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

Understanding the structure of information visualization through visual metaphors

Information visualization is an increasingly widespread way to present and analyze complex data, but there is much we still do not know about how people understand vi- sually presented information. Every visualization contains certain assumptions about the structure of its information: how the data can be broken down into pieces, how those pieces relate to one another, what actions can and cannot be performed with the data, and so forth. Yet information visualization still lacks the language and the- ory to analyze these properties of visual information structure. I propose that these structural properties can be thought of as visual metaphors that drive a visualization, analogous to the verbal metaphors that structure abstract information in speech and writing. In this model, people analyze visual relationships among shapes and patterns in a visualization in the same way that they analyze other kinds of visual scenes, then metaphorically interpret those visual relationships as conceptual relationships. I have grounded this proposed model through empirical studies showing how metaphors af- fect visualization use and how minor structural changes can have significant effects on the way people interpret visual information. I argue that this framework sheds new light on the importance of design and conceptual structure in visualization and can substantially improve future techniques and evaluation

Truncating the Y-Axis: Threat or Menace?

Bar charts with y-axes that don't begin at zero can visually exaggerate effect sizes. However, advice for whether or not to truncate the y-axis can be equivocal for other visualization types. In this paper we present examples of visualizations where this y-axis truncation can be beneficial as well as harmful, depending on the communicative and analytic intent. We also present the results of a series of crowd-sourced experiments in which we examine how y-axis truncation impacts subjective effect size across visualization types, and we explore alternative designs that more directly alert viewers to this truncation. We find that the subjective impact of axis truncation is persistent across visualizations designs, even for designs with explicit visual cues that indicate truncation has taken place. We suggest that designers consider the scale of the meaningful effect sizes and variation they intend to communicate, regardless of the visual encoding

Do Mechanical Turks Dream of Square Pie Charts?

Online studies are an attractive alternative to the laborintensive lab study, and promise the possibility of reaching a larger variety and number of people than at a typical university. There are also a number of draw-backs, however, that have made these studies largely impractical so far. Amazon’s Mechanical Turk is a web service that facilitates the assignment of small, web-based tasks to a large pool of anonymous workers. We used it to conduct several perception and cognition studies, one of which was identical to a previous study performed in our lab. We report on our experiences and present ways to avoid common problems by taking them into account in the study design, and taking advantage of Mechanical Turk’s features