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Cognitive Biases in Visualizations pp 137–147Cite as

Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization

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Abstract

Information visualization involves the use of visual representations of data to amplify cognition. While visualizations do generally amplify cognition, they also have representational biases that encourage thinking and reasoning in certain ways at the expense of others. I propose that the development of representational fluency by visualization designers and users can help mitigate such biases, and that promoting representational fluency in visualization education and practice can be a useful general strategy for mitigating cognitive biases. Literature from various disciplines is discussed, including perspectives on meta-visualization, representational competence, and meta-representational competence. Some implications for visualization research, education, and practice are examined. The need for engaging users in deep, effortful cognitive processing is discussed and is situated within literature on established bias-mitigating strategies. A preliminary research agenda comprising five challenges is also proposed.

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Notes

  1. 1.

    Representation and visualization are used interchangeably throughout when referring to external, visual representations of data. A discussion of internal representations is outside the scope of this paper.

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Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Paul Parsons

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  1. Paul Parsons
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Correspondence to Paul Parsons .

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  1. Data Analysis and Visualisation Group, Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Geoffrey Ellis

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Parsons, P. (2018). Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization. In: Ellis, G. (eds) Cognitive Biases in Visualizations. Springer, Cham. https://doi.org/10.1007/978-3-319-95831-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-95831-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95830-9

  • Online ISBN: 978-3-319-95831-6

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