Beyond the Numbers: Comprehension and Recall in Data-Driven Visual Communication
In today’s data-saturated society, visual communication has become an essential tool for making complex information accessible. As exposure to visual data increases—whether through educational settings, media outlets, official statistics, public institutions, or social media—the ability to interpret charts becomes a vital form of literacy. However, our knowledge of how charts from real-life contexts are understood and remembered, and what influences their comprehension and recall is relatively modest. This study examines how individuals comprehend and recall information from plain versus embellished bar charts using authentic Eurostat data. In a between-subjects design, participants described chart messages and recalled visuals at two different time points. Unlike traditional comprehension tests, open-ended responses were used to capture spontaneous interpretations, simulating a real-world task: viewing a public statistical chart and making sense of it without guidance. Most participants identified the general topic—often echoing the title—but deeper insights (e.g., pattern recognition, opinions) were rare. While embellishments did not affect message extraction, they significantly improved chart recall. A statistical background positively predicted recall, with some evidence also suggesting an association with comprehension, whereas cognitive reasoning and topic interest showed limited associations. These findings underscore the importance of chart titles, user experience, and design features in shaping understanding. The study advocates for improved chart wording, cautious use of embellishment, and educational efforts to foster data visualization literacy.
Authors
- Irena Bolko (ORCID: https://orcid.org/0000-0003-0847-0560)
- Mojca Bavdaž (ORCID: https://orcid.org/0000-0003-1924-7516)
Institutions
- University of Bergamo (IT)
- University of Ljubljana (SI)
Publication Details
- Journal
- SAGE Open
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1177/21582440251406854
- Primary Topic
- Data Visualization and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00