法棍图表：通过触觉引导调节正态分布可视化性能
Croissant Charts: Modulating the Performance of Normal Distribution Visualizations with Affordances
AI总结：
本文通过法棍图表研究如何通过触觉引导优化正态分布可视化性能，通过案例研究和实验证明触觉引导对任务表现的影响。
摘要：
触觉引导起源于心理学，描述物体设计如何影响用户物理和认知行为。以往研究将触觉理论应用于可视化，解释设计决策如何影响可视化读者的认知行为。本文证明触觉引导可以补充有效性排名，进一步解释可视化任务表现的根本原因。为此，我们对静态正态概率密度函数图进行案例研究，确定其当前触觉引导。接着，我们识别出常见概率比较任务的最佳触觉引导，并开发出一种新型触觉引导可视化——法棍图表，以支持这些引导。我们通过预先注册的研究（n=808）实证验证了设计的有效性，证明触觉引导如何影响可预测的任务表现变化。我们的发现强调了基于触觉引导的方法在提升可视化有效性方面的潜力，并为未来的设计决策提供指导。
英文摘要：
Affordances, originating in psychology, describe how an object's design influences the physical and cognitive actions users may take. Past work applied affordance theory to visualization to explain how design decisions can impact the cognitive actions of visualization readers. In this work, we demonstrate that affordances can complement effectiveness rankings by further explaining the root causes behind visualizations' task performance. To do so, we conduct a case study on static normal probability density function plots, identifying their current affordances. Next, we identify the optimal affordances for a common probability-comparison task and develop a novel affordance-driven visualization, the Croissant Chart, to support them. We empirically validate the design's effectiveness through a preregistered study (n = 808), demonstrating how affordances can inform predictable changes in task performance. Our findings underscore the potential for affordance-based approaches to enhance visualization effectiveness and inform future design decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
