[论文解读] A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Alireza Karduni, Doug Markant+2 more|arXiv (Cornell University)|Jul 31, 2020

Data Visualization and Analytics参考文献 54被引用 7

一句话总结

本文提出了一种新颖的“直线+圆锥”视觉诱发方法，用于捕捉用户对双变量相关性的先验信念及其不确定性，采用贝叶斯认知建模分析用户在有无不确定性可视化的情况下对散点图的信念更新。主要发现表明，不确定性可视化减少了信念更新，尤其是在数据与先验信念相矛盾时，表明此类可视化可能通过平衡先验知识与新证据来稳定判断。

ABSTRACT

Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such judgements. The present work focuses on the impact of uncertainty communication when judging bivariate visualizations. Specifically, we model how users update their beliefs about variable relationships after seeing a scatterplot with and without uncertainty representation. To model and evaluate the belief updating, we present three studies. Study 1 focuses on a proposed ''Line + Cone'' visual elicitation method for capturing users' beliefs in an accurate and intuitive fashion. The findings reveal that our proposed method of belief solicitation reduces complexity and accurately captures the users' uncertainty about a range of bivariate relationships. Study 2 leverages the ``Line + Cone'' elicitation method to measure belief updating on the relationship between different sets of variables when seeing correlation visualization with and without uncertainty representation. We compare changes in users beliefs to the predictions of Bayesian cognitive models which provide normative benchmarks for how users should update their prior beliefs about a relationship in light of observed data. The findings from Study 2 revealed that one of the visualization conditions with uncertainty communication led to users being slightly more confident about their judgement compared to visualization without uncertainty information. Study 3 builds on findings from Study 2 and explores differences in belief update when the bivariate visualization is congruent or incongruent with users' prior belief. Our results highlight the effects of incorporating uncertainty representation, and the potential of measuring belief updating on correlation judgement with Bayesian cognitive models.

研究动机与目标

• 探讨先验信念与不确定性表达如何影响用户在数据可视化中对相关性的判断。
• 开发并验证一种用户友好的视觉方法，用于诱发关于双变量关系的主观信念与不确定性。
• 应用贝叶斯认知建模作为规范性基准，评估用户在响应可视化数据时信念更新是否合乎理性。
• 考察不确定性可视化如何影响信念更新，尤其是在数据与先验信念冲突时。
• 理解可视化设计在塑造用户对相关性解释的自信程度与准确性方面的作用。

提出的方法

• 提出“直线+圆锥”方法：用户通过调整直线（表示预期相关性）和圆锥（表示不确定性范围）来表达其对双变量关系的先验信念。
• 使用交互式视觉诱发方法，捕捉参与者对相关性数值的主观概率分布的均值（直线）与方差（圆锥）。
• 采用贝叶斯认知建模，将参与者实际的信念更新（后验）与三种规范性基准进行比较：仅先验模型、贝叶斯-有信息先验模型与贝叶斯-均匀先验模型。
• 开展三项实验研究：（1）验证诱发方法的有效性，（2）比较有无不确定性可视化下的信念更新，（3）测试在数据与信念一致或不一致条件下的信念更新。
• 通过测量不同条件下相关性估计均值与不确定性（标准差）的变化，分析信念更新的动力学过程。
• 使用统计模型比较（AIC/BIC）确定哪种贝叶斯模型最能解释观察到的后验分布。

Figure 1: Elicitation methods in Study 1. A: For the Line + Cone elicitation, participants first recorded the belief about the most likely relationship between two variables (red line), then adjusted the set of plausible alternatives based on their uncertainty (gray lines). B: For the MCMC-P elicita

实验结果

研究问题

• RQ1 先验信念在多大程度上影响个体在双变量可视化中对相关性的判断？
• RQ2 当数据与先验信念一致或冲突时，不确定性可视化是否存在影响信念更新？
• RQ3 用户在观看带有或不带不确定性的相关性可视化时，其信念更新在多大程度上与贝叶斯推理一致？
• RQ4 不确定性表示方式（如圆锥与HOPs）如何影响用户的信心与信念稳定性？
• RQ5 在可视化情境中，先验信念与新数据证据对后验信念的相对影响如何？

主要发现

• “直线+圆锥”方法能以高精度捕捉用户对双变量相关性的先验信念与不确定性，且认知负荷较低。
• 与无不确定性表示的可视化相比，带有不确定性表示的可视化显著减少了信念更新，尤其是在数据与先验信念相矛盾时。
• 参与者的后验信念最符合贝叶斯-均匀模型，表明新数据对信念的影响强于其诱发的先验信念。
• 当观察到极端样本相关性时，用户减少了不确定性，表现出对数据强度的敏感性。
• 即使在仅使用直线的条件下，样本量较小时（n=10 vs. n=100）不确定性也更高，表明用户直观地感知到小样本数据的可靠性较低。
• 尽管贝叶斯模型预测如此，参与者表达的后验不确定性仍高于模型预测值，表明在先验诱发中可能存在过度自信，或圆锥解释存在偏差。

Figure 2: Study 2 Design. Each user goes through ten variable sets (five variables for two rounds) and elicit their belief before and after seeing data visualizations about each variable set. In Round 1, the user views five variable sets through only scatterplots. In Round 2, the user is randomly as