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Findings from graphical perception can guide visualization recommendationalgorithms in identifying effective visualization designs. However, existingalgorithms use knowledge from, at best, a few studies, limiting ourunderstanding of how complementary (or contradictory) graphical perceptionresults influence generated recommendations. In this paper, we present apipeline of applying a large body of graphical perception results to developnew visualization recommendation algorithms and conduct an exploratory study toinvestigate how results from graphical perception can alter the behavior ofdownstream algorithms. Specifically, we model graphical perception results from30 papers in Draco -- a framework to model visualization knowledge -- todevelop new recommendation algorithms. By analyzing Draco-generated algorithms,we showcase the feasibility of our method to (1) identify gaps in existinggraphical perception literature informing recommendation algorithms, (2)cluster papers by their preferred design rules and constraints, and (3)investigate why certain studies can dominate Draco's recommendations, whereasothers may have little influence. Given our findings, we discuss the potentialfor mutually reinforcing advancements in graphical perception and visualizationrecommendation research.

Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco