Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations
Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations
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多重预测可视化 (MFV)：多重 COVID-19 预测可视化中信任与性能的权衡
DOI：
10.1109/tvcg.2022.3209457
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发表时间：
2022
影响因子：
5.2
通讯作者：
Bertini, Enrico
中科院分区：
文献类型：
--
作者：
Padilla, Lace;Fygenson, Racquel;Castro, Spencer C.;Bertini, Enrico
SARS-COV-2（COVID-19）应对措施不足的普遍存在可能表明对预测和风险沟通缺乏信任。然而，没有工作已经经验性地测试了多个预测可视化选择如何影响信任和基于任务的性能。本文中提出的三项研究（）研究了可视化选择如何影响对COVID-19死亡率预测的信任，以及它们如何影响趋势预测任务的表现。该等研究集中于填有实时COVID-19数据的折线图，该等数据改变了预测的数字及颜色编码，以及最佳/最坏情况预测的存在。研究显示，对COVID-19预测可视化的信任最初随着预测数量的增加而增加，然后在6-9次预测后达到平稳状态。然而，参与者最信任显示较少视觉信息的可视化，包括95%置信区间，单一预测和灰度编码预测。参与者在标记为50%和25%的区间内保持高度信任，并且没有按比例将他们的信任扩展到指定的区间大小。尽管信任度很高，但95%CI条件最有可能引发与实际COVID-19趋势不一致的预测。对参与者策略的定性分析证实，许多参与者既信任简单的可视化，也信任那些有大量预测的人。这项工作为COVID-19预测可视化如何影响信任提供了实用指南，包括确定预测平衡信任和基于任务的性能之间的权衡的范围的建议。
The prevalence of inadequate SARS-COV-2 (COVID-19) responses may indicate a lack of trust in forecasts and risk communication. However, no work has empirically tested how multiple forecast visualization choices impact trust and task-based performance. The three studies presented in this paper () examine how visualization choices impact trust in COVID-19 mortality forecasts and how they influence performance in a trend prediction task. These studies focus on line charts populated with real-time COVID-19 data that varied the number and color encoding of the forecasts and the presence of best/worst-case forecasts. The studies reveal that trust in COVID-19 forecast visualizations initially increases with the number of forecasts and then plateaus after 6–9 forecasts. However, participants were most trusting of visualizations that showed less visual information, including a 95% confidence interval, single forecast, and grayscale encoded forecasts. Participants maintained high trust in intervals labeled with 50% and 25% and did not proportionally scale their trust to the indicated interval size. Despite the high trust, the 95% CI condition was the most likely to evoke predictions that did not correspond with the actual COVID-19 trend. Qualitative analysis of participants' strategies confirmed that many participants trusted both the simplistic visualizations and those with numerous forecasts. This work provides practical guides for how COVID-19 forecast visualizations influence trust, including recommendations for identifying the range where forecasts balance trade-offs between trust and task-based performance.
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影响因子：
4.6
作者：
Padilla L;Hosseinpour H;Fygenson R;Howell J;Chunara R;Bertini E
通讯作者：
Bertini E
影响因子：
5.8
作者：
Komiak, Sherrie Y. X.;Benbasat, Izak
通讯作者：
Benbasat, Izak
以 APA 风格报告统计数据
DOI：
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发表时间：
2011
期刊：
影响因子：
--
作者：
J. Kahn
通讯作者：
J. Kahn
DOI：
--
发表时间：
2021
期刊：
2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)
影响因子：
--
作者：
Dirk Leffrang;Oliver Müller
通讯作者：
Oliver Müller
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DOI：
--
发表时间：
2022
期刊：
International Conference on Human Factors in Computing Systems
影响因子：
--
作者：
Yixuan Zhang;Nurul M. Suhaimi;Nutchanon Yongsatianchot;Joseph D. Gaggiano;Miso Kim;Shivani A. Patel;Yifan Sun;S. Marsella;Jacqueline A. Griffin;Andrea G. Parker
通讯作者：
Andrea G. Parker