Abstract
This study employed eye-tracking to examine the comprehension of data visualizations (DVs) with and without seductive details (Harp & Mayer, 1998) and representing either relative or absolute data. DVs serve to visualize numerical data and use visual dimensional objects. Given advancements in computer graphics, DVs are increasingly designed using (a) seductive details, and (b) relative data (e.g., data on monthly changes of COVID-19 cases). The purpose of this study was to explore how differences in undergraduates’ verbal and visuospatial working memory capacity (WMC) influence their processing of DVs with and without seductive details, and representing either relative or absolute data. The study was conducted with 70 undergraduates at a public US university. Each participant examined six COVID-19 DVs: (a) two with related seductive details, two with non-related seductive details, and two plain (no seductive details); as well as (b) three DVs representing absolute and three DVs with relative data representations. DV processing was assessed using the proportion of eye-gaze frequency data on DVs’ three regions of interest (ROIs)- instructions, visualizations, and assessment items. Additionally, participants completed ACCES test of reading span (rspan), visuospatial span (sspan), and operations span (ospan). Multiple regression analyses were performed with participants’ rspan, sspan, and ospan as predictors of average proportions of eye-gaze frequency on three ROIs for various conditions. The findings reveal that: (a) “related” details supported DV processing, whereas “non-related” details hindered it for people with higher operations and low visuospatial span scores, (b) all participants had difficulty with processing DVs with relative data regardless of their span scores, (c) participants with higher reading span needed significantly more time for examining visualizations compared to verbal information. Further studies will include analyses of participants’ fixations and saccades between ROIs. These findings motivate designing personalized DV environments sensitive to the differences in users’ WMC.