Perception, Cognition, and Effectiveness of Visualizations with Applications in Science and Engineering

Visualization is a powerful tool for data exploration and analysis. With data ever-increasing in quantity and becoming integrated into our daily lives, having effective visualizations is necessary. But how does one design an effective visualization? To answer this question we need to understand how humans perceive, process, and understand visualizations. Through visualization evaluation studies we can gain deeper insight into the basic perception and cognition theory of visualizations, both through domain-specific case studies as well as generalized laboratory experiments.;This dissertation presents the results of four evaluation studies, each of which contributes new knowledge to the theory of perception and cognition of visualizations. The results of these studies include a deeper clearer understanding of how color, data representation dimensionality, spatial layout, and visual complexity affect a visualization's effectiveness, as well as how visualization types and visual attributes affect the memorability of a visualization.;We first present the results of two domain-specific case study evaluations. The first study is in the field of biomedicine in which we developed a new heart disease diagnostic tool, and conducted a study to evaluate the effectiveness of 2D versus 3D data representations as well as color maps. In the second study, we developed a new visualization tool for filesystem provenance data with applications in computer science and the sciences more broadly. We additionally developed a new time-based hierarchical node grouping method. We then conducted a study to evaluate the effectiveness of the new tool with its radial layout versus the conventional node-link diagram, and the new node grouping method. Finally, we discuss the results of two generalized studies designed to understand what makes a visualization memorable. In the first evaluation we focused on visualization memorability and conducted an online study using Amazon's Mechanical Turk with hundreds of users and thousands of visualizations. For the second evaluation we designed an eye-tracking laboratory study to gain insight into precisely which elements of a visualization contribute to memorability as well as visualization recognition and recall.