How locus of control influences compatibility with visualization style
How locus of control influences compatibility with visualization style
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控制点如何影响与可视化风格的兼容性
DOI：
10.1109/vast.2011.6102445
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Remco Chang
中科院分区：
文献类型：
--
作者：
Caroline Ziemkiewicz;R. Crouser;Ashley Rye Yauilla;S. Su;W. Ribarsky;Remco Chang
现有的研究表明，个体性格差异与用户使用不同类型的复杂可视化系统解决问题的速度和准确性有关。在本文中，我们通过隔离人格特质中的因素以及可能导致观察到的相关性的可视化来扩展这项研究。我们关注的是一种被称为“控制点”的人格特征，它代表了一个人倾向于认为自己被外部事件所控制或控制。为了隔离可视化设计的变量，我们控制了无关的因素，如颜色、交互和标签，并特别关注可视化的整体布局风格。我们进行了一项用户研究，从缩进隐喻逐渐转变为包容隐喻，并比较了参与者的速度、准确性和偏好与他们的控制点。我们的研究结果表明，两者之间确实存在相关性：具有内部控制点的参与者在使用遏制隐喻的视觉化方面表现得更差，而具有外部控制点的参与者在这种视觉化方面表现得很好。我们讨论了基于认知心理学的这种关系的可能解释，并提出这些结果可以用来更好地理解人们如何使用可视化，以及如何使视觉分析设计适应个人用户的需求。
Existing research suggests that individual personality differences are correlated with a user's speed and accuracy in solving problems with different types of complex visualization systems. In this paper, we extend this research by isolating factors in personality traits as well as in the visualizations that could have contributed to the observed correlation. We focus on a personality trait known as “locus of control,” which represents a person's tendency to see themselves as controlled by or in control of external events. To isolate variables of the visualization design, we control extraneous factors such as color, interaction, and labeling, and specifically focus on the overall layout style of the visualizations. We conduct a user study with four visualizations that gradually shift from an indentation metaphor to a containment metaphor and compare the participants' speed, accuracy, and preference with their locus of control. Our findings demonstrate that there is indeed a correlation between the two: participants with an internal locus of control perform more poorly with visualizations that employ a containment metaphor, while those with an external locus of control perform well with such visualizations. We discuss a possible explanation for this relationship based in cognitive psychology and propose that these results can be used to better understand how people use visualizations and how to adapt visual analytics design to an individual user's needs.