Ergonomic Considerations for the Design and the Evaluation of Uncertain Data Visualizations

Abstract

Uncertainty impacts many crucial issues the world is facing today – from climate change prediction, to scientific modelling, to the interpretation of medical data. Decisions typically rely on data which can be aggregated from different sources and further transformed using a variety of algorithms and models. Such data processing pipelines involve different types of uncertainty. As visual data representations are able to mediate between human cognition and computational models, a trustworthy conveyance of data characteristics requires effective representations of uncertainty which take productivity and cognitive abilities, as important human factors, into account. We summarize findings resulting from prior work on interactive uncertainty visualizations. Subsequently, an evaluation study is presented which investigates the effect of different visualizations of uncertain data on users’ efficiency (time, error rate) and subjectively perceived cognitive load. A table, a static graphic, and an interactive graphic containing uncertain data were compared. The results of an online study (N = 146) showed a significant difference in the task completion time between the visualization type, while there are no significant differences in error rate. A non-parametric K-W test found a significant difference in subjective cognitive load [H (2) = 7.39, p < 0.05]. Subjectively perceived cognitive load was lower for static and interactive graphs than for the numerical table. Given that the shortest task completion time was produced by a static graphic representation, we recommend this for use cases in which uncertain data are to be used time-efficiently.