Analyzing and supporting mental representations and strategies in solving Bayesian problems

Julia Sirock; Markus Vogel; Tina Seufert

doi:10.3389/fpsyg.2023.1085470

Analyzing and supporting mental representations and strategies in solving Bayesian problems

Front Psychol. 2023 Jun 15:14:1085470. doi: 10.3389/fpsyg.2023.1085470. eCollection 2023.

Authors

Julia Sirock¹, Markus Vogel¹, Tina Seufert²

Affiliations

¹ Mathematics Education, Institute of Mathematics and Computer Science, University of Education Heidelberg, Heidelberg, Germany.
² Institute for Learning and Instruction, Department for Psychology and Education, Ulm University, Ulm, Germany.

Abstract

Solving Bayesian problems poses many challenges, such as identifying relevant numerical information, classifying, and translating it into mathematical formula language, and forming a mental representation. This triggers research on how to support the solving of Bayesian problems. The facilitating effect of using numerical data in frequency format instead of probabilities is well documented, as is the facilitating effect of given visualizations of statistical data. The present study not only compares the visualizations of the 2 × 2 table and the unit square, but also focuses on the results obtained from the self-creation of these visualizations by the participants. Since it has not yet been investigated whether the better correspondence between external and internal visualization also has an effect on cognitive load when solving Bayesian tasks, passive and active cognitive load are additionally measured. Due to the analog character and the proportional representation of the numerical information by the unit square, it is assumed that the passive cognitive load is lower when using the unit square as visualization than when using the 2 × 2 table. The opposite is true for active cognitive load.

Keywords: Bayesian problem-solving; cognitive load; coherence formation; mental model; multiple representations; visualization.