Data-Visual Relationships to Subject Performance and Eye Movements

Abstract

Visual communication is ubiquitous, commanding our attention and commandeering our inattention. The presentation of information can take myriad visual forms, such as bar charts, scatter plots, network diagrams, and tables. These information graphics are attempts to map potentially large amounts of complex data to easily navigable visual form for rapid and accurate knowledge transfer. However, there is not yet a satisfactory formal methodology for selecting the most appropriate visualization method for a given set of data. A data taxonomy and novel visual taxonomy will be used to select visual stimuli from a database of acquired and newly generated information graphics. Oculomotor responses (eye tracking data) and task-based responses (mouse clicks or keyboard input) are recorded; performance on the latter is used to establish an expert subgroup. These results will be used to satisfy the three primary objectives of the proposed research, determining: how the choice of data visualization impacts oculomotor behavior and task performance, if this behavior is discriminable between experts and novices, and an empirically-based taxonomy of visualization based on the results of 1 and 2. Intellectual merit of the proposed activity The proposed research will create a novel taxonomy for and database of acquired and generated information graphics as well as an associated web application to search, organize, and compare entries in the database. Part of this research program is intended to establish the most comprehensive, manually annotated (and taxonomically classified) information graphics database in the world, for use by the public via a web interface. These images will be important for procuring stimuli for other kinds of perceptual and cognitive psychology experiments. The eye tracking and task performance results should help lead to a better understanding of how humans look at data, respond to the relationship between data structures and visual composition, and respond differentially to visualizations of different types. With respect to qualifications, the PI has a background in brain imaging research, image processing, and programming applications for generating graphs. Through his collaborator Dr. Ferrera of Columbia University, he has access to facilities and faculty specialized in eye tracking and psychophysics research. Collaborator Dr. Michelle Zhou, a research manager at IBM T. J. Watson Research Center, has years of experience in the areas of data and visual taxonomies, image databases, and automated generation of information graphics [Zhou and Feiner 1998, Zhou et al. 2002b, Zhou et al. 2002a]. Broader impacts of the proposed activity In addition to contributions the image taxonomy, database, and web application are intended to make to research, they will serve as a rich resource for teaching about the history and scope of visualization methods and design within and across disciplines, and for the general public with an interest in information graphics. The research will be conducted on subjects of varied background and race and will be broadly disseminated via websites in addition to publications. Additionally, defining a visual taxonomy will inform design choices made in information visualization. One implication of this research is a determination of how effective different visualization methods are at conveying information; this understanding will be of profound help to anyone interested in conveying information effectively in a graphical form.