Functional brain network architecture supporting the learning of social networks in humans
Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimagi...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811919310894 |
| _version_ | 1830232941003800576 |
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| author | Steven H. Tompson Ari E. Kahn Emily B. Falk Jean M. Vettel Danielle S. Bassett |
| author_facet | Steven H. Tompson Ari E. Kahn Emily B. Falk Jean M. Vettel Danielle S. Bassett |
| author_sort | Steven H. Tompson |
| collection | DOAJ |
| description | Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies. |
| first_indexed | 2024-12-18T11:46:37Z |
| format | Article |
| id | doaj.art-2a484930524a4cf4add868a7f0ee993d |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-18T11:46:37Z |
| publishDate | 2020-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-2a484930524a4cf4add868a7f0ee993d2022-12-21T21:09:16ZengElsevierNeuroImage1095-95722020-04-01210116498Functional brain network architecture supporting the learning of social networks in humansSteven H. Tompson0Ari E. Kahn1Emily B. Falk2Jean M. Vettel3Danielle S. Bassett4Human Sciences Campaign, U.S. Combat Capabilities Development Center Army Research Laboratory, Aberdeen, MD, 21005, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USADepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USAAnnenberg School of Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, PA, 19104, USAHuman Sciences Campaign, U.S. Combat Capabilities Development Center Army Research Laboratory, Aberdeen, MD, 21005, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, 93106, USADepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Santa Fe Institute, Santa Fe, NM, 87501, USA; Corresponding author. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104.Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.http://www.sciencedirect.com/science/article/pii/S1053811919310894Social network learningStatistical learningSocial cognitionFunctional brain networks |
| spellingShingle | Steven H. Tompson Ari E. Kahn Emily B. Falk Jean M. Vettel Danielle S. Bassett Functional brain network architecture supporting the learning of social networks in humans NeuroImage Social network learning Statistical learning Social cognition Functional brain networks |
| title | Functional brain network architecture supporting the learning of social networks in humans |
| title_full | Functional brain network architecture supporting the learning of social networks in humans |
| title_fullStr | Functional brain network architecture supporting the learning of social networks in humans |
| title_full_unstemmed | Functional brain network architecture supporting the learning of social networks in humans |
| title_short | Functional brain network architecture supporting the learning of social networks in humans |
| title_sort | functional brain network architecture supporting the learning of social networks in humans |
| topic | Social network learning Statistical learning Social cognition Functional brain networks |
| url | http://www.sciencedirect.com/science/article/pii/S1053811919310894 |
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