Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco
Zehua Zeng, Junran Yang, Dominik Moritz, Jeffrey Heer, Leilani Battle

TL;DR
This paper demonstrates how integrating a large body of graphical perception research into a framework can improve visualization recommendation algorithms and reveal gaps and biases in existing literature.
Contribution
It introduces a pipeline that models graphical perception results from 30 studies to enhance and analyze visualization recommendation algorithms.
Findings
Identifies gaps in graphical perception literature affecting recommendations
Clusters papers by design rules and constraints
Analyzes influence of different perception studies on recommendations
Abstract
Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a pipeline of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an exploratory study to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco -- a framework to model visualization knowledge -- to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to (1) identify gaps in…
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Taxonomy
TopicsData Visualization and Analytics · Color perception and design
