Impact of personality factors on interface interaction and the development of user profiles: Next steps in the personal equation of interaction

Abstract

These current comparative studies explore the impact of individual differences in personality factors on interface interaction and learning performance behaviors in both an interactive visualization and a menu-driven web table in two studies. Participants were administered three psychometric measures designed to assess Locus of Control, Big Five Extraversion, and Big Five Neuroticism. Participants were then asked to complete procedural learning tasks in each interface. Results demonstrated that all three measures predicted completion times. Additionally, analyses demonstrated that personality factors also predicted the number of insights participants reported while completing the tasks in each interface. Furthermore, we used the psychometric findings in conjunction with a follow-up psychometric survey with a further 50 participants to build initial user profiles based on the cognitive task being undertaken. We discuss how these findings advance our ongoing research in the Personal Equation of Interaction.

Individual user characteristics and information visualization

“…For example, Ziemkiewicz et al [31] and Green and Fisher [15], looked at the influence of personality traits, showing that locus of control impacts performance across different visualizations. Cognitive measures such as perceptual speed and visual memory have been shown to influence a user's ability to complete a task effectively [3,30].…”

“…However, recent research has shown that individual differences can indeed have a significant impact on task effectiveness and user satisfaction during Infovis usage. For example, personality traits have been found to impact a user's performance with different Infovis designs [31,15]. Velez et al [30] found that a user's abilities for spatial reasoning (e.g., spatial orientation) were correlated with visualization comprehension.…”

The Personal Equation of Complex Individual Cognition during Visual Interface Interaction

“…The PEI has three current and future end goals: the prediction of analytical performance based on inherent differences [15], [16], the ability to inform real-time interface individuation, and the creation of fuller-bodied user profiles, broken down by the reasoning tasks performed [17].…”

“…Not surprisingly, this research is currently quite fluid, and continues to inform the second goal of what matrices will be needed to support real-time interface adaptation. In addition, having hundred of participants complete these studies has allowed us to sketch out initial user profiles, or describe inherent characteristics of a user based how the user performs on an analytical task [17].…”

Survey on Individual Differences in Visualization

“…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.…”