AI 中文总结
该研究针对通道有效性评估的缺口，通过原始视觉刺激的众包实验，揭示通道有效性具多维度特性，发现准确性与预注意检测的分离，提出锚定调和韦伯模型，形成情境驱动的通道选择视角。
AI 中文摘要
已有的通道有效性排名主要评估完整图表场景下的幅度估计准确性，往往忽略可辨别性、可分性和弹出等其他感知任务。为解决这一缺口，我们采用原始视觉刺激（一组无图表特定支架的视觉标记，用于分离通道级变化），对7种核心视觉通道（位置、长度、倾斜度、面积、曲率、亮度和饱和度）开展众包实验。我们在4项感知任务（准确性、可辨别性、可分性和弹出）中评估这些通道，发现通道有效性本质上是多维度的，排名会随任务发生显著变化。例如，空间通道虽保持整体优势，但准确性高度依赖是否存在固定空间锚点；可辨别性随通道和数值范围变化剧烈，我们用新提出的锚定调和韦伯模型将此模式形式化；通道间的成对交互通常具有强不对称性。最后，我们发现估计准确性与预注意检测存在分离：长度的检测有效性仅为中等，却拥有顶级准确性；面积虽定量准确性较差，却实现了最高检测率，不过后者的优势可能部分反映了刺激级线索。我们将这些发现综合为情境驱动的视角，用于上下文敏感的通道选择。
英文摘要
Established channel effectiveness rankings primarily assess magnitude estimation accuracy in complete chart contexts, often neglecting other perceptual tasks such as discriminability, separability, and pop-out. To address this gap, we conducted crowdsourced experiments on seven core visual channels (position, length, tilt, area, curvature, luminance, and saturation) using primitive visual stimuli, a set of visual marks without chart-specific scaffolding to isolate channel-level variation. We evaluated these channels across four perceptual tasks (accuracy, discriminability, separability, and pop-out) and found that channel effectiveness is fundamentally multi-dimensional, with rankings shifting substantially across tasks. For instance, while spatial channels maintain an overall advantage, accuracy depends strongly on whether a fixed spatial anchor is available. Discriminability varies dramatically across channels and value ranges, a pattern we formalized with a novel Anchored Harmonic Weber model. Pairwise channel interactions are often strongly asymmetric. Finally, we identify a dissociation between estimation accuracy and preattentive detection: length shows only moderate detection effectiveness despite top-tier accuracy, while area achieves the highest detection rates despite poor quantitative accuracy, though the latter advantage may partly reflect stimulus-level cues. We synthesize these findings into a scenario-driven perspective for context-sensitive channel selection.
CommentsAccepted to IEEE VIS 2026; to appear in IEEE Transactions on Visualization and Computer Graphics. 9 pages (11 with references) plus supplementary material
