Manipulating and Controlling for Personality Effects on Visualization Tasks

Researchers in human–computer interaction and visualization have recently been challenged to develop a better understanding of users’ underlying cognitive processes in order to improve system design and evaluation. While existing studies lay a critical foundation for understanding the role of cognitive processes and individual differences in visualization, concretizing the intuition that each user experiences a visual interface through an individual cognitive lens is only half the battle. In this article, we investigate the impact of manipulating users’ personality on observed behavior when using a visualization. In a targeted study, we demonstrate that personality priming can result in changes in behavior when interacting with visualizations. We then discuss how this and similar techniques could be used to control for personality effects when designing and evaluating visualizations systems

Tag clouds algorithm with the inclusion of personality traits

Tag clouds have emerged as the latest technique in information visualization using text analysis methods in a variety of situations to interpret unstructured data types. Literature review emphasizes that information visualization development techniques should include the personality traits of humans to provide effective and meaningful information. However, in the field of tag clouds, no published studies have investigated the role of personality traits to guide the design of tag cloud visualization. Furthermore, the algorithm to generate tag cloud visualization based on personality traits has not been explored. Therefore, the main objective of this study is to develop an algorithm that can adapt visual features of tag cloud layout styles based on personality traits of the user. This study focuses on two visual features associated with personality traits, which are colors and shapes. To achieve the aim of this study, Design Science methodology was used through three main phases: problem identification, design of solution, and evaluation. The algorithm was developed based on three theories of personality traits, namely Myers-Briggs Type Indicator (MBTI), Shape, and Multiple Intelligence (MI). The algorithm was then tested through a black box testing. In addition, a prototype was developed to evaluate the proposed algorithm. Then, user satisfaction was conducted in order to evaluate this prototype using Q-SAFI instruments. Notable findings suggest that users are highly satisfied with colors and shapes of tag cloud as well as the overall tag cloud layout styles. The main contribution of this research is the tag cloud layout styles algorithm, which combines the concept of personality traits and characteristics of colors and shapes. This algorithm is beneficial for decision making using information visualization in which personality traits of the user are heavily inclined. Moreover, the tag cloud user’s satisfaction instrument, Q-SAFI, provides measurements for evaluating tag cloud visualization

Survey on Individual Differences in Visualization

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 ROLE OF EMOTION IN VISUALIZATION

The popular notion that emotion and reason are incompatible is no longer defensi- ble. Recent research in psychology and cognitive science has established emotion as a key element in numerous aspects of perception and cognition, including attention, memory, decision-making, risk perception, and creativity. This dissertation centers around the observation that emotion influences many aspects of perception and cog- nition that are crucial for effective visualization. First, I demonstrate that emotion influences accuracy in fundamental visualiza- tion tasks by combining a classic graphical perception experiment (from Cleveland and McGill) with emotion induction procedures from psychology (chapter 3). Next, I expand on the experiments in the first chapter to explore additional techniques for studying emotion and visualization, resulting in an experiment that shows that performance differences between primed individuals persist even as task difficulty in- creases (chapter 4). In a separate experiment, I show how certain emotional states (i.e. frustration and engagement) can be inferred from visualization interaction logs using machine learning (chapter 5). I then discuss a model for individual cognitive dif- ferences in visualization, which situates emotion into existing individual differences research in visualization (chapter 6). Finally, I propose an preliminary model for emotion in visualization (chapter 7)

Use of performance predictors in visual analytics

Visual Analytics is a multi-disciplinary field that uses interactive visualisations to promote and assist the analytic reasoning and generate insights. Understanding the perceptual and cognitive factors is key to the progress in this field. This research focuses on understanding the benefits of interaction in terms of insight generation Moreover, this investigation explores the compounding effects individual differences have with interaction when analysing data to generate insights. This study investigated the individual differences in two sets; psychometric set measures, and a sensorial preferences multimodal learning style. Interaction was analysed from an information visualisation perspective, exploring the Visual Mapping and View Transformation interaction, by isolating interaction as an independent variable. Moreover, the View Transformation experiment used two different visual representations 2D and 3D. Additionally, the individual differences were analysed using the aptitude-by-treatment interaction (ATI) methodology. The ATI approach enabled the assessment of the performance gains in terms of insight generation according to pre-defined set levels of individual differences measures. This thesis confirms the benefits of interaction in generating more insights and increasing their accuracy, whilst facilitating the generation of insights requiring lower mental effort. Further, the results show significant conjoint effects between interaction and individual differences. Furthermore this research revealed a performance difference between 2D and 3D visual representation in the serious game problem solving context. Overall, this thesis provides tangible proof that both visual mapping and view transformation interaction are beneficial to visual analytics in generating insights. Strengthening the view that interaction with the problem-set improves understanding, and the number of insights gleaned into the problem and that more research into the use of individual differences, as a performance predictor in Visual Analytics is beneficial

A review on the visual design styles in data storytelling based on user preferences and personality differences

The proliferation of data analytics has led to a vast application of data visualization and storytelling in a variety of disciplines extending across banking, sports to healthcare. Data, information, and knowledge are transformed into interactive visual representations that convey a meaningful story. In big data analytics, relevant and high-quality graphical insights ought to be factually accurate and relevant to make a key decision. Data storytelling has become an effective way to apply information visualization as it can enhance communication effectiveness. Using visualization as a tool to enhance narrative for the viewers in enforcing data storytelling as a way to understand data and information. Findings suggest that an individual's personality variations correspond strongly with a user's preference toward visual design styles for visualization and storytelling. This paper investigates previous studies regarding personality, information visualization, narrative, and storytelling, as well as their interrelationships through online databases. The futuredirection of the present study

Who benefits from Visualization Adaptations? Towards a better Understanding of the Influence of Visualization Literacy

The ability to read, understand, and comprehend visual information representations is subsumed under the term visualization literacy (VL). One possibility to improve the use of information visualizations is to introduce adaptations. However, it is yet unclear whether people with different VL benefit from adaptations to the same degree. We conducted an online experiment (n = 42) to investigate whether the effect of an adaptation (here: De-Emphasis) of visualizations (bar charts, scatter plots) on performance (accuracy, time) and user experiences depends on users' VL level. Using linear mixed models for the analyses, we found a positive impact of the De-Emphasis adaptation across all conditions, as well as an interaction effect of adaptation and VL on the task completion time for bar charts. This work contributes to a better understanding of the intertwined relationship of VL and visual adaptations and motivates future research.Comment: Preprint and Author Version of a Short Paper, accepted to the 2022 IEEE Visualization Conference (VIS