Representing Information Collections for Visual Cognition

Abstract

The importance of digital information collections is growing. Collections are typically represented with text-only, in a linear list format, which turns out to be a weak representation for cognition. We learned this from empirical research in cognitive psychology, and by conducting a study to develop an understanding of current practices and resulting breakdowns in human experiences of building and utilizing collections. Because of limited human attention and memory, participants had trouble finding specific elements in their collections, resulting in low levels of collection utilization. To address these issues, this research develops new collection representations for visual cognition. First, we present the image+text surrogate, a concise representation for a document, or portion thereof, which is easy to understand and think about. An information extraction algorithm is developed to automatically transform a document into a small set of image+text surrogates. After refinement, the average accuracy performance of the algorithm was 90%. Then, we introduce the composition space to represent collections, which helps people connect elements visually in a spatial format. To ensure diverse information from multiple sources to be presented evenly in the composition space, we developed a new control structure, the ResultDis- tributor. A user study has demonstrated that the participants were able to browse more diverse information using the ResultDistributor-enhanced composition space. Participants also found it easier and more entertaining to browse information in this representation. This research is applicable to represent the information resources in contexts such as search engines or digital libraries. The better representation will enhance the cognitive efficacy and enjoyment of people’s everyday tasks of information searching, browsing, collecting, and discovering.