The Attraction Effect in Information Visualization

A viewer can extract many potential patterns from any set of visualized data values. But that means that two people can see different patterns in the same visualization, potentially leading to miscommunication. Here, we show that when people are primed to see one pattern in the data as visually salient, they believe that naïve viewers will experience the same visual salience. Participants were told one of multiple backstories about political events that affected public polling data, before viewing a graph that depicted those data. One pattern in the data was particularly visually salient to them given the backstory that they heard. They then predicted what naïve viewers would most visually salient on the visualization. They were strongly influenced by their own knowledge, despite explicit instructions to ignore it, predicting that others would find the same patterns to be most visually salient. This result reflects a psychological phenomenon known as the curse of knowledge, where an expert struggles to recreate the state of mind of a novice. The present findings show that the curse of knowledge also plagues the visual perception of data, explaining why people can fail to connect with audiences when they communicate patterns in data.

Section: Resultssupporting

confidence: 70%

Section: Related Workmentioning

confidence: 99%

The Curse of Knowledge in Visual Data Communication

Xiong

Weelden

Franconeri

2020

“…It has seen increased use in HCI and visualization (e.g. [11,18,35,66,71,74]). We pre-specified all analyses before conducting the experiment and tested on pilot data.…”

Section: S2: Results and Analysismentioning

confidence: 99%

“…In S1, these corresponded to the active reading goals of decoding and analyzing visualizations, which are of particular interest to visualization researchers. One of the best ways to progress towards precision is through controlled lab studies in which participants are asked to perform low-level tasks (e.g., [15,18,61]). Following this approach, with S2 we evaluated the accuracy and completion time of 18 participants for two low-level graph reading tasks, with and without active reading support for marking and creation actions.…”

Section: Methodology and Rationale For The Studiesmentioning

confidence: 99%

Active Reading of Visualizations

Walny

Huron

Périn

et al. 2018

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We investigate whether the notion of active reading for text might be usefully applied to visualizations. Through a qualitative study we explored whether people apply observable active reading techniques when reading paper-based node-link visualizations. Participants used a range of physical actions while reading, and from these we synthesized an initial set of active reading techniques for visualizations. To learn more about the potential impact such techniques may have on visualization reading, we implemented support for one type of physical action from our observations (making freeform marks) in an interactive node-link visualization. Results from our quantitative study of this implementation show that interactive support for active reading techniques can improve the accuracy of performing low-level visualization tasks. Together, our studies suggest that the active reading space is ripe for research exploration within visualization and can lead to new interactions that make for a more flexible and effective visualization reading experience. Permanent repository linkIndex Terms-active reading of visualizations, active reading, information visualization, spectrum of physical engagement

“…For example, lines indicate connection, arrows indicate dynamic (or causal) information [17], and scattered dots each represents a value of an individual subject or collection [12]. In higher-level decision tasks, visualization design also influences data interpretation and decision making [9,4]. People interpret climate data differently depending on whether the visualization presented percentile information versus showing the range [8].…”

Section: Related Workmentioning

confidence: 99%

Illusion of Causality in Visualized Data

Xiong¹,

Shapiro

Hullman³

et al. 2020

When students eat breakfast very often (more than 4 times a week), their GPA is around 3.5; while when students eat breakfast not very often (less than 4 times a week), their GPA is around 3.0.Number of breakfasts eaten per week GPA Number of breakfasts eaten per week GPA Number of breakfasts eaten per week GPA When students eat breakfast very often (more than 4 times a week), their GPA is around 3.5; while when students eat breakfast not very often (less than 4 times a week), their GPA is around 3.0. Fig. 1. The same data showing the relation between eating breakfast and GPA presented via text, bar graph, line graph or scatter plot. Which depiction makes eating breakfast causing higher GPA seem more plausible to you?Abstract-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.