AI 中文总结
该研究构建任务感知调色板推荐系统CatPAL，通过多阶段用户研究和Bradley-Terry模型等，实现适配不同分析任务的分类调色板推荐，提升调色板设计的可靠性。
AI 中文摘要
设计有效的分类调色板需要平衡一系列因素，包括感知辨识度、类别数量和任务有效性。分类编码的有效性会根据目标分析任务有很大差异；然而，现有的推荐工具在评估调色板质量时大多忽略任务上下文，导致在不同任务上性能不一致。我们整合了一系列多阶段用户研究的结果，构建了一个统一模型，用于针对类别数量和七种常见散点图任务的颜色编码、形状编码及其冗余组合的任务有效性建模。我们的结果表明，任务和调色板选择共同影响感知准确性：不同的颜色和形状调色板在不同任务中表现出不同的稳健性，表明调色板有效性具有任务依赖性。我们使用Bradley-Terry模型估算39种颜色和39种形状的特定任务感知强度，并通过自适应采样优化以针对不确定和任务敏感的比较。我们进一步使用冗余增益Delta G指标量化跨通道交互，以对颜色和形状配对的性能进行建模。随后我们训练了一个预测模型，该模型基于任务、类别数量和感知特征对候选调色板进行评分。该模型在CatPAL中实现了有效的调色板推荐，CatPAL是一个基于经验数据、响应用户约束的任务感知调色板推荐系统。我们的研究结果强调了选择与特定分析任务匹配的分类调色板的重要性，并证明了任务感知建模如何实现更可靠的调色板设计。CatPAL将经验结果转化为实用工具，支持用户指定颜色或形状，并返回适用于多种任务的排名调色板推荐。
英文摘要
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.
