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Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach
Although it is considered that two heads are better than one, related studies argued that groups rarely outperform their best members. This study examined not only whether two heads are better than one but also whether three heads are better than two or one in the context of two-armed bandit problem...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211165/ https://www.ncbi.nlm.nih.gov/pubmed/34138907 http://dx.doi.org/10.1371/journal.pone.0252122 |
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author | Harada, Tsutomu |
author_facet | Harada, Tsutomu |
author_sort | Harada, Tsutomu |
collection | PubMed |
description | Although it is considered that two heads are better than one, related studies argued that groups rarely outperform their best members. This study examined not only whether two heads are better than one but also whether three heads are better than two or one in the context of two-armed bandit problems where learning plays an instrumental role in achieving high performance. This research revealed that a U-shaped correlation exists between performance and group size. The performance was highest for either individuals or triads, but the lowest for dyads. Moreover, this study estimated learning properties and determined that high inverse temperature (exploitation) accounted for high performance. In particular, it was shown that group effects regarding the inverse temperatures in dyads did not generate higher values to surpass the averages of their two group members. In contrast, triads gave rise to higher values of the inverse temperatures than their averages of their individual group members. These results were consistent with our proposed hypothesis that learning coherence is likely to emerge in individuals and triads, but not in dyads, which in turn leads to higher performance. This hypothesis is based on the classical argument by Simmel stating that while dyads are likely to involve more emotion and generate greater variability, triads are the smallest structure which tends to constrain emotions, reduce individuality, and generate behavioral convergences or uniformity because of the ‘‘two against one” social pressures. As a result, three heads or one head were better than two in our study. |
format | Online Article Text |
id | pubmed-8211165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82111652021-06-29 Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach Harada, Tsutomu PLoS One Research Article Although it is considered that two heads are better than one, related studies argued that groups rarely outperform their best members. This study examined not only whether two heads are better than one but also whether three heads are better than two or one in the context of two-armed bandit problems where learning plays an instrumental role in achieving high performance. This research revealed that a U-shaped correlation exists between performance and group size. The performance was highest for either individuals or triads, but the lowest for dyads. Moreover, this study estimated learning properties and determined that high inverse temperature (exploitation) accounted for high performance. In particular, it was shown that group effects regarding the inverse temperatures in dyads did not generate higher values to surpass the averages of their two group members. In contrast, triads gave rise to higher values of the inverse temperatures than their averages of their individual group members. These results were consistent with our proposed hypothesis that learning coherence is likely to emerge in individuals and triads, but not in dyads, which in turn leads to higher performance. This hypothesis is based on the classical argument by Simmel stating that while dyads are likely to involve more emotion and generate greater variability, triads are the smallest structure which tends to constrain emotions, reduce individuality, and generate behavioral convergences or uniformity because of the ‘‘two against one” social pressures. As a result, three heads or one head were better than two in our study. Public Library of Science 2021-06-17 /pmc/articles/PMC8211165/ /pubmed/34138907 http://dx.doi.org/10.1371/journal.pone.0252122 Text en © 2021 Tsutomu Harada https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Harada, Tsutomu Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach |
title | Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach |
title_full | Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach |
title_fullStr | Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach |
title_full_unstemmed | Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach |
title_short | Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach |
title_sort | three heads are better than two: comparing learning properties and performances across individuals, dyads, and triads through a computational approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211165/ https://www.ncbi.nlm.nih.gov/pubmed/34138907 http://dx.doi.org/10.1371/journal.pone.0252122 |
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