认知模型在视觉化研究中的案例:立场文件

Lace M. K. Padilla
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引用次数: 8

摘要

可视化社区在采用用户研究方面已经有了很大的发展。经验用户研究系统地测试了我们所做的关于可视化如何帮助或阻碍观众完成任务的假设。尽管用户研究的增加令人鼓舞,但至关重要的是,对人类推理的可视化研究必须建立在对思维功能的理解之上。以前,没有足够的模型可以用可视化来说明决策过程。然而,Padilla等人[41]最近提出了一个与可视化相结合的决策模型,该模型扩展了现代可视化认知和决策理论。在本文中,我们提供了认知模型如何加速创新、提高有效性和促进复制工作的见解,这些还有待于在可视化社区中进行深入讨论。为了做到这一点,我们使用Padilla等人[41]的认知模型作为指导框架,为可视化社区提供了可视化决策认知科学的简明概述。通过详细的可视化研究的例子来说明模型的每个组成部分,本文为可视化研究人员如何利用认知框架来指导他们的用户研究提供了新颖的见解。我们从可视化的实证研究中提供了模型的每个组成部分的实际例子,以及每个认知过程的可视化含义,这些在以前的工作中没有直接解决。最后,这项工作提供了一个案例研究,利用对人类认知的理解,在飓风预报轨道可视化的背景下,为可视化推理偏差生成一种新的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Case for Cognitive Models in Visualization Research : Position paper
The visualization community has seen a rise in the adoption of user studies. Empirical user studies systematically test the assumptions that we make about how visualizations can help or hinder viewers’ performance of tasks. Although the increase in user studies is encouraging, it is vital that research on human reasoning with visualizations be grounded in an understanding of how the mind functions. Previously, there were no sufficient models that illustrate the process of decision-making with visualizations. However, Padilla et al. [41] recently proposed an integrative model for decision-making with visualizations, which expands on modern theories of visualization cognition and decision-making. In this paper, we provide insights into how cognitive models can accelerate innovation, improve validity, and facilitate replication efforts, which have yet to be thoroughly discussed in the visualization community. To do this, we offer a compact overview of the cognitive science of decision-making with visualizations for the visualization community, using the Padilla et al. [41] cognitive model as a guiding framework. By detailing examples of visualization research that illustrate each component of the model, this paper offers novel insights into how visualization researchers can utilize a cognitive framework to guide their user studies. We provide practical examples of each component of the model from empirical studies of visualizations, along with visualization implications of each cognitive process, which have not been directly addressed in prior work. Finally, this work offers a case study in utilizing an understanding of human cognition to generate a novel solution to a visualization reasoning bias in the context of hurricane forecast track visualizations.
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