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 term individual differences refers to individuals' "traits or stable tendencies to respond to certain classes of stimuli or situations in predictable ways" [DW96]. Much of the literature on individual differences has roots in psychology. Psychological research has demonstrated that people with distinct personality types and various cognitive abilities exhibit observable differences in task-solving and behavioral patterns [WB00,Ajz05]. Studies dating back to the late 1920s began by investigating variations in workplace performance [Hul28]. Throughout the intervening century, these findings have been extended to investigate individual characteristics that may predict performance under various conditions.
In the past few decades, the computational sciences have begun to recognize the role individual differences might play in shaping interaction in human-machine systems. For example, Benyon and Murray observed a relationship between spatial ability (a metric that measures a personâ Ȃ Źs ability to mentally represent and manipulate two-or three-dimensional objects) and task performance and preferences when using common interaction paradigms such as menus and the command line [BM93]. Nov et al. [NALB13] found that extraversion (one’s tendency to engage with the external world) and neuroticism (a measure of emotional stability) had effects on users’ contributions to online discussions, and suggested adaptations to certain visual cues to cater to different personality types. Gajos and Chauncey [GC17] observed that introverted people were more likely to use adaptive features in user interfaces as compared to extraverts. Orji et al. [ONDM17] showed that conscientious participants (a measure of carefulness or diligence) responded well to persuasive strategies such as self-monitoring and feedback in gamified systems. These studies are just a small sample of a large body of work documenting the influence of personality and cognitive ability on interactions with computer interfaces. For more detailed surveys of the literature, see [AA91,Poc91,DW96].
There is a growing interest in extending these findings to the field of data visualization [Yi12, ZOC * 12a]. Some posit that knowledge of broad differences between user groups could guide the design, evaluation, or customization of systems [VHW87,ZOC * 12a]. Supporting this claim, a cluster of promising research has produced evidence to suggest that individual characteristics, in addition to data mapping and visual encodings, determine the value of a visualization system. These studies have demonstrated that personality traits and cognitive abilities can have substantial impact on task performance [GF10, ZCY * 11], usage patterns [BOZ * 14, OYC15] and user satisfaction [Kob04]. Building on these findings, others have begun to examine how we might leverage cognitive traits for applications such as user modeling [BOZ * 14, OYC15] and adaptive interfaces [LTC19].
In some circumstances, the interaction between individual differences and visualization use may have critical impact on important decision-making processes. Ottley et al. [OPH * 15] investigated the impact of visualization on medical decision-making, and found that approximately 50% of the studied population were unsupported by commonly-used visualization tools when making decisions about their medical treatment. Specifically, their study showed that visual aides tended to be most beneficial for people with high spatial ability, while those with low spatial ability had difficulty interpreting and analyzing the underlying medical data when they were presented with visual representations. Another study by Conati and Maclaren [CM08] found that participants with high perceptual speed were less accurate in computing derived values when using radar graphs instead of heatmapped tables for data analysis. A series of studies have shown that locus of control (a measure of perceived control over external events) mediates search performance on hierarchical visualizations [GJF10, GF12, ZCY * 11, ZOC * 12b, OYC15, OCZC15]. These findings underscore the importance of incorporating individual differences into the design pipeline in order to create visualization tools that are broadly usable.
Unlike in human-computer interaction, to date there exists no comprehensive report that surveys the relevant literature on the role of individual differences in the data visualization domain. This makes it difficult to understand the scope of existing research on individual differences in this discipline, as there is no central resource researchers can consult to learn what individual differences, visualizations and tasks have been studied, and whether the results of those studies have been independently replicated. More importantly, there is limited information about how each existing study contributes to the ultimate goal of designing flexible data visualization tools that better support individual users.
In this STAR, we aim to produce
This content is AI-processed based on open access ArXiv data.
