TY - JOUR
T1 - Perceptual Biases in Font Size as a Data Encoding
AU - Alexander, Eric Carlson
AU - Chang, Chih Ching
AU - Shimabukuro, Mariana
AU - Franconeri, Steven
AU - Collins, Christopher
AU - Gleicher, Michael
N1 - Funding Information:
This work was supported in part by US National Science Foundation awards IIS-1162037 and IIS-1162067, a grant from the Andrew W. Mellon Foundation, and funding from NSERC and the Canada Research Chairs program.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - Many visualizations, including word clouds, cartographic labels, and word trees, encode data within the sizes of fonts. While font size can be an intuitive dimension for the viewer, using it as an encoding can introduce factors that may bias the perception of the underlying values. Viewers might conflate the size of a word's font with a word's length, the number of letters it contains, or with the larger or smaller heights of particular characters ('o' versus 'p' versus 'b'). We present a collection of empirical studies showing that such factors - which are irrelevant to the encoded values - can indeed influence comparative judgements of font size, though less than conventional wisdom might suggest. We highlight the largest potential biases, and describe a strategy to mitigate them.
AB - Many visualizations, including word clouds, cartographic labels, and word trees, encode data within the sizes of fonts. While font size can be an intuitive dimension for the viewer, using it as an encoding can introduce factors that may bias the perception of the underlying values. Viewers might conflate the size of a word's font with a word's length, the number of letters it contains, or with the larger or smaller heights of particular characters ('o' versus 'p' versus 'b'). We present a collection of empirical studies showing that such factors - which are irrelevant to the encoded values - can indeed influence comparative judgements of font size, though less than conventional wisdom might suggest. We highlight the largest potential biases, and describe a strategy to mitigate them.
KW - Text and document data
KW - cognitive and perceptual skill
KW - quantitative evaluation
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U2 - 10.1109/TVCG.2017.2723397
DO - 10.1109/TVCG.2017.2723397
M3 - Article
C2 - 28692979
AN - SCOPUS:85023201732
SN - 1077-2626
VL - 24
SP - 2397
EP - 2410
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 8
ER -