Böcherer-Linder Katharina, Eichler Andreas
Institute of Mathematics Education, University of Education, Freiburg Freiburg, Germany.
Institute of Mathematics, University of Kassel Kassel, Germany.
Front Psychol. 2017 Jan 6;7:2026. doi: 10.3389/fpsyg.2016.02026. eCollection 2016.
It is an ongoing debate, what properties of visualizations increase people's performance when solving Bayesian reasoning tasks. In the discussion of the properties of two visualizations, i.e., the tree diagram and the unit square, we emphasize how both visualizations make relevant subset relations transparent. Actually, the unit square with natural frequencies reveals the subset relation that is essential for the Bayes' rule in a numerical and geometrical way whereas the tree diagram with natural frequencies does it only in a numerical way. Accordingly, in a first experiment with 148 university students, the unit square outperformed the tree diagram when referring to the students' ability to quantify the subset relation that must be applied in Bayes' rule. As hypothesized, in a second experiment with 143 students, the unit square was significantly more effective when the students' performance in tasks based on Bayes' rule was regarded. Our results could inform the debate referring to Bayesian reasoning since we found that the graphical transparency of nested sets could explain these visualizations' effect.
在解决贝叶斯推理任务时,可视化的哪些属性能够提高人们的表现,这是一个仍在进行的争论。在讨论两种可视化工具(即树形图和单位正方形)的属性时,我们强调了这两种可视化工具是如何使相关子集关系变得清晰明了的。实际上,带有自然频率的单位正方形以数值和几何方式揭示了对贝叶斯规则至关重要的子集关系,而带有自然频率的树形图仅以数值方式做到这一点。因此,在对148名大学生进行的首次实验中,就学生量化贝叶斯规则中必须应用的子集关系的能力而言,单位正方形的表现优于树形图。正如所假设的那样,在对143名学生进行的第二次实验中,当考虑学生在基于贝叶斯规则的任务中的表现时,单位正方形的效果显著更佳。我们的研究结果可以为有关贝叶斯推理的争论提供参考,因为我们发现嵌套集合的图形透明度可以解释这些可视化工具的效果。