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Predicting group benefits in joint multiple object tracking: Predicting group benefits
In everyday life, people often work together to accomplish a joint goal. Working together is often beneficial as it can result in a higher performance compared to working alone – a so-called “group benefit”. While several factors influencing group benefits have been investigated in a range of tasks,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545603/ https://www.ncbi.nlm.nih.gov/pubmed/37410254 http://dx.doi.org/10.3758/s13414-023-02693-6 |
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author | Wahn, Basil König, Peter Kingstone, Alan |
author_facet | Wahn, Basil König, Peter Kingstone, Alan |
author_sort | Wahn, Basil |
collection | PubMed |
description | In everyday life, people often work together to accomplish a joint goal. Working together is often beneficial as it can result in a higher performance compared to working alone – a so-called “group benefit”. While several factors influencing group benefits have been investigated in a range of tasks, to date, they have not been examined collectively with an integrative statistical approach such as linear modeling. To address this gap in the literature, we investigated several factors that are highly relevant for group benefits (i.e., task feedback, information about the co-actor’s actions, the similarity in the individual performances, and personality traits) and used these factors as predictors in a linear model to predict group benefits in a joint multiple object tracking (MOT) task. In the joint MOT task, pairs of participants jointly tracked the movements of target objects among distractor objects and, depending on the experiment, either received group performance feedback, individual performance feedback, information about the group member’s performed actions, or a combination of these types of information. We found that predictors collectively account for half of the variance and make non-redundant contributions towards predicting group benefits, suggesting that they independently influence group benefits. The model also accurately predicts group benefits, suggesting that it could be used to anticipate group benefits for individuals that have not yet performed a joint task together. Given that the investigated factors are relevant for other joint tasks, our model provides a first step towards developing a more general model for predicting group benefits across several shared tasks. |
format | Online Article Text |
id | pubmed-10545603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-105456032023-10-04 Predicting group benefits in joint multiple object tracking: Predicting group benefits Wahn, Basil König, Peter Kingstone, Alan Atten Percept Psychophys Article In everyday life, people often work together to accomplish a joint goal. Working together is often beneficial as it can result in a higher performance compared to working alone – a so-called “group benefit”. While several factors influencing group benefits have been investigated in a range of tasks, to date, they have not been examined collectively with an integrative statistical approach such as linear modeling. To address this gap in the literature, we investigated several factors that are highly relevant for group benefits (i.e., task feedback, information about the co-actor’s actions, the similarity in the individual performances, and personality traits) and used these factors as predictors in a linear model to predict group benefits in a joint multiple object tracking (MOT) task. In the joint MOT task, pairs of participants jointly tracked the movements of target objects among distractor objects and, depending on the experiment, either received group performance feedback, individual performance feedback, information about the group member’s performed actions, or a combination of these types of information. We found that predictors collectively account for half of the variance and make non-redundant contributions towards predicting group benefits, suggesting that they independently influence group benefits. The model also accurately predicts group benefits, suggesting that it could be used to anticipate group benefits for individuals that have not yet performed a joint task together. Given that the investigated factors are relevant for other joint tasks, our model provides a first step towards developing a more general model for predicting group benefits across several shared tasks. Springer US 2023-06-28 2023 /pmc/articles/PMC10545603/ /pubmed/37410254 http://dx.doi.org/10.3758/s13414-023-02693-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wahn, Basil König, Peter Kingstone, Alan Predicting group benefits in joint multiple object tracking: Predicting group benefits |
title | Predicting group benefits in joint multiple object tracking: Predicting group benefits |
title_full | Predicting group benefits in joint multiple object tracking: Predicting group benefits |
title_fullStr | Predicting group benefits in joint multiple object tracking: Predicting group benefits |
title_full_unstemmed | Predicting group benefits in joint multiple object tracking: Predicting group benefits |
title_short | Predicting group benefits in joint multiple object tracking: Predicting group benefits |
title_sort | predicting group benefits in joint multiple object tracking: predicting group benefits |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545603/ https://www.ncbi.nlm.nih.gov/pubmed/37410254 http://dx.doi.org/10.3758/s13414-023-02693-6 |
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