Studies in the psychology of visual expertise have tended to focus on a limited set of expert domains, such as radiology and athletics. Conclusions drawn from these data indicate that experts use parafoveal vision to process images holistically. In this study, we examined a novel, as-of-yet-unstudied class of visual experts—architects—expecting similar results. However, the results indicate that architects, though visual experts, may not employ the holistic processing strategy observed in their previously studied counterparts. Participants (n = 48, 24 architects, 24 (...) naïve) were asked to find targets in chest radiographs and perspective images. All images were presented in both gaze-contingent and normal viewing conditions. Consistent with a holistic processing model, we expected two results: (1) architects would display a greater difference in saccadic amplitude between the gaze- contingent and normal conditions, and (2) architects would spend less time per search than an undergraduate control group. We found that the architects were more accurate in the perspectival task, but they took more time and displayed a lower difference in saccadic amplitude than the controls. Our research indicates a disjunctive conclusion. Either architects are simply different kinds of visual experts than those previously studied, or we have generated a task that employs visual expertise without holistic processing. Our data suggest a healthy skepticism for across-the-board inferences collected from a single domain of expertise to the nature of visual expertise generally. More work is needed to determine whether holism is a feature of all visual expertise. (shrink)

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction betweendirect quantitative uncertaintyandindirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of knowledge (...) concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty. (shrink)