A Design Space of Vision Science Methods for Visualization Research
A Design Space of Vision Science Methods for Visualization Research
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DOI：
10.1109/tvcg.2020.3029413
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发表时间：
2021-02-01
影响因子：
5.2
通讯作者：
Szafir, Danielle Albers
中科院分区：
文献类型：
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作者：
Elliott, Madison A.;Nothelfer, Christine;Szafir, Danielle Albers
越来越多的努力旨在了解人们在使用可视化技术时看到了什么。这些努力为补充设计直觉提供了科学基础，导致了更有效的可视化实践。然而，目前发表的可视化研究反映了一套有限的可用方法来理解人们如何处理可视化数据。视觉科学的替代方法为理解可视化提供了一套丰富的工具，但在感知或可视化研究中都不存在这些方法的精选集合。我们介绍了一个实验方法的设计空间，用于经验性地调查与查看数据可视化相关的感知过程，以最终形成可视化设计指南。本文为实验可视化研究提供了一个共享词典。我们讨论了视觉科学研究中流行的实验范式、调节类型、反应类型和依赖测量，每一个都植根于可视化的例子。然后我们讨论每种技术的优点和局限性。研究人员可以利用这个设计空间来创造创新的研究，并以可视化的方式提高对设计选择和评估的科学理解。我们突出了可视化和视觉科学研究之间合作成功的历史，并倡导这两个领域之间有更深层次的关系，可以详细说明和扩展理解可视化和视觉的方法论设计空间。
A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for understanding how people process visualized data. Alternative methods from vision science offer a rich suite of tools for understanding visualizations, but no curated collection of these methods exists in either perception or visualization research. We introduce a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to ultimately inform visualization design guidelines. This paper provides a shared lexicon for facilitating experimental visualization research. We discuss popular experimental paradigms, adjustment types, response types, and dependent measures used in vision science research, rooting each in visualization examples. We then discuss the advantages and limitations of each technique. Researchers can use this design space to create innovative studies and progress scientific understanding of design choices and evaluations in visualization. We highlight a history of collaborative success between visualization and vision science research and advocate for a deeper relationship between the two fields that can elaborate on and extend the methodological design space for understanding visualization and vision.