Eye tracking on visualizations: Progressive extraction of scanning strategies

Abstract

Eye tracking methods can be used to help understand what people think about as they search for or compare visual information, such as data displayed in graphs, charts, and other visualizations. This chapter, intended for the visualization researcher who is new to eye tracking, focuses on decision making during data analysis. It steps through the progressive data abstraction that is required during analysis, describing both resources needed and results found. Examples are taken from a study that presented two graph types (linear, radial) in three styles (bar, line, area) to 32 participants. Tasks comprised comparisons between two data series on each graph, across 1-8 categorical dimensions. Analysis proceeded from creating visualizations (Level 0), to statistical factor effects (Level 1), to adding Areas of Interest (Level 2), to comparing scanning sequences (Level 3). Objective, metric- based analysis is advocated throughout, but deeper analyses (Level 3) can be tedious and resource-demanding. Research and new product needs are highlighted.