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Journal Articles IEEE Transactions on Visualization and Computer Graphics Year : 2019

Mitigating the Attraction Effect with Visualizations

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Abstract

Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias – the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.
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Dates and versions

hal-01845004 , version 1 (19-07-2018)
hal-01845004 , version 2 (22-08-2018)

Identifiers

Cite

Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri. Mitigating the Attraction Effect with Visualizations. IEEE Transactions on Visualization and Computer Graphics, 2019, TVCG 2019 (InfoVis 2018), 25 (1), pp.850 - 860. ⟨10.1109/TVCG.2018.2865233⟩. ⟨hal-01845004v2⟩
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