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Measuring effectiveness of graph visualizations: A cognitive load perspective

机译：测量图形可视化的有效性：认知负载的角度

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摘要

Graph visualizations are typically evaluated by comparing their differences in effectiveness, measured by task performance such as response time and accuracy. Such performance-based measures have proved to be useful in their own right. There are some situations, however, where the performance measures alone may not be sensitive enough to detect differences. This limitation can be seen from the fact that the graph viewer may achieve the same level of performance by devoting different amounts of cognitive effort. In addition, it is not often that individual performance measures are consistently in favor of a particular visualization. This makes design and evaluation difficult in choosing one visualization over another. In an attempt to overcome the above-mentioned limitations, we measure the effectiveness of graph visualizations from a cognitive load perspective. Human memory as an information processing system and recent results from cognitive load research are reviewed first. The construct of cognitive load in the context of graph visualization is proposed and discussed. A model of user task performance, mental effort and cognitive load is proposed thereafter to further reveal the interacting relations between these three concepts. A cognitive load measure called mental effort is introduced and this measure is further combined with traditional performance measures into a single multi-dimensional measure called visualization efficiency. The proposed model and measurements are tested in a user study for validity. Implications of the cognitive load considerations in graph visualization are discussed.
机译：图形可视化通常是通过比较其有效性差异来评估的，该差异由任务性能（如响应时间和准确性）衡量。事实证明，这种基于绩效的措施本身是有用的。但是，在某些情况下，仅绩效指标可能不够灵敏以检测差异。从图形查看器可以通过投入不同量的认知努力来实现相同级别的性能这一事实可以看出这一局限性。此外，个别绩效指标并不总是一致地支持特定的可视化。这使得设计和评估难以选择一种可视化效果。为了克服上述限制，我们从认知负荷的角度衡量了图形可视化的有效性。首先回顾了作为信息处理系统的人类记忆和认知负荷研究的最新成果。提出并讨论了图可视化背景下认知负荷的构建。此后，提出了用户任务绩效，精神努力和认知负担的模型，以进一步揭示这三个概念之间的相互作用关系。引入了一种称为精神努力的认知负荷量度，并将该量度与传统的绩效量度进一步组合为一个称为可视化效率的多维量度。建议的模型和度量在用户研究中进行了有效性测试。讨论了图可视化中认知负荷考虑因素的含义。

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