AI 中文总结
本研究通过三项受试者实验，探究不同定性不确定性可视化方式对非专业人士决策的影响，为折线图预测的多形式不确定性呈现提供设计指南。
AI 中文摘要
预测涉及多种形式的不确定性，包括可直接量化的不确定性（定量不确定性），以及必须通过专家对预测及其背景的主观判断来表达的不确定性（定性置信度）。过往研究已证实，在预测中同时传达定量不确定性与定性置信度会改变读者的决策，但很少有研究探讨这些不确定性的呈现方式所产生的影响。本研究报告了三项预先注册的人类受试者研究（总样本量n=923），探究在折线图的置信区间旁，不同的定性不确定性可视化方法对非专业人士决策的影响，具体研究了通过文本和图标单独呈现定性不确定性，以及通过颜色、透明度和模糊笔触设计将其整合到定量置信区间中的方式。实验1证实，在统计变异性旁展示定性置信度可改变决策模式，在时间序列折线图这一新情境下重复了过往研究的发现；实验2和3发现，多种非文本编码技术能让参与者将定性置信度纳入判断时产生相似效果。研究结果为可视化设计师提供了可操作的指南，指导其为单张折线图预测呈现多种形式的不确定性。本论文及所有补充材料的免费副本可在该https网址获取。
英文摘要
Forecasting involves multiple forms of uncertainty, including both uncertainties that can be quantified directly (quantitative uncertainty) and those that must be expressed through experts' subjective judgments about the forecast and its context (qualitative confidence). Past work has established that conveying both quantitative uncertainty and qualitative confidence in forecasts can alter readers' decision making, but little research investigates the impact of how these forms of uncertainty are presented. In this work, we present three preregistered human-subjects studies (total n = 923) on how different methods of visualizing qualitative uncertainty alongside line charts' confidence intervals affects non-experts' decision making. In particular, we investigate representing qualitative uncertainty separately via text and icons, and integrated into quantitative confidence intervals via color, transparency, and a blurred stroke design. In Experiment 1, we confirm that showing qualitative confidence alongside statistical variability can change patterns of decision making, replicating findings from previous work in the new context of time-series line charts. In Experiments 2 and 3, we find several non-textual encoding techniques that produce similar effects in participants' incorporation of qualitative confidence into their judgments. Our findings suggest actionable guidelines for visualization designers who seek to represent multiple forms of uncertainty for a single line chart forecast. A free copy of this paper and all supplemental materials are available at https://osf.io/7ya2c/overview.
