Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios
Abstract Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individua...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-20704-8 |
| _version_ | 1811202866157715456 |
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| author | Zachariah R. Cross Alex Chatburn Lee Melberzs Philip Temby Diane Pomeroy Matthias Schlesewsky Ina Bornkessel-Schlesewsky |
| author_facet | Zachariah R. Cross Alex Chatburn Lee Melberzs Philip Temby Diane Pomeroy Matthias Schlesewsky Ina Bornkessel-Schlesewsky |
| author_sort | Zachariah R. Cross |
| collection | DOAJ |
| description | Abstract Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and a logistic regression machine learning model, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dyad. Task-related theta and alpha activity during easy training tasks predicted later performance on complex training to a greater extent than prior behaviour. These findings complement laboratory-based research on both oscillatory and aperiodic activity in higher-order cognition and provide evidence that theta and alpha activity play a critical role in complex task performance in team environments. |
| first_indexed | 2024-04-12T02:45:15Z |
| format | Article |
| id | doaj.art-58d775c485d74475a2cd1493fbe22c79 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T02:45:15Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-58d775c485d74475a2cd1493fbe22c792022-12-22T03:51:11ZengNature PortfolioScientific Reports2045-23222022-09-0112111510.1038/s41598-022-20704-8Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenariosZachariah R. Cross0Alex Chatburn1Lee Melberzs2Philip Temby3Diane Pomeroy4Matthias Schlesewsky5Ina Bornkessel-Schlesewsky6Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South AustraliaCognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South AustraliaDepartment of Defence, Australian ArmyLand Division, Defence Science and Technology GroupLand Division, Defence Science and Technology GroupCognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South AustraliaCognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South AustraliaAbstract Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and a logistic regression machine learning model, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dyad. Task-related theta and alpha activity during easy training tasks predicted later performance on complex training to a greater extent than prior behaviour. These findings complement laboratory-based research on both oscillatory and aperiodic activity in higher-order cognition and provide evidence that theta and alpha activity play a critical role in complex task performance in team environments.https://doi.org/10.1038/s41598-022-20704-8 |
| spellingShingle | Zachariah R. Cross Alex Chatburn Lee Melberzs Philip Temby Diane Pomeroy Matthias Schlesewsky Ina Bornkessel-Schlesewsky Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios Scientific Reports |
| title | Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios |
| title_full | Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios |
| title_fullStr | Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios |
| title_full_unstemmed | Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios |
| title_short | Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios |
| title_sort | task related intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team based training scenarios |
| url | https://doi.org/10.1038/s41598-022-20704-8 |
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