Rethinking the Ranks of Visual Channels
Caitlyn M. McColeman, Fumeng Yang, Steven Franconeri, Timothy F. Brady

TL;DR
This study challenges existing visual channel rankings by testing how well they support immediate memory tasks across different numbers of marks, revealing that rankings vary and are influenced by factors beyond channel choice.
Contribution
It introduces a new approach to ranking visual channels based on memory reproduction tasks and demonstrates that traditional rankings do not hold across different numbers of marks or tasks.
Findings
Rankings of visual channels vary with number of marks and task context.
Memory errors increase with more marks, especially at 8 marks.
Factors like number of values and value size significantly affect performance.
Abstract
Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is little existing work testing assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values. We asked participants to immediately reproduce a set of values from memory. With a Bayesian multilevel modeling approach, we observed how the relevant rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision,…
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