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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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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 |
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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 |
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