Exploring the role of individual differences in information visualization

Towards Adaptive Information Visualization: On the Influence of User Characteristics

“…The following points summarize the findings from this analysis: . This result confirms previous findings that differences in cognitive measures can impact general visualization effectiveness and, like in [7], it singles out perceptual speed as a relevant measure.  There is a medium-large significant interaction effect between visualization type and perceptual speed, F(1,20) = 4.49, p < .05 ηp²= 0.18.…”

“…Even though completion time is always faster with the bar graph, the difference in time performance between bar and radar decreases as a user's perceptual speed increases (See Figure 2left). This result is important because it confirms the finding in [7] that perceptual speed is a cognitive measure that can impact the compared effectiveness of two different visualizations, at least when one of them is a radar graph.  There is a large significant interaction effect between visualization type and visualization order, F(1,20) = 8.66, p < .01, ηp²= 0.30.…”

“…Other cognitive traits too have also been shown to have a strong influence on users' performance. The study by Conati and Maclaren [7] looked at two different visualizations to represent changes in a set of variables: a radar graph and a Multiscale Dimension Visualizer (MDV); a visualization that primarily uses color hue and intensity to represent change direction and magnitude [23]. They found that: (1) a user's perceptual speed was a significant predictor of which of the two visualizations would work better for that user on a specific comparison task, and (2) both perceptual speed and visual spatial working memory were predictors of performance with each visualization for some of the study's tasks.…”

“…They were also the only two in the set for which [7] found significant relationships with visualization effectiveness when comparing radar graphs and Multiscale Dimension Visualizer (MDV). Verbal working memory was selected because it may affect performance in processing the textual components of a visualization, which, in our study, include legends, labels, and task descriptions.…”

“…We believe this may be attributed to the fact that existing visualizations are designed mostly around the target data set and associated task model, with little consideration for user differences. Both long term user characteristics (e.g., cognitive abilities and expertise) and short term factors (e.g., cognitive load and attention) have often been overlooked in the design of information visualizations, despite studies linking individual differences to visualization efficacy for search and navigation tasks [1,8], for information seeking tasks [7,25], as well as anecdotal evidence of diverse personal visualization preferences [3]. Our long term goal is to explore the possibilities of user-centered visualizations, which understand that different users have different visualization needs and abilities, and which can adapt to these differences.…”