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Insight into the ten-penny problem: guiding search by constraints and maximization
For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Rec...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533865/ https://www.ncbi.nlm.nih.gov/pubmed/27592343 http://dx.doi.org/10.1007/s00426-016-0800-3 |
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author | Öllinger, Michael Fedor, Anna Brodt, Svenja Szathmáry, Eörs |
author_facet | Öllinger, Michael Fedor, Anna Brodt, Svenja Szathmáry, Eörs |
author_sort | Öllinger, Michael |
collection | PubMed |
description | For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving. |
format | Online Article Text |
id | pubmed-5533865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-55338652017-08-11 Insight into the ten-penny problem: guiding search by constraints and maximization Öllinger, Michael Fedor, Anna Brodt, Svenja Szathmáry, Eörs Psychol Res Original Article For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving. Springer Berlin Heidelberg 2016-09-03 2017 /pmc/articles/PMC5533865/ /pubmed/27592343 http://dx.doi.org/10.1007/s00426-016-0800-3 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Öllinger, Michael Fedor, Anna Brodt, Svenja Szathmáry, Eörs Insight into the ten-penny problem: guiding search by constraints and maximization |
title | Insight into the ten-penny problem: guiding search by constraints and maximization |
title_full | Insight into the ten-penny problem: guiding search by constraints and maximization |
title_fullStr | Insight into the ten-penny problem: guiding search by constraints and maximization |
title_full_unstemmed | Insight into the ten-penny problem: guiding search by constraints and maximization |
title_short | Insight into the ten-penny problem: guiding search by constraints and maximization |
title_sort | insight into the ten-penny problem: guiding search by constraints and maximization |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533865/ https://www.ncbi.nlm.nih.gov/pubmed/27592343 http://dx.doi.org/10.1007/s00426-016-0800-3 |
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