The Impact of Cognitive Differences on Processing Data Stories through Infographics: Advancing Toward Inclusive Design

This study explores how individuals with diverse cognitive abilities process data stories through infographics that integrate visual or verbal data representations, including seductive details. Specifically, it investigates the interactive effects of individual cognitive differences (working memory, inhibitory control, and visual search abilities) and data representation mode (visual vs. verbal) on the visual processing and comprehension of infographics. Fifty-one undergraduates performed tasks involving data infographics, with six communicating data verbally and six visually, alongside completing Inquisit tests assessing cognitive differences. Infographics were segmented into four regions of interest (ROIs), containing task answers. Comprehension was assessed by task accuracy. Visual processing was assessed by the number of fixations and their average durations on ROIs, utilizing the dispersion-threshold identification method and moving window algorithm. The author's custom Python program, available on GitHub (Kantonyan, n.d.), facilitates this analysis. Multiple mixed-effects regression analyses predicted visual processing and comprehension, incorporating data representation mode and individual cognitive differences. The study reveals that individuals demonstrate more effective engagement and comprehension with verbally represented data stories, influenced by the emphasis on improved reading comprehension in K-12 mathematics instruction. However, individuals with superior visuospatial working memory and goal-oriented visual search abilities exhibit enhanced engagement and comprehension across both data representation modalities, emphasizing the importance of spatial information processing. Furthermore, individuals effectively suppress unnecessary seductive details primarily when exploring verbally represented data stories, driven by their perception of data visualizations as images. This finding reflects their behavior to seek supportive information in other displayed images. The findings carry twofold implications: firstly, they provide valuable insights for designers to enhance data infographic design, and secondly, they underscore the effectiveness of utilizing eye-movement data to explain infographic processing. Future research is encouraged to explore real-time eye-movement data for classifying individuals based on cognitive differences and developing adaptive data stories for inclusive engagement.