Correlation Judgment and Visualization Features: A Comparative Study
Yang F, Harrison LT, Rensink RA, Franconeri SL, and Chang R
IEEE VIS, Berlin, Germany. [Oct 2018] [web]
Recent visualization research efforts have incorporated experimental techniques and perceptual models from the vision science community. Perceptual models such as Weber’s law, for example, have been used to model the perception of correlation in scatterplots. While this thread of research has progressively refined a model of the perception of correlation in scatterplots, it remains unclear as to why the perception of correlation in scatterplots can be modeled using relatively simple functions, e.g., linear and log-linear. In this paper, we investigate a longstanding hypothesis that people use visual features in a chart as a proxy for statistical measures like correlation. For a given scatterplot, we extract 49 candidate visual features and apply a set of metrics to evaluate which features best align with existing models and participant judgments. The results support the hypothesis that people attend to a small number of visual features when discriminating correlation in scatterplots. We discuss how this result may account for prior conflicting findings, and how visual features provide a baseline for future model-based approaches in visualization evaluation and design.