Dereck Toker

Towards adaptive information visualization: on the influence of user characteristics

The long-term goal of our research is to design information visualization systems that adapt to the specific needs, characteristics, and context of each individual viewer. In order to successfully perform such adaptation, it is crucial to first identify characteristics that influence an individual users effectiveness, efficiency, and satisfaction with a particular information visualization type. In this paper, we present a study that focuses on investigating the impact of four user characteristics (perceptual speed, verbal working memory, visual working memory, and user expertise) on the effectiveness of two common data visualization techniques: bar graphs and radar graphs. Our results show that certain user characteristics do in fact have a significant effect on task efficiency, user preference, and ease of use. We conclude with a discussion of how our findings could be effectively used for an adaptive visualization system.

Evaluating the impact of user characteristics and different layouts on an interactive visualization for decision making

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be designed to better fit each users specific needs. Most studies to date, however, have looked at static visualizations. Studies considering interactive visualizations have only looked at a limited number of user characteristics, and consider either low‐level tasks (e.g., value retrieval), or high‐level tasks (in particular: discovery), but not both. This paper contributes to this line of work by looking at the impact of a large set of user characteristics on user performance with interactive visualizations, for both low and high‐level tasks. We focus on interactive visualizations that support decision making, exemplified by a visualization known as Value Charts. We include in the study two versions of ValueCharts that differ in terms of layout, to ascertain whether layout mediates the impact of individual differences and could be considered as a form of personalization. Our key findings are that (i) performance with low and high‐level tasks is affected by different user characteristics, and (ii) users with low visual working memory perform better with a horizontal layout. We discuss how these findings can inform the provision of personalized support to visualization processing.

Modeling Motivation in a Social Network Game Using Player-Centric Traits and Personality Traits

People are drawn to play different types of videogames and find enjoyment in a range of gameplay experiences. Envisaging a representative game player or persona allows game designers to personalize game content; however, there are many ways to characterize players and little guidance on which approaches best model player behavior and preference. To provide knowledge about how player characteristics contribute to game experience, we investigate how personality traits as well as player styles from the BrianHex model moderate the prediction of player motivation with a social network game. Our results show that several player characteristics impact motivation, expressed in terms of enjoyment and effort. We also show that player enjoyment and effort, as predicted by our models, impact players’ in-game behaviors, illustrating both the predictive power and practical utility of our models for guiding user adaptation.

Te,Te,Hi,Hi: Eye Gaze Sequence Analysis for Informing User-Adaptive Information Visualizations

Information visualization systems have traditionally followed a one-size-fits-all paradigm with respect to their users, i.e., their design is seldom personalized to the specific characteristics of users (e.g. perceptual abilities) or their tasks (e.g. task difficulty). In view of creating information visualization systems that can adapt to each individual user and task, this paper provides an analysis of user eye gaze data aimed at identifying behavioral patterns that are specific to certain user and task groups. In particular, the paper leverages the sequential nature of user eye gaze patterns through differential sequence mining, and successfully identifies a number of pattern differences that could be leveraged by adaptive information visualization systems in order to automatically identify (and consequently adapt to) different user and task characteristics.

Eye Tracking to Understand User Differences in Visualization Processing with Highlighting Interventions

We present an analysis of user gaze data to understand if and how user characteristics impact visual processing of bar charts in the presence of different highlighting interventions designed to facilitate visualization usage. We then link these results to task performance in order to provide insights on how to design user-adaptive information visualization systems. Our results show how the least effective intervention manifests itself as a distractor based on gaze patterns. The results also identify specific visualization regions that cause poor task performance in users with low values of certain cognitive measures, and should therefore be the target of personalized visualization support.

Further Results on Predicting Cognitive Abilities for Adaptive Visualizations

Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye tracking data. Performing this type of user modeling is important for devising user-adaptive visualizations that can adapt to a user’s abilities as needed during the interaction. In this paper, we contribute to previous work by extending the type of visualizations considered and the set of cognitive abilities that can be predicted from gaze data, thus providing evidence on the generality of these findings. We also evaluate how quality of gaze data impacts prediction.

User-adaptive Support for Processing Magazine Style Narrative Visualizations: Identifying User Characteristics that Matter

In this paper we present results from an exploratory user study to uncover which user characteristics (e.g., perceptual speed, verbal working memory, etc.) play a role in how users process textual documents with embedded visualizations (i.e., Magazine Style Narrative Visualizations). We present our findings as a step toward developing user-adaptive support, and provide suggestions on how our results can be leveraged for creating a set of meaningful interventions for future evaluation.

Leveraging Pupil Dilation Measures for Understanding Users' Cognitive Load During Visualization Processing

In this paper we describe a preliminary investigation in using pupil dilation measurements to understand user visualization processing, with the long-term goal of building user-adaptive visualizations that can tailor the presentation of complex visual information to specific user needs and states. In particular, we look at how a selection of pupil dilation measurements are affected by adding several highlighting interventions designed to aid visualization processing to a bar graph.