Impact of Cognitive Biases on Progressive Visualization

Procopio, Marianne; Mosca, Ab; Scheidegger, Carlos; Wu, Eugene; Chang, Remco

doi:10.1109/tvcg.2021.3051013

Cited by 12 publications

(15 citation statements)

References 49 publications

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“…Research in visualization biases tend to develop in parallel with research in visualization literacy, since biases often arise when readers are not familiar with a visualization [55]. The weighted average illusion adds to the growing literature of biases [3,11,23,24,69,90,96] that may affect interpretations of even the most familiar visualizations.…”

Section: Discussionmentioning

confidence: 99%

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

Matt-Heun¹,

Witt²,

Szafir³

2021

Preprint

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The mean provides a reference for judging data points as above or below average.If points vary in lightness or size, the perceived mean position will be biased... ...leading some data points to be misjudged as being above average. Fig. 1. We measured how integrating size or lightness into scatterplots can systematically bias the perceived mean of those points. We model how individual marks contribute to this bias, allowing us to predict differences in the actual mean (blue) and where people see the mean (orange). This misinterpretation can inhibit sensemaking and decision making. For instance, points in the grey boxes above will be incorrectly perceived as having higher-than average y-values. Under this illusion, graph readers' mean estimates can be increasingly biased as a function of size or lightness ranges mapped onto the data.

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Section: Discussionmentioning

confidence: 99%

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

Matt-Heun¹,

Witt²,

Szafir³

2021

Preprint

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“…If an analyst had a perceived assumption about what was likely causing the anomalies, their steering would often reflect the bias and direct the computation towards confirming that assumption. Recent research has begun to explore the potential pitfalls of progressive visualization and steering (e.g., [7,15,37,43,46]). In the context of anomaly reasoning, we observe that such bias can be detrimental to the analysts' abilities to appropriately reason about the anomalies.…”

Section: Mixed-initiative Anomaly Reasoning Considered Harmfulmentioning

confidence: 99%

PIXAL: Anomaly Reasoning with Visual Analytics

Montambault¹,

Brumar²,

Behrisch³

et al. 2022

Preprint

Self Cite

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“…The length of these three phases is affected by the speed of access to the data, the data distribution, the order in which it is processed, and the underlying computation processing it. The user essentially tries to balance between speed and accuracy along these three phases [32,44]. The challenging aspect is the uncertainty in the data or computations and the resulting visualizations.…”

Section: Progressive Visualizationmentioning

confidence: 99%

“…This reliance on trends in past data to predict the future could lead to inference uncertainty [51]. Yet another possible source of error is misjudgments of the uncertainty due to the dynamic nature of visualizations in a progressive setting, also known as uncertainty bias [36,44]. This bias includes biases due to human tendencies to distrust fluctuating values-ambiguity bias [6], and neglect the visual glyphs for uncertainty visualization-neglect of probability bias [53].…”

Section: Decision-making In the Progressive Settingmentioning

confidence: 99%

“…This bias includes biases due to human tendencies to distrust fluctuating values-ambiguity bias [6], and neglect the visual glyphs for uncertainty visualization-neglect of probability bias [53]. Finally, false patterns that occur in intermediate results have also been shown to affect decision-making, known as illusion bias [36,44].…”

Section: Decision-making In the Progressive Settingmentioning

confidence: 99%

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Studying Early Decision Making with Progressive Bar Charts

Patil

Richer

Jermaine

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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We conduct a user study to quantify and compare user performance for a value comparison task using four bar chart designs, where the bars show the mean values of data loaded progressively and updated every second (progressive bar charts). Progressive visualization divides different stages of the visualization pipeline-data loading, processing, and visualization-into iterative animated steps to limit the latency when loading large amounts of data. An animated visualization appearing quickly, unfolding, and getting more accurate with time, enables users to make early decisions. However, intermediate mean estimates are computed only on partial data and may not have time to converge to the true means, potentially misleading users and resulting in incorrect decisions. To address this issue, we propose two new designs visualizing the history of values in progressive bar charts, in addition to the use of confidence intervals. We comparatively study four progressive bar chart designs: with/without confidence intervals, and using near-history representation with/without confidence intervals, on three realistic data distributions. We evaluate user performance based on the percentage of correct answers (accuracy), response time, and user confidence. Our results show that, overall, users can make early and accurate decisions with 92% accuracy using only 18% of the data, regardless of the design. We find that our proposed bar chart design with only near-history is comparable to bar charts with only confidence intervals in performance, and the qualitative feedback we received indicates a preference for designs with history.

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Impact of Cognitive Biases on Progressive Visualization

Cited by 12 publications

References 49 publications

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots

PIXAL: Anomaly Reasoning with Visual Analytics

Studying Early Decision Making with Progressive Bar Charts

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