People often use visualizations not only to explore a dataset but also to draw generalizable conclusions about underlying models or phenomena. While previous research has viewed deviations from rational analysis as problematic, we hypothesize that human reliance on non-normative heuristics may be advantageous in certain situations. In this study, we investigate scenarios where human intuition might outperform idealized statistical rationality. Our experiment assesses participants' accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings show that, while participants generally demonstrated lower accuracy than statistical models, they often outperformed Bayesian agents, particularly when dealing with extreme samples. These results suggest that, even when deviating from rationality, human gut reactions to visualizations can provide an advantage. Our findings offer insights into how analyst intuition and statistical models can be integrated to improve inference and decision-making, with important implications for the design of visual analytics tools.

翻译：人们使用可视化不仅是为了探索数据集，也是为了对底层模型或现象得出可推广的结论。以往的研究通常将偏离理性分析视为问题，但我们假设，在某些情况下，人类对非规范性启发式的依赖可能是有益的。在本研究中，我们探究了人类直觉可能胜过理想化统计理性的场景。我们的实验评估了参与者通过双变量可视化来表征已知数据生成模型参数的准确性。研究结果表明，虽然参与者的准确性通常低于统计模型，但他们常常优于贝叶斯智能体，尤其是在处理极端样本时。这些结果表明，即使偏离理性，人类对可视化的直觉反应也能提供优势。我们的发现为如何整合分析师的直觉与统计模型以改进推理和决策提供了见解，并对可视化分析工具的设计具有重要启示。