Literature DB >> 28406684

Redundant encoding strengthens segmentation and grouping in visual displays of data.

Christine Nothelfer1, Michael Gleicher2, Steven Franconeri1.   

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. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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Year:  2017        PMID: 28406684     DOI: 10.1037/xhp0000314

Source DB:  PubMed          Journal:  J Exp Psychol Hum Percept Perform        ISSN: 0096-1523            Impact factor:   3.332


  4 in total

1.  Space of preattentive shape features.

Authors:  Liqiang Huang
Journal:  J Vis       Date:  2020-04-09       Impact factor: 2.240

2.  Two people, one graph: the effect of rotated viewpoints on accessibility of data visualizations.

Authors:  Tjark Müller; Friedrich W Hesse; Hauke S Meyerhoff
Journal:  Cogn Res Princ Implic       Date:  2021-04-13

3.  Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0.

Authors:  Hasaru Kariyawasam; Shian Su; Oliver Voogd; Matthew E Ritchie; Charity W Law
Journal:  NAR Genom Bioinform       Date:  2021-12-22

4.  The relation between color and spatial structure for interpreting colormap data visualizations.

Authors:  Shannon C Sibrel; Ragini Rathore; Laurent Lessard; Karen B Schloss
Journal:  J Vis       Date:  2020-11-02       Impact factor: 2.240

  4 in total

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