Alvitta Ottley

Using fNIRS brain sensing to evaluate information visualization interfaces

We show how brain sensing can lend insight to the evaluation of visual interfaces and establish a role for fNIRS in visualization. Research suggests that the evaluation of visual design benefits by going beyond performance measures or questionnaires to measurements of the users cognitive state. Unfortunately, objectively and unobtrusively monitoring the brain is difficult. While functional near-infrared spectroscopy (fNIRS) has emerged as a practical brain sensing technology in HCI, visual tasks often rely on the brains quick, massively parallel visual system, which may be inaccessible to this measurement. It is unknown whether fNIRS can distinguish differences in cognitive state that derive from visual design alone. In this paper, we use the classic comparison of bar graphs and pie charts to test the viability of fNIRS for measuring the impact of a visual design on the brain. Our results demonstrate that we can indeed measure this impact, and furthermore measurements indicate that there are not universal differences in bar graphs and pie charts.

Understanding Visualization by Understanding Individual Users

Visualization is often seen as a tool to support complex thinking. Although different people can have very different ways of approaching the kind of complex task that visualizations support, as researchers and designers we still rarely consider individual differences in creating and evaluating visualizations. This article reviews recent research on individual differences in visualization and human-computer interaction, showing that both cognitive abilities and personality profiles might significantly affect performance with these tools. The study of individual differences has led to the conclusion that advances in this important area in visualization will require more focused research. Specifically, we must isolate the cognitive factors that are relevant to visualization and the design factors that make one visualization more suited to a user than another. In doing so, we could increase our understanding of the visualization user and reshape how we approach design and evaluation.

Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability

Decades of research have repeatedly shown that people perform poorly at estimating and understanding conditional probabilities that are inherent in Bayesian reasoning problems. Yet in the medical domain, both physicians and patients make daily, life-critical judgments based on conditional probability. Although there have been a number of attempts to develop more effective ways to facilitate Bayesian reasoning, reports of these findings tend to be inconsistent and sometimes even contradictory. For instance, the reported accuracies for individuals being able to correctly estimate conditional probability range from 6% to 62%. In this work, we show that problem representation can significantly affect accuracies. By controlling the amount of information presented to the user, we demonstrate how text and visualization designs can increase overall accuracies to as high as 77%. Additionally, we found that for users with high spatial ability, our designs can further improve their accuracies to as high as 100%. By and large, our findings provide explanations for the inconsistent reports on accuracy in Bayesian reasoning tasks and show a significant improvement over existing methods. We believe that these findings can have immediate impact on risk communication in health-related fields.

Personality as a Predictor of User Strategy: How Locus of Control Affects Search Strategies on Tree Visualizations

Individual differences matter. While this has been the theme for many recent works in the Visualization and HCI communities, the mystery of how to develop personalized visualizations remains. This is largely because very little is known about how users actually use visualizations to solve problems and even less is known about how individual differences affect these problem-solving strategies. In this paper, we provide evidence that strategies are indeed influenced by individual differences. We demonstrate how the personality trait locus of control impacts strategies on hierarchical visualizations, and we introduce design recommendations for personalized visualizations.

Balancing Human and Machine Contributions in Human Computation Systems

Many interesting and successful human computation systems leverage the complementary computational strengths of both humans and machines to solve these problems. In this chapter, we examine Human Computation as a type of Human-Computer Collaboration—collaboration involving at least one human and at least one computational agent. We discuss recent advances in the open area of function allocation, and explore how to balance the contributions of humans and machines in computational systems. We then explore how human-computer collaborative strategies can be used to solve problems that are difficult or computationally infeasible for computers or humans alone.

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.