AI 中文总结
本文提出将模型素养作为VA的额外评估因素，通过对照研究发现模型-任务准确率与VA-任务准确率正相关，PCA的相关性强于t-SNE，为VA评估方法的丰富提供了方向。
AI 中文摘要
理解并提升视觉分析（VA）的性能对最大化其影响力至关重要。现有研究已成功将信息可视化领域成熟的总结性评估方法应用于VA场景，但VA流程中近期对额外数据分析/建模阶段的重视带来了新挑战。受现代可视化素养概念的启发，本文研究模型素养——即用户对VA技术所用分析模型的了解程度，作为VA性能的额外评估因素。针对使用两种降维模型进行多维数据视觉分析的对照研究结果显示，模型-任务准确率与VA-任务准确率呈正相关。研究涉及两种常见降维模型：PCA和t-SNE。在当前任务设计中，PCA的相关性强于t-SNE，这一模式与以下可能性一致：当模型输出难以从可视化中直接解读时，VA有效性与模型素养的关联更紧密。完成时间证据未显示稳定的效率提升，表明模型直观性的差异或可解释模型知识何时缩短任务完成时间、何时涉及额外解释工作量。本研究的发现为进一步丰富VA评估方法提供了思路，并为开发更严谨的模型素养评估工具指明了方向。
英文摘要
Understanding and enhancing visual analytics (VA) performance is important for maximizing their impact. Existing studies have successfully applied well-established summative evaluation methods from information visualization to the VA context, yet the recent emphasis on an extra data analysis/modeling stage in the VA pipeline poses an additional challenge. Inspired by the modern concept of visualization literacy, this paper examines model literacy, namely users' knowledge of the analysis model used in a VA technique, as an additional factor for VA performance. Results from a controlled study on the visual analysis of multidimensional data with two dimensionality-reduction models indicate a positive correlation between model-task accuracy and VA-task accuracy. The study involves two common dimensionality-reduction models, PCA and t-SNE. The correlation is stronger for PCA than for t-SNE in the current task design, a pattern consistent with the possibility that VA effectiveness is more closely associated with model literacy when model outputs are less directly readable from the visualization. Completion-time evidence does not show a stable efficiency gain, suggesting that differences in model intuitiveness may help explain when model knowledge shortens task completion and when it involves additional interpretive effort. The findings of this study suggest ways to further enrich VA evaluation methods and provide directions for developing more rigorous model-literacy assessment instruments.
