Croissant Charts: Modulating the Performance of Normal Distribution Visualizations with Affordances
This work addresses the need for more effective visualizations in data analysis by providing a theoretical basis for design improvements, though it is incremental as it builds on existing affordance theory in visualization.
The paper tackled the problem of explaining why certain visualizations perform better by applying affordance theory to normal distribution plots, resulting in the Croissant Chart which was empirically validated in a study with 808 participants to show predictable performance improvements.
Affordances, originating in psychology, describe how an object's design influences the physical and cognitive actions users may take. Past work applied affordance theory to visualization to explain how design decisions can impact the cognitive actions of visualization readers. In this work, we demonstrate that affordances can complement effectiveness rankings by further explaining the root causes behind visualizations' task performance. To do so, we conduct a case study on static normal probability density function plots, identifying their current affordances. Next, we identify the optimal affordances for a common probability-comparison task and develop a novel affordance-driven visualization, the Croissant Chart, to support them. We empirically validate the design's effectiveness through a preregistered study (n = 808), demonstrating how affordances can inform predictable changes in task performance. Our findings underscore the potential for affordance-based approaches to enhance visualization effectiveness and inform future design decisions.