Survey on Individual Differences in Visualization

Zhengliang Liu, R. Jordan Crouser, and Alvitta Ottley



Abstract: Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one-size-fits-all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence-based reviews, and spur future inquiry.

The 29 key articles we reviewed. The filled boxes indicate the traits ( ), visualizations ( ), tasks ( ), and measures ( ) that were in the manuscripts. We use 'X' to indicate traits that were evaluated, but no measurable effect was reported under the studied conditions.


Acknowledgments
We thank Jesse Huang'19 (Washington University in St. Louis) for his help with data collection, and Ananda Montoly '22 (Smith College) for her work validating the paper classification. This project was supported in part by: the Laboratory for Analytic Sciences at North Carolina State University, Boeing under award 2018-BRT-PA-332, and the National Science Foundation under Grant No. 1755734.