Analyzing and supporting mental representations and strategies in solving Bayesian problems

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 ef...

Descripción completa

Detalles Bibliográficos
Autores principales: Sirock, Julia, Vogel, Markus, Seufert, Tina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311000/
https://www.ncbi.nlm.nih.gov/pubmed/37397310
http://dx.doi.org/10.3389/fpsyg.2023.1085470
_version_ 1785066651774877696
author Sirock, Julia
Vogel, Markus
Seufert, Tina
author_facet Sirock, Julia
Vogel, Markus
Seufert, Tina
author_sort Sirock, Julia
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10311000
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-103110002023-07-01 Analyzing and supporting mental representations and strategies in solving Bayesian problems Sirock, Julia Vogel, Markus Seufert, Tina Front Psychol Psychology 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. Frontiers Media S.A. 2023-06-15 /pmc/articles/PMC10311000/ /pubmed/37397310 http://dx.doi.org/10.3389/fpsyg.2023.1085470 Text en Copyright © 2023 Sirock, Vogel and Seufert. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Sirock, Julia
Vogel, Markus
Seufert, Tina
Analyzing and supporting mental representations and strategies in solving Bayesian problems
title Analyzing and supporting mental representations and strategies in solving Bayesian problems
title_full Analyzing and supporting mental representations and strategies in solving Bayesian problems
title_fullStr Analyzing and supporting mental representations and strategies in solving Bayesian problems
title_full_unstemmed Analyzing and supporting mental representations and strategies in solving Bayesian problems
title_short Analyzing and supporting mental representations and strategies in solving Bayesian problems
title_sort analyzing and supporting mental representations and strategies in solving bayesian problems
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311000/
https://www.ncbi.nlm.nih.gov/pubmed/37397310
http://dx.doi.org/10.3389/fpsyg.2023.1085470
work_keys_str_mv AT sirockjulia analyzingandsupportingmentalrepresentationsandstrategiesinsolvingbayesianproblems
AT vogelmarkus analyzingandsupportingmentalrepresentationsandstrategiesinsolvingbayesianproblems
AT seuferttina analyzingandsupportingmentalrepresentationsandstrategiesinsolvingbayesianproblems