AI 中文总结
本研究聚焦颜色，验证了可通过聚合单个颜色情感关联（可加性）及结合颜色频率（数据依赖性）预测可视化情感内涵，该方法可推广至散点图等，为自动化情感可视化设计提供支撑。
AI 中文摘要
越来越多的证据表明，情感内涵（情感关联）是视觉传达的重要方面，因此需要能够预测可视化情感内涵的方法。可视化设计的许多方面，包括颜色、纹理和形状，都可能影响情感内涵，关键问题在于多种设计属性如何结合以确定整个可视化的情感关联。本研究专门聚焦于颜色，测试是否可以通过聚合单个组成颜色的情感关联（可加性假设）来预测整个可视化的情感内涵；还测试是否考虑由底层数据集确定的有色区域大小能改进预测（数据依赖性假设）。研究发现，对于颜色在色标中分布均匀的色图数据可视化，单个颜色的平均估计关联能有效预测色图整体的情感关联（可加性；实验1）；对于底层数据集偏向映射到色标一端颜色的色图，通过考虑色图中颜色频率的加权平均能更好地预测情感关联（数据依赖性；实验2）。可加性和数据依赖性的效应可推广到散点图和条形图（实验3）。这些结果表明，从单个设计组件预测整个可视化的情感内涵是可行的，这对自动化情感可视化设计以支持视觉传达具有重要意义。
英文摘要
With increasing evidence that affective connotation (emotional association) is an important aspect of visual communication, there is a need for methods to predict affective connotation of visualizations. Many aspects of visualization design, including colors, textures, and shapes, can contribute to affective connotation, and a key question is how multiple design properties combine to determine the emotion association of a whole visualization. In this study, we focused specifically on color and tested whether it is possible to predict the affective connotation of whole visualizations by aggregating the emotion associations of the individual, constituent colors (additivity hypothesis). We also tested whether accounting for the size of colored regions, as determined by the underlying dataset, improved predictions (data-dependence hypothesis). We found that for colormap data visualizations in which colors were well-distributed across all colors in the color scale, the mean estimated associations of individual colors effectively predicted emotional associations of the maps as a whole (additivity; Exp. 1). For colormaps whose underlying datasets were biased to map more to colors at one end of the color scale, emotional associations were better predicted by a weighted mean that accounted for color frequency in the colormap (data-dependence; Exp. 2). Effects of additivity and data-dependence generalized to dot plots and bar charts (Exp. 3). These results suggest it is viable to predict affective connotation of whole visualizations from their individual design components, which has important implications for automating affective visualization design to support visual communication.
CommentsTo be published in IEEE Transactions on Visualization and Computer Graphics
