People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users' prior beliefs in interactions with data presentations like visualizations. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization evaluation. In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people's judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don't behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation framework for visualizations, including of uncertainty.
翻译:人们自然而然地在解释新信息时运用了他们先前的信念,然而,在计算用户先前的信仰在与可视化等数据演示中的影响方面,却没有多少正式模型。我们展示了一种贝叶西亚认知模型,以了解人们如何根据先前的信念解释可视化,并展示了这一模型如何为改进可视化评估提供指南。在一项第一项研究中,我们展示了如何将贝叶西亚认知模型应用到简单的可视化假设中,表明人们的判断与他们近似贝叶西亚推断的假设一致。在第二项研究中,我们评估了我们对巴伊西亚行为的观察对激发人们主观分布和不同数据集的不同技术的敏感程度。我们发现,人们的行为与巴伊斯人的大型抽样数据集预测不一致,而这种差异不能通过引证技术来解释。在一项最后的研究中,我们展示了如何将典型的贝伊斯人的推断用作可视化的评估框架,包括不确定性。