Rethinking the Ranks of Visual Channels

IF 4.7 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-07-23 DOI:10.31219/osf.io/n7kxu
Caitlyn M. McColeman, Fumeng Yang, S. Franconeri, Timothy F. Brady
{"title":"Rethinking the Ranks of Visual Channels","authors":"Caitlyn M. McColeman, Fumeng Yang, S. Franconeri, Timothy F. Brady","doi":"10.31219/osf.io/n7kxu","DOIUrl":null,"url":null,"abstract":"Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values. To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or ‘wind map’ (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands).","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":"28 1","pages":"707-717"},"PeriodicalIF":4.7000,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Visualization and Computer Graphics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.31219/osf.io/n7kxu","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 13

Abstract

Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values. To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or ‘wind map’ (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands).
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
对视觉频道排名的再思考
数据可以使用视觉通道(如位置、长度或亮度)进行视觉表示。这些视觉通道的现有排名是基于参与者报告两个描述值之间的比率的准确程度。有一种假设是,这种排名应该适用于不同的任务和不同数量的分数。然而,令人惊讶的是,很少有现有的工作来检验这一假设,特别是考虑到视觉计算比率在现实世界的可视化中相对不重要,与观看、记忆和比较趋势和主题相比,在几乎普遍描绘两个以上值的显示中。为了模拟从可视化的一瞥中提取的信息,我们要求参与者在看到可视化后立即从内存中重现一组值。这些值可以显示在条形图(位置(条形))、折线图(位置)、热图(亮度)、气泡图(面积)、未对齐的条形图(长度)或“风图”(角度)中。使用贝叶斯多级建模方法,我们展示了视觉通道的等级位置如何在不同数量的标记(2、4或8)之间移动,以及偏差、精度和误差测量。即使是只有2个分数的复制品,该排名也不成立,而且新的概率排名对不同分数的复制物极不一致。除了通道选择之外,其他因素对性能的影响更大,如序列中的值的数量(例如,更多的标记导致更大的误差),或每个标记的值(例如,小的值被系统地高估)。8分的显示器的每个视觉通道都比4分差,这与视觉记忆的既定限制一致。这些结果表明,需要进行一系列实证研究,超越两个价值比判断,将其作为可靠排名视觉通道质量的基线,包括测试新任务(趋势或主题的检测)、时间尺度(即时计算或稍后的比较)和值的数量(从少数到数千)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics 工程技术-计算机:软件工程
CiteScore
10.40
自引率
19.20%
发文量
946
审稿时长
4.5 months
期刊介绍: TVCG is a scholarly, archival journal published monthly. Its Editorial Board strives to publish papers that present important research results and state-of-the-art seminal papers in computer graphics, visualization, and virtual reality. Specific topics include, but are not limited to: rendering technologies; geometric modeling and processing; shape analysis; graphics hardware; animation and simulation; perception, interaction and user interfaces; haptics; computational photography; high-dynamic range imaging and display; user studies and evaluation; biomedical visualization; volume visualization and graphics; visual analytics for machine learning; topology-based visualization; visual programming and software visualization; visualization in data science; virtual reality, augmented reality and mixed reality; advanced display technology, (e.g., 3D, immersive and multi-modal displays); applications of computer graphics and visualization.
期刊最新文献
EventPointMesh: Human Mesh Recovery Solely From Event Point Clouds A Review and Analysis of Evaluation Practices in VIS Domain Applications HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents RSVP for VPSA : A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis 3D Shape Completion on Unseen Categories: A Weakly-Supervised Approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1