Old Wine in a New Bottle? Analysis of Visual Lineups with Signal Detection Theory

This position paper critically examines the graphical inference framework for evaluating visualizations using the lineup task. We present a re-analysis of lineup task data using signal detection theory, applying four Bayesian non-linear models to investigate whether color ramps with more color name variation increase false discoveries. Our study utilizes data from Reda and Szafir’s previous work, corroborating their findings while providing additional insights into sensitivity and bias differences across colormaps and individuals. We suggest improvements to lineup study designs and explore the connections between graphical inference, signal detection theory, and statistical decision theory. Our work contributes a more perceptually grounded approach for assessing visualizationeffectiveness and offers a path forward for better aligning graphical inference methods with human cognition. The results have implications for the development and evaluation of visualizations, particularly for exploratory data analysis scenarios. Supplementary materials are available at https://osf.io/xd5cj/.