How different visualizations affect human reasoning about uncertainty: An analysis of visual behaviour

Manuele Reani, Caroline Jay, Niels Peek

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Humans find reasoning about uncertainty difficult. In decision support systems and software for intelligence analysis, graphical representations are commonly used to display uncertainty. Nevertheless, our understanding of how people use the information presented in graphs displaying uncertainty to make decisions is limited. As many artificial intelligent systems require a human-in-the-loop who is able to actively take part in the analysis process, the understanding of high-level cognition involved in human-graph interaction is essential in the design of better tools for analysis. In this research, we investigate the visual behaviour that is associated with participants responses to problems testing probabilistic reasoning represented through two different visualizations (tree and Venn diagrams). Using the data from visual fixations and transitions, we present a description of different reasoning strategies covering both accurate and inaccurate reasoning for different visualization formats. The results show that gaze behaviour is related to reasoning accuracy. Moreover, this study shows that different graphs representing the same problem evoke different reasoning strategies, suggesting that higher level cognition is influenced by the graphical representation in which uncertainty is encoded.
Original languageEnglish
Pages (from-to)55-64
Number of pages10
JournalComputers in human behavior
Early online date26 Oct 2018
Publication statusPublished - Mar 2019


  • Probabilistic reasoningEye trackingInformation visualizationDecision makingVisual analyticsHuman-computer interaction


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