CatPAL: Task-Aware Learning for Categorical Palette Recommendation
2026-08-10 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied how well different colors and shapes help people understand data when used in charts, especially scatterplots. They found that which colors and shapes work best depends on the specific task people are doing. By testing many color and shape combinations, they created a model that predicts how easy it is to tell categories apart for different tasks. They then built CatPAL, a tool that suggests the best color and shape palettes based on the task and number of categories, improving how clearly data can be shown. This work shows it's important to pick palettes that fit the task to make data easier to understand.
categorical paletteperceptual distinctivenessscatterplottask-based effectivenessBradley-Terry modeladaptive samplingredundant encodingcolor-shape interactionpalette recommendationvisualization design
Authors
Chin Tseng, Arran Zeyu Wang, Yunqi Li, Danielle Albers Szafir
Abstract
Designing effective categorical palettes requires balancing a range of factors, including perceptual distinctiveness, category count, and task effectiveness. The effectiveness of categorical encodings can vary substantially depending on the target analytical tasks; however, existing recommendation tools largely ignore task context when evaluating palette quality, resulting in inconsistent performance across tasks. We synthesize findings from a series of multi-stage user studies into a unified model of task-based effectiveness for color encodings, shape encodings, and their redundant combination across category counts and seven common scatterplot tasks. Our results show that task and palette choice jointly influence perceptual accuracy: different color and shape palettes exhibit varying levels of robustness across tasks, indicating that palette effectiveness is task-dependent. We estimate task-specific perceptual strengths for 39 colors and 39 shapes using Bradley-Terry models, refined through adaptive sampling to target uncertain and task-sensitive comparisons. We further quantify cross-channel interactions using a redundant gain Delta G metric to model performance across color and shape pairings. We then train a predictive model that scores candidate palettes based on task, category count, and perceptual features. This model drives effective palette recommendations in CatPAL, a task-aware palette recommendation system grounded in empirical data responsive to user constraints. Our findings highlight the importance of selecting categorical palettes aligned with specific analytical tasks and demonstrate how task-aware modeling enables more reliable palette design. CatPAL translates empirical results into a practical tool that supports user-specified colors or shapes and returns ranked palette recommendations adaptable to a range of tasks.