Redundant Encoding Strengthens Segmentation and Grouping in Visual Displays of Data

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Abstract

The availability and importance of data are 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. 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 rapidly segmenting objects within a dense display (Experiments 1 and 2), and strengthening visual grouping of display elements (Experiment 3). We predict situations where this benefit might be present, and discuss implications for models of attentional control.

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