AI 中文总结
本文指出图形感知研究的缺陷，提出将可视化建模为图像、用人类视觉计算模型评估的新范式，复现散点图可辨别性预测结果并规划相关研究议程。
AI 中文摘要
图形感知研究是可视化领域用于评估可视化的首选工具，通过测量人们解释视觉标记和通道排列的准确度，旨在建立视觉编码的最佳实践。本文认为该模型存在根本性缺陷，再多的额外实证研究也无法修复。可视化理论在编码器层面定义有效性：即在特定情境下，哪种数据到视觉的映射效果最佳。然而，人类感知在视网膜图像层面作为根本不同的解码器运作。这种编码器-解码器不对称性意味着实验结果和指南可能无法很好地预测感知性能。此外，到达视觉系统的图像由编码规则、输入数据和微设计参数之间的相互作用产生，这些因素在很大程度上是编码理论无法察觉的。因此，即使名义编码保持不变，数据分布的微小变化或设计变体也会显著改变感知。本文主张应将可视化作为图像进行研究，并使用以像素为输入的人类视觉计算模型进行评估，这类模型能捕捉视觉系统实际构建的感知表征，将评估转向解码器而非抽象编码规范。该方法具有可扩展性、以人类为基础，且对编码理论和图形感知研究都遗漏的涌现图像属性敏感。本文首先描述当前范式的缺陷，提出基于视觉总结统计解释的可视化感知理论；接着展示基于图像的视觉模型如何预测散点图中可视化的可辨别性，同时复现已确立的结果；最后概述基于视觉的可视化评估的研究议程。
英文摘要
Graphical perception studies are the visualization community's preferred tool for evaluating visualizations. By measuring how accurately people interpret arrangements of visual marks and channels, they aim to establish best practices for visual encoding. We argue that this model is fundamentally flawed, and no amount of additional empirical studies will fix it. Visualization theory frames effectiveness at the level of the encoder: which data-to-visual mappings work best in a given context. Human perception, however, operates as a fundamentally different decoder at the level of retinal images. This encoder-decoder asymmetry means that experimental results and guidelines can be poor predictors of perceptual performance. Moreover, the image reaching the visual system emerges from interactions among encoding rules, input data, and micro-design parameters--factors largely invisible to encoding theory. Consequently, small changes in data distributions or design variations can substantially alter perception even when the nominal encoding remains unchanged. We argue that visualizations should instead be studied as images and evaluated using computational models of human vision that take pixels as input. Such models capture the perceptual representations the visual system actually constructs, shifting evaluation toward the decoder rather than abstract encoding specifications. This approach is scalable, human-grounded, and sensitive to emergent image properties that both encoding theory and graphical perception studies miss. We first describe weaknesses of the current paradigm and propose a theory of visualization perception grounded in summary-statistical accounts of vision. We then show how image-based vision models can predict visualization discriminability in scatterplots while reproducing established results. We close by outlining a research agenda for vision-based visualization evaluation.
CommentsTo appear in BELIV 2026 workshop (in conjunction with IEEE VIS'26)
