidence for system-level interventions in bias reduction.
    • To explore the effectiveness of removing suboptimal data points in decision-making.

    Main Methods:

    • Participants interacted with 2D scatterplots designed to elicit the attraction effect.
    • Two intervention strategies were tested: highlighting superior alternatives and interactive removal of dominated points.
    • Decision-making patterns were analyzed to quantify bias mitigation.

    Main Results:

    • Highlighting superior alternatives partially weakened the attraction effect but did not eliminate it.
    • Interactive removal of locally dominated points significantly reduced the attraction effect.
    • This suggests that controlling data salience can influence decision rationality.

    Conclusions:

    • Interactive visualization tools can be designed to mitigate cognitive biases like the attraction effect.
    • Allowing users to remove distracting or inappropriately salient data enhances rational decision-making.
    • System-level design modifications offer a promising avenue for bias mitigation in data visualization.