Visualizing Uncertainty for Non-Expert End Users: The Challenge of the Deterministic Construal Error - Joslyn, Susan; Savelli, Sonia (Frontiers in Computer Science, 2021)
There is a growing body of evidence that numerical expressions of uncertainty can be used by nonexperts to improve decision quality. Do these same advantages extend to graphic expressions of uncertainty? Here we provide a brief review of the literature and discuss the methodological issues related to this important question. In addition, we discuss key misunderstandings that may arise from uncertainty visualizations, in particular the evidence that users of visualizations, including experts, sometimes fail to realize that the graphic depicts uncertainty. Instead they have a tendency to interpret the image as representing some deterministic quantity. We refer to this as the deterministic construal error (DCE). Although there is now growing evidence for the DCE, few studies are designed to detect it directly because they inform participants upfront that the visualization expresses uncertainty. In a natural setting, such cues would be absent, perhaps making the deterministic assumption even more likely. Here we discuss the psychological roots of this key but underappreciated misunderstanding as well as possible solutions. This is a critical question because it is now clear that members of the public understand that predictions involve uncertainty and have greater trust when uncertainty is included. Moreover, they can understand and use predictions that include uncertainty information to tailor decisions to their own risk tolerance, as long as they are carefully expressed, taking into account the cognitive processes involved.