Understanding Data Visualisations: Cognitive Processes Involved in Representing Data

理解数据可视化：表示数据所涉及的认知过程

• 批准号: 2302604
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2302604
• 关键词: Understanding; Data; Visualisations; Cognitive; Processes

The range of data visualisations used within academia and journalism is ever-increasing. With data literacy's importance recognised by the UN and the Gapminder Foundation, style tips and advice on producing effective data visualisations are also widespread (Cairo, 2016; Tufte, 1983). Whilst there is some research on perceptual and comprehension processes involved in simple graph-reading (Carpenter & Shah, 1998; Shah & Freedman, 2011), studies have frequently focused on geo-spatial maps (e.g. Ratwani, Trafton, & Boehm-Davis, 2008), and there is little empirical evidence on how individuals form coherent mental representations of complex data in graphs. It is somewhat unclear, therefore, how graph-readers make sense of what they see, and therefore how graph-creators can decide which graph best depicts their data. My research will investigate cognitive processes involved in understanding data visualisations and explore how individuals create and maintain a 'situation model' of a graph.Given the importance of reliable and robust findings, I plan to carry out four carefully designed, large sample studies over the course of four years. In my master's and PhD projects, I will use a range of visualisations to investigate how different presentation styles influence encoding and representation. Experiments will also investigate uncertainty and ambiguity in visually presented data, and the influence of accompanying text. In addition, I will explore the visual behaviours associated with comprehension of data visualisations. Participants' eye movements, recorded during exposure to graphs in the first two PhD studies proposed here, will be analysed with machine learning algorithms (e.g. principal component analysis). This will provide an unbiased method of identifying areas of interest (like Davies et al., 2017) and emergent patterns of behaviour associated with comprehension. This data will be used to simulate experiments, generating predictions which will then be tested with highly controlled studies. For example, if absent fixations to a particular area are associated with poorer comprehension, can emphasising those areas improve comprehension, or facilitate resolution of ambiguity?The studies outlined here will provide valuable insights into how data in graphs are mentally encoded and represented. All experiments will be linked not only by their exploration of how 'situation models' are built, but also through the comparison of visualisation formats, old and new. Together, this body of evidence will produce both theoretical implications about the representation of graphically-presented data, and practical implications relevant to journalism, academia and human-computer interaction. For example, it will highlight the effects of accompanying text and the potential for facilitating resolution of ambiguity. In addition, comprehension measures are likely to generate implications for data visualisation choice and design, highlighting the most suitable formats for presenting data, and also the possibility of making certain aspects more salient to facilitate interpretation. This research will make an important contribution to current knowledge on data visualisations, advancing understanding of graph comprehension and ultimately helping to improve design to benefit both users and creators.

学术界和新闻界使用的数据可视化的范围正在不断增加。随着数据素养的重要性得到联合国和Gapminder基金会的认可，关于制作有效数据可视化的风格提示和建议也很普遍（开罗，2016;塔夫特，1983）。虽然有一些关于简单图形阅读中涉及的感知和理解过程的研究（Carpenter & Shah，1998; Shah & Freedman，2011），研究经常集中在地理空间地图上（例如Ratwani，Trafton，& Boehm-Davis，2008），并且很少有经验证据表明个体如何形成图形中复杂数据的连贯心理表征。因此，有些不清楚图形阅读者如何理解他们所看到的，以及图形创建者如何决定哪个图形最好地描述了他们的数据。我的研究将调查认知过程中涉及的理解数据可视化，并探讨个人如何创建和维护一个图形的“情境模型”。鉴于可靠和强大的发现的重要性，我计划在四年内进行四个精心设计的大样本研究。在我的硕士和博士项目中，我将使用一系列可视化来研究不同的演示风格如何影响编码和表示。实验还将调查视觉呈现的数据中的不确定性和模糊性，以及伴随文本的影响。此外，我将探索与数据可视化理解相关的视觉行为。参与者的眼球运动，在这里提出的前两个博士研究中暴露于图形期间记录，将使用机器学习算法（例如主成分分析）进行分析。这将提供一种无偏的方法来识别感兴趣的区域（如Davies等人，2017）和与理解相关的新兴行为模式。这些数据将用于模拟实验，生成预测，然后通过高度受控的研究进行测试。例如，如果对某个特定区域的注视缺失与较差的理解有关，那么强调这些区域是否可以提高理解力，或者促进歧义的解决？这里概述的研究将为图形中的数据如何在心理上编码和表示提供有价值的见解。所有的实验将不仅通过他们如何建立“情境模型”的探索，而且还通过可视化格式的比较，新旧。总之，这一证据体系将产生关于图形呈现数据表示的理论影响，以及与新闻，学术和人机交互相关的实际影响。例如，它将突出说明附文的效果和促进消除歧义的潜力。此外，理解措施可能会产生影响数据可视化的选择和设计，突出最合适的格式来呈现数据，也有可能使某些方面更加突出，以方便解释。这项研究将对当前的数据可视化知识做出重要贡献，促进对图形理解的理解，并最终帮助改进设计，使用户和创作者受益。