Visual Reasoning Strategies for Effect Size Judgments and Decisions
Visual Reasoning Strategies for Effect Size Judgments and Decisions
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DOI：
10.1109/tvcg.2020.3030335
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发表时间：
2021-02-01
影响因子：
5.2
通讯作者：
Hullman, Jessica
中科院分区：
文献类型：
--
作者：
Kale, Alex;Kay, Matthew;Hullman, Jessica
不确定性可视化通常强调点估计，以通过视觉比较来支持震级估计或决策。然而，当设计选择强调手段时，用户可能会忽视不确定性信息，并将视觉距离误解为效果大小的代理。我们介绍了一项关于机械土耳其人的混合设计实验的结果，该实验测试了八个不确定的可视化设计：95%的遏制区间、假设的结果图、密度和分位数点图，每个都有和没有添加均值。我们发现，增加不确定性可视化的手段对震级估计和决策都有很小的偏差影响，与贴现不确定性是一致的。我们还看到，支持最小偏差效果大小估计的可视化设计并不支持最佳决策，这表明当图表用户将相同的信息用于不同的任务时，他们的效果大小感觉可能不一定相同。在对用户策略描述的定性分析中，我们发现许多用户会转换策略，而不是在存在最优策略的情况下使用最优策略。在理论上优化设计的不确定性可视化在实践中可能并不是最有效的，因为用户满足启发式的方式，这意味着有机会通过对潜在策略集进行建模来更好地理解可视化效果。
Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies.