A Survey on the Role of Individual Differences on Visual Analytics Interactions: Masters Project Report

There is ample evidence in the visualization commu- nity that individual differences matter. These prior works high- light various traits and cognitive abilities that can modulate the use of the visualization systems and demonstrate a measurable influence on speed, accuracy, process, and attention. Perhaps the most important implication of this body of work is that we can use individual differences as a mechanism for estimating people’s potential to effectively leverage visual interfaces or to identify those people who may struggle. As visual literacy and data fluency continue to become essential skills for our everyday lives, we must embrace the growing need to understand the factors that divide our society, and identify concrete steps for bridging this gap. This paper presents the current understanding of how individual differences interact with visualization use and draws from recent research in the Visualization, Human-Computer Interaction, and Psychology communities. We focus on the specific designs and tasks for which there is concrete evidence of performance divergence due to individual characteristics. The purpose of this paper is to underscore the need to consider individual differences when designing and evaluating visualization systems and to call attention to this critical future research direction

Priming Locus of Control to Affect Performance

Recent research suggests that the personality trait Locus of Control (LOC) can be a reliable predictor of performance when learn- ing a new visualization tool. While these results are compelling and have direct implications to visualization design, the relation- ship between a user’s LOC measure and their performance is not well understood. We hypothesize that there is a dependent relation- ship between LOC and performance; specifically, a person’s orientation on the LOC scale directly influences their performance when learning new visualizations. To test this hypothesis, we conduct an experiment with 300 subjects using Amazon’s Mechanical Turk. We adapt techniques from personality psychology to manipulate a user’s LOC so that users are either primed to be more internally or externally oriented on the LOC scale. Replicating previous studies investigating the effect of LOC on performance, we measure users’ speed and accuracy as they use visualizations with varying visual metaphors. Our findings demonstrate that changing a user’s LOC impacts their performance. We find that a change in users’ LOC results in performance changes

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

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

Is Virtual Marriage Acceptable? A Psychological Study Investigating The Role of Ambiguity Tolerance and Intimacy Illusion in Online Dating among Adolescents and Early Adults

Marriage is one of the most important topics in the education field since life in this world is structured by interaction among families and between families and other social institutions. Dissatisfaction and unsustainability of marriage have led the urgency of premarital education in various countries. The problem is that the spread of virtual reality has made marriage itself to become more complex and experience reinterpretation and reconfiguration, moreover with the emergence of new kind of marriage in the digital era, i.e. virtual marriage. Everybody who has observed, known, or even tried, certainly asks the question, “Could (or: should) I accept virtual marriage?” . This study was aimed to investigate the role of tolerance of ambiguity and illusion of intimacy in online dating in predicting the acceptance of virtual marriage. There were 420 adolescents and young adults (212 males, 208 females; Mage=21.10 years old, SDage=1.459 years; 338 students, 82 employees or entrepreneurs) in the Greater Jakarta, Indonesia, participated in this study. It was found that the acceptance was not predicted by the ambiguity tolerance, but by the illusion of intimacy in online dating. The psychometric issues, substantive discussion, and recommendation are presented at the end of this article. The trend of virtual marriage should not be allowed to roll away, by autopilot, without loaded by strategies in designing an online game as one of the pivotal educational technologies that needs to shape appropriate character and attitude for it