Suppr超能文献

没有标记是一个孤岛:数据再现中的精度和类别排斥偏差。

No mark is an island: Precision and category repulsion biases in data reproductions.

出版信息

IEEE Trans Vis Comput Graph. 2021 Feb;27(2):1063-1072. doi: 10.1109/TVCG.2020.3030345. Epub 2021 Jan 28.

Abstract

Data visualization is powerful in large part because it facilitates visual extraction of values. Yet, existing measures of perceptual precision for data channels (e.g., position, length, orientation, etc.) are based largely on verbal reports of ratio judgments between two values (e.g., [7]). Verbal report conflates multiple sources of error beyond actual visual precision, introducing a ratio computation between these values and a requirement to translate that ratio to a verbal number. Here we observe raw measures of precision by eliminating both ratio computations and verbal reports; we simply ask participants to reproduce marks (a single bar or dot) to match a previously seen one. We manipulated whether the mark was initially presented (and later drawn) alone, paired with a reference (e.g. a second '100%' bar also present at test, or a y-axis for the dot), or integrated with the reference (merging that reference bar into a stacked bar graph, or placing the dot directly on the axis). Reproductions of smaller values were overestimated, and larger values were underestimated, suggesting systematic memory biases. Average reproduction error was around 10% of the actual value, regardless of whether the reproduction was done on a common baseline with the original. In the reference and (especially) the integrated conditions, responses were repulsed from an implicit midpoint of the reference mark, such that values above 50% were overestimated, and values below 50% were underestimated. This reproduction paradigm may serve within a new suite of more fundamental measures of the precision of graphical perception.

摘要

数据可视化之所以强大,在很大程度上是因为它便于直观地提取数值。然而,现有的数据通道感知精度度量(例如位置、长度、方向等)主要基于对两个值之间比率判断的口头报告(例如[7])。口头报告将实际视觉精度之外的多种错误来源混为一谈,从而在这些值之间进行比率计算,并要求将该比率转换为口头数字。在这里,我们通过消除比率计算和口头报告来观察精确的原始度量;我们只是要求参与者复制标记(一个单独的条或点)以匹配之前看到的标记。我们操纵标记是最初呈现(然后绘制)单独的,还是与参考标记(例如,测试时也存在的第二个“100%”条,或者用于点的 y 轴)配对,还是与参考标记集成(将该参考条合并到堆叠条形图中,或者直接将点放置在轴上)。较小值的再现被高估,而较大值被低估,这表明存在系统的记忆偏差。无论再现是否在原始值的公共基准上进行,平均再现误差都在实际值的 10%左右。在参考和(特别是)集成条件下,响应被从参考标记的隐含中点排斥,使得 50%以上的值被高估,而 50%以下的值被低估。这种再现范式可以作为一系列更基本的图形感知精度度量的新套件的一部分。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验