Clusters, Trends, and Choices: Feature Selection in Interactive Statistical Graphics

This study investigates how user ability to manipulate plot features affects graphical perception, by extending a previous graphical study (Vanderplas and Hofmann, 2017) with an interactive framework. Similar to the original study, statistical lineups included two target patterns (a linear trend and a clustering pattern), as well as eighteen null plots generated from three different mixture proportions of the combined cluster and trend models. Participants were asked to select two plots that they perceived as ‘most different’, and were able to interact with the graphics by toggling aesthetic features such as cluster coloring, cluster ellipses, linear trendlines, and regression error bands. We found that toggle workflow varied across participants, revealing a divide between “maximalists,” who enabled all features, and “minimalists,” who used few or none, with most toggling occurring before the first selection. Starting features aesthetics did not have a significant effect on target choice. A generalized linear mixed model identified mixture proportion as the strongest predictor of target selection, with additional interactions involving the enabled ending features. These findings contribute to understanding how users engage with interactive graphical tools and how such tools support data interpretation in exploratory data analysis.