Improving Data Visualization With Cognitive Science (2024-2025)

Effective data visualization is important for any field that uses data. Data visualizations allow audiences to perceive data patterns much more efficiently and accurately than written or verbal descriptions.

Best practices in data visualization have largely been developed by designers, statisticians and computer scientists. Surprisingly, there is still little evidence about whether humans perceive, interpret and interact with data visualizations in the way that best practices assume. This gap in how we are hypothesized to process data visualizations and how we actually process them limits our ability to predict what data visualizations will be most effective or most misleading.

This project team leveraged cognitive science tools to evaluate some of the most popular data visualization “best practices” and adjudicated between ways of applying them to individual data visualizations. The team used eye-tracking analysis software, cognitive surveys and qualitative interviews to test whether popular visualization practices have the intended impact on their audience’s perception, evaluation and understanding of data visualizations in different contexts.

Team members developed visualizations that intentionally challenged the efficacy of best visualization practices and engaged 2-3 data visualization experts to make predictions about how audience members would respond to those visualizations. The team collected eye-tracking, facial expression, questionnaire and qualitative interview data to test those predictions. Team members shared results from both phases in a public-facing blog to bring visibility to the team’s work and inspire others in the data visualization field to incorporate empirical testing into their practice and thinking.

Generated data will be used as preliminary evidence for whether online eye tracking tools could be used for follow-up work related to “Explainable AI” visualizations in future projects.

Timing

Fall 2024 – Spring 2025

Team Outputs

Blog posts

Symposium presentation to Duke and RTP data visualization community

Data for grant applications

Image: Data visualization of marine data, by Ars Electronica, licensed under CC BY-NC-ND 2.0