Examining Effort in 1D Uncertainty Communication Using Individual Differences in Working Memory and NASA-TLX
Examining Effort in 1D Uncertainty Communication Using Individual Differences in Working Memory and NASA-TLX
复制标题
DOI：
10.1109/tvcg.2021.3114803
复制
发表时间：
2022-01-01
影响因子：
5.2
通讯作者：
Padilla, Lace
中科院分区：
文献类型：
--
作者：
Castro, Spencer C.;Hosseinpour, Helia;Padilla, Lace
随着面向普通观众的不确定性可视化变得越来越普遍，设计师必须了解不确定性沟通技术对观众决策过程的全面影响。先前的工作表明，关于个人如何使用各种视觉和文本的不确定性解释做出决策，表现结果好坏参半。研究结果的不一致性部分可能是由于过度依赖任务的准确性，这本身不能提供一个全面的理解不确定性可视化技术如何支持推理过程。在这项工作中，我们推进了围绕现代1D不确定性可视化的有效性进行收敛的定量和定性分析的努力和策略时，由个人提供的分位数点图，密度图，区间图，平均图，和文本描述的不确定性。我们利用两种方法来检查跨不确定性通信技术的努力：被称为操作跨度（OSPAN）任务和自我报告的工作量通过NASA-TLX的工作记忆容量的个体差异的措施。结果表明，可视化方法和工作记忆容量都会影响参与者的决策。具体而言，分位数点图和密度图（即，分布注释）比区间图、不确定性的文本描述和平均值图（即，摘要注释）。此外，参与者的开放式的反应表明，个人查看分布式注释更有可能采用的策略，明确纳入他们的判断比那些查看摘要注释的不确定性。当比较分位数点图和密度图时，这项工作发现这两种方法对低工作记忆个体同样有效。然而，对于具有高工作记忆容量的个体，分位数点图唤起了更准确的反应，而感知的努力更少。鉴于这些结果，我们主张除了准确性性能之外，还应包括收敛行为和主观工作负载指标，以进一步消除可视化技术之间有意义的差异。
As uncertainty visualizations for general audiences become increasingly common, designers must understand the full impact of uncertainty communication techniques on viewers' decision processes. Prior work demonstrates mixed performance outcomes with respect to how individuals make decisions using various visual and textual depictions of uncertainty. Part of the inconsistency across findings may be due to an over-reliance on task accuracy, which cannot, on its own, provide a comprehensive understanding of how uncertainty visualization techniques support reasoning processes. In this work, we advance the debate surrounding the efficacy of modern 1D uncertainty visualizations by conducting converging quantitative and qualitative analyses of both the effort and strategies used by individuals when provided with quantile dotplots, density plots, interval plots, mean plots, and textual descriptions of uncertainty. We utilize two approaches for examining effort across uncertainty communication techniques: a measure of individual differences in working-memory capacity known as an operation span (OSPAN) task and self-reports of perceived workload via the NASA-TLX. The results reveal that both visualization methods and working-memory capacity impact participants' decisions. Specifically, quantile dotplots and density plots (i.e., distributional annotations) result in more accurate judgments than interval plots, textual descriptions of uncertainty, and mean plots (i.e., summary annotations). Additionally, participants' open-ended responses suggest that individuals viewing distributional annotations are more likely to employ a strategy that explicitly incorporates uncertainty into their judgments than those viewing summary annotations. When comparing quantile dotplots to density plots, this work finds that both methods are equally effective for low-working-memory individuals. However, for individuals with high-working-memory capacity, quantile dotplots evoke more accurate responses with less perceived effort. Given these results, we advocate for the inclusion of converging behavioral and subjective workload metrics in addition to accuracy performance to further disambiguate meaningful differences among visualization techniques.