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.


_Eurovis \electronicVersion\PrintedOrElectronic Survey on Individual Differences in Visualization]Survey on Individual Differences in Visualization Z. Liu, R. J. Crouser & A. Ottley] Zhengliang Liu, R. Jordan Crouser, and Alvitta Ottley
Washington University in St. Louis, USA
Smith College, USA

1 Introduction

The term individual differences refers to individuals’ “traits or stable tendencies to respond to certain classes of stimuli or situations in predictable ways” [50]. 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 [165, 2]. Studies dating back to the late 1920s began by investigating variations in workplace performance [83]. 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 person’s 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 [15]. Nov et al. [118] 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 [64] observed that introverted people were more likely to use adaptive features in user interfaces as compared to extraverts. Orji et al. [121] 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 [12, 130, 50].

There is a growing interest in extending these findings to the field of data visualization [167, 172]. Some posit that knowledge of broad differences between user groups could guide the design, evaluation, or customization of systems [157, 172]. 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 [72, 170], usage patterns [20, 126] and user satisfaction [93]. Building on these findings, others have begun to examine how we might leverage cognitive traits for applications such as user modeling [20, 126] and adaptive interfaces [97].

In some circumstances, the interaction between individual differences and visualization use may have critical impact on important decision-making processes. Ottley et al. [125] 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 [40] 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 [74, 73, 170, 173, 126, 123]. 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 a comprehensive survey that reviews the literature relevant to this topic. We identify and taxonomize existing scholarship to provide a complete picture of the current state of research, and identify possible avenues for investigation that builds upon this existing body of work. We begin by describing the scope of our review and methodology. We then proceed to a detailed analysis of the findings of this body of work. Finally, we reflect on our review to discuss core topics and opportunities for future development in this emerging area.

2 Existing Perspectives on Individual Differences

The sampling of scholarly work in the previous section demonstrates the wide variety of individual differences that may be relevant to the visualization community. Pioneering work by Peck et al. [128] proposed the Individual Cognitive Differences () model which classified the space of individual differences into three dimensions (see Figure 1):

  • Cognitive traits are the relatively stable characteristics of an individual that include features of a person’s personality alongside their cognitive abilities, such as perceptual speed, spatial ability, and visual memory.

  • Cognitive states are temporary mental states such as cognitive load and emotion. They are, by definition, transient and related to recent stimuli and the surrounding environment.

  • Experience is the long-term construction of knowledge through exposure to real-world stimuli. Bias describes the predispositions one has such that one behaves in certain ways when performing certain tasks. Together, experience and bias represent a dimension that describes the accumulation of experiences that influence behavior when encountering a familiar scenario.

Efforts to systematically evaluate visualization literacy (a measure of visualization experience for non-experts) [7, 18, 17, 54, 100] postdate the model, but this can be viewed as a specific domain of familiarity.

In this STAR, we restrict the scope of our survey to focus only on the invariant characteristics that distinguish one person from another. Unlike cognitive states and measures of experience, the cognitive traits covered in this survey are stable throughout adulthood. This makes it tractable to reason about how the community can begin to incorporate individual difference into design and evaluation pipelines. Our goal is to advocate for the advancement of individual difference research in the visualization discipline by highlighting the pioneering work in this domain.

Figure 1: The ICD model from Peck et al. [128] categorizes individual differences into three orthogonal dimensions: cognitive traits, cognitive states, and experience/bias. In this STAR, we focus exclusively on cognitive trails.
Cognitive Traits
Extraversion The tendency to engage with the external world.
Neuroticism The tendency to experience negative emotions such as stress, depression or anger.
Openness to Experience The propensity to seek, appreciate, understand and use information.
Agreeableness The tendency to consider the harmony among a group of individuals.

Five-Factor Model [68]

Conscientiousness The propensity to control one’s impulse and display self-discipline.
Locus of Control
[134, 135, 136]
The extent to which a person believes the external world is influenced by their own actions, and/or whether they have control over the outcome of events occurring around them.

Personality Traits

Need for Cognition [25] The tendency to engage in and enjoy activities that involve thinking.
Spatial Ability [131] The ability to generate, understand, reason and memorize spatial relations among objects.
Perceptual Speed [57] The rate at which an individual is able to make accurate visual comparisons between objects.
Visual / Spatial Memory [141] The capacity to remember the appearance, configuration, location, and/or orientation of an object.

Cognitive Abilities

Working Memory [13] The capacity to store information for immediate use.
Table 1: Definitions of the cognitive traits that are common in the visualization literature.

3 Survey Scope and Methodology

This STAR report surveys the ongoing research that studies the impact of individual differences on the use of data visualizations. The candidate papers are obtained via three methods. First, we obtain the main corpus by reviewing all the papers published on leading conferences and journals in Visualization and HCI, including InfoVis, VAST, SciVis, EuroVis, TVCG, CHI and IUI from 2008 to 2019. Second, we search Google Scholar, ACM Digital Library and IEEE Digital libraries with keywords such as individual differences, personality, cognitive ability and filter the returned results to retrieve only data visualization publications. We also web-scrape ACM Digital Library and IEEE Digital Libraries and filter the results programmatically to aid the process. Finally, we follow the citations of the candidate papers obtained in the first two methods to retrieve relevant publications (some of which are not published in computer science venues). For all candidate papers we have collected, we manually review the title, abstract, introduction and conclusions to determine whether they are within our proposed scope. If in doubt, we also review the main content of a paper to determine its inclusion or exclusion. Eventually, we have found 31 keys publications that are within the review scope for our main analysis.

3.1 Coding

We compiled a corpus of relevant literature and organized the prior work based on the types of individual differences, the visualizations used in the studies, and experimental designs such as the tasks and measure used in the experiments. During the first round of coding, a single author thoroughly read all papers to create an initial set of keywords. A second author then re-read the papers and added or consolidated the keywords when there were gaps or redundancies. For the final round of coding, a two independent researchers validated the coding tags and populated Table 2.

Traits Visualizations Tasks Measures






Locus of Control

Need for Cognition

Spatial Ability

Perceptual Speed

Visual/Spatial Memory

Visual Working Memory

Verbal Working Memory

Simple Visualization





Search / Retrieve Value

Find Extremum

Compute Derived Value






Other Quantitative

Other Qualitative

Vicente et al. (1987) [157]