Visual Selection in Data VisualizationsPublic Deposited
The availability and importance of data is accelerating, and our visual system is a critical tool for understanding it. The research field of data visualization seeks design guidelines – often inspired by perceptual psychology – for more efficient visual data analysis. Data visualization can borrow phenomena, tasks, and methods of quantifying performance from perceptual psychology to guide effective visualization techniques and design practices. Perceptual psychology, on the other hand, benefits from such work by gaining new insight into the human visual system through new research questions rooted in real world problems. In Section 1, we evaluated a common guideline: when presenting multiple sets of values to a viewer, those sets should be distinguished not just by a single feature, such as color, but redundantly by multiple features, such as color and shape. Despite the broad use of this practice across maps and graphs, it may carry costs, and there is no direct evidence for a benefit. We show that this practice can indeed yield a large benefit for segmenting objects within a dense display in terms of accuracy of selection (Experiments 1 and 2), visual grouping strength of display elements (Experiment 3), and speed of selection in both abstract (Experiment 4) and realistic (Experiment 5) displays. These results show that the visual system can successfully attend to two visual features at once. The visual system can select objects with such features with greater precision and speed than when selecting objects defined by only one visual feature, and all despite the additional visual complexity. In Section 2, we explored how different ways of encoding pairs of data values can lead to vast differences in the efficiency of visually processing the relations between those pairs. We show that perceiving relations between individually-depicted data values leads to a highly inefficient process (Experiments 1a, 1b, 3), which makes it challenging to discriminate both the proportion of opposing relations (Experiments 2 and 4) and the magnitude of relations (Experiment 5). Considering the ubiquity of bar charts and dot plots, relation perception for individual data values is highly inefficient. We show that relations are extracted from pairs of objects in a serial manner. Our results also demonstrate for the first time that it is possible to ensemble code magnitude differences across object pairs, despite how serial this process is when searching for a single relation, and that relation perception is more efficient within an object than between two separate objects. Together, these studies reveal the strengths (Section 1) and weaknesses (Section 2) of visual attention while holding implications for encoding guidelines for data visualization for a range of tasks.