Illusion of Causality in Visualized Data
Illusion of Causality in Visualized Data
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DOI：
10.1109/tvcg.2019.2934399
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发表时间：
2019-08
影响因子：
5.2
通讯作者：
Cindy Xiong;Joel K Shapiro;J. Hullman;S. Franconeri
中科院分区：
文献类型：
--
作者：
Cindy Xiong;Joel K Shapiro;J. Hullman;S. Franconeri
吃早餐次数越多的学生平均成绩越高。根据这一数据，许多人可能会自信地表示，学前早餐计划会带来更高的分数。这是一个推理错误，因为相关性并不一定表明因果关系--X和Y可以相互关联，而不是其中一个直接导致另一个。虽然这种错误是普遍存在的，但通过将数据呈现给查看者的方式，它的普遍性可能会被放大或减轻。在三个众包实验中，我们检查了如何呈现简单的数据关系是否会减少这种推理错误。第一个实验测试了类似于早餐-GPA关系的例子，因果关系的似是而非程度不同。我们要求参与者对他们对这一关系相关的认同程度进行评分，他们适当地将其评分为高。然而，与会者也高度同意对数据的因果解释。对因果解释的支持程度在可视化类型中并不是同样强烈：文本描述和条形图的因果关系评分最高，但散点图的因果评分较低。但是，这种效果是由将数据聚合到两组的条形图还是由视觉编码类型驱动的呢？我们分离了数据聚合与可视编码类型，并检查了它们对感知因果关系的单独影响。总体而言，不同的可视化设计对相同的数据提供了不同的认知推理启示。通过图表进行的高水平数据聚合往往与数据中感知到的更高因果关系相关。参与者认为线条和点状视觉编码比条形编码更具因果性。我们的结果表明，一些可视化设计如何触发更强的因果联系，而选择其他设计可以帮助缓解对因果关系的无端感知。
Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program would lead to higher grades. This is a reasoning error, because correlation does not necessarily indicate causation – X and Y can be correlated without one directly causing the other. While this error is pervasive, its prevalence might be amplified or mitigated by the way that the data is presented to a viewer. Across three crowdsourced experiments, we examined whether how simple data relations are presented would mitigate this reasoning error. The first experiment tested examples similar to the breakfast-GPA relation, varying in the plausibility of the causal link. We asked participants to rate their level of agreement that the relation was correlated, which they rated appropriately as high. However, participants also expressed high agreement with a causal interpretation of the data. Levels of support for the causal interpretation were not equally strong across visualization types: causality ratings were highest for text descriptions and bar graphs, but weaker for scatter plots. But is this effect driven by bar graphs aggregating data into two groups or by the visual encoding type? We isolated data aggregation versus visual encoding type and examined their individual effect on perceived causality. Overall, different visualization designs afford different cognitive reasoning affordances across the same data. High levels of data aggregation by graphs tend to be associated with higher perceived causality in data. Participants perceived line and dot visual encodings as more causal than bar encodings. Our results demonstrate how some visualization designs trigger stronger causal links while choosing others can help mitigate unwarranted perceptions of causality.