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dc.contributor.authorReda, Khairien_US
dc.contributor.authorSalvi, Amey A.en_US
dc.contributor.authorGray, Jacken_US
dc.contributor.authorPapka, Michael E.en_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:01:19Z
dc.date.available2021-06-12T11:01:19Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14288
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14288
dc.description.abstractColor encoding is foundational to visualizing quantitative data. Guidelines for colormap design have traditionally emphasized perceptual principles, such as order and uniformity. However, colors also evoke cognitive and linguistic associations whose role in data interpretation remains underexplored. We study how two linguistic factors, name salience and name variation, affect people's ability to draw inferences from spatial visualizations. In two experiments, we found that participants are better at interpreting visualizations when viewing colors with more salient names (e.g., prototypical 'blue', 'yellow', and 'red' over 'teal', 'beige', and 'maroon'). The effect was robust across four visualization types, but was more pronounced in continuous (e.g., smooth geographical maps) than in similar discrete representations (e.g., choropleths). Participants' accuracy also improved as the number of nameable colors increased, although the latter had a less robust effect. Our findings suggest that color nameability is an important design consideration for quantitative colormaps, and may even outweigh traditional perceptual metrics. In particular, we found that the linguistic associations of color are a better predictor of performance than the perceptual properties of those colors. We discuss the implications and outline research opportunities. The data and materials for this study are available at https://osf.io/asb7nen_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman centered computing
dc.subjectEmpirical studies in visualization
dc.titleColor Nameability Predicts Inference Accuracy in Spatial Visualizationsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersBest Papers
dc.description.volume40
dc.description.number3
dc.identifier.doi10.1111/cgf.14288
dc.identifier.pages49-60


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  • 40-Issue 3
    EuroVis 2021 - Conference Proceedings

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