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Redundant encoding strengthens segmentation and grouping in visual displays of data.
Author(s) -
Christine Nothelfer,
Michael Gleicher,
Steven Franconeri
Publication year - 2017
Publication title -
journal of experimental psychology human perception and performance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.691
H-Index - 148
eISSN - 1939-1277
pISSN - 0096-1523
DOI - 10.1037/xhp0000314
Subject(s) - computer science , visualization , segmentation , feature (linguistics) , data visualization , encoding (memory) , artificial intelligence , perception , field (mathematics) , psycinfo , data mining , psychology , mathematics , philosophy , linguistics , medline , neuroscience , political science , pure mathematics , law
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. (PsycINFO Database Record

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