Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS

Social neuroscience research has demonstrated that those who are like-minded are also ‘like-brained.’ Studies have shown that people who share similar viewpoints have greater neural synchrony with one another, and less synchrony with people who ‘see things differently.’ Although these effects have b...

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Autores principales: Dieffenbach, Macrina C, Gillespie, Grace S R, Burns, Shannon M, McCulloh, Ian A, Ames, Daniel L, Dagher, Munqith M, Falk, Emily B, Lieberman, Matthew D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812626/
https://www.ncbi.nlm.nih.gov/pubmed/33025001
http://dx.doi.org/10.1093/scan/nsaa115
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author Dieffenbach, Macrina C
Gillespie, Grace S R
Burns, Shannon M
McCulloh, Ian A
Ames, Daniel L
Dagher, Munqith M
Falk, Emily B
Lieberman, Matthew D
author_facet Dieffenbach, Macrina C
Gillespie, Grace S R
Burns, Shannon M
McCulloh, Ian A
Ames, Daniel L
Dagher, Munqith M
Falk, Emily B
Lieberman, Matthew D
author_sort Dieffenbach, Macrina C
collection PubMed
description Social neuroscience research has demonstrated that those who are like-minded are also ‘like-brained.’ Studies have shown that people who share similar viewpoints have greater neural synchrony with one another, and less synchrony with people who ‘see things differently.’ Although these effects have been demonstrated at the ‘group level,’ little work has been done to predict the viewpoints of specific ‘individuals’ using neural synchrony measures. Furthermore, the studies that have made predictions using synchrony-based classification at the individual level used expensive and immobile neuroimaging equipment (e.g. functional magnetic resonance imaging) in highly controlled laboratory settings, which may not generalize to real-world contexts. Thus, this study uses a simple synchrony-based classification method, which we refer to as the ‘neural reference groups’ approach, to predict individuals’ dispositional attitudes from data collected in a mobile ‘pop-up neuroscience’ lab. Using functional near-infrared spectroscopy data, we predicted individuals’ partisan stances on a sociopolitical issue by comparing their neural timecourses to data from two partisan neural reference groups. We found that partisan stance could be identified at above-chance levels using data from dorsomedial prefrontal cortex. These results indicate that the neural reference groups approach can be used to investigate naturally occurring, dispositional differences anywhere in the world.
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spelling pubmed-78126262021-01-25 Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS Dieffenbach, Macrina C Gillespie, Grace S R Burns, Shannon M McCulloh, Ian A Ames, Daniel L Dagher, Munqith M Falk, Emily B Lieberman, Matthew D Soc Cogn Affect Neurosci Original Manuscript Social neuroscience research has demonstrated that those who are like-minded are also ‘like-brained.’ Studies have shown that people who share similar viewpoints have greater neural synchrony with one another, and less synchrony with people who ‘see things differently.’ Although these effects have been demonstrated at the ‘group level,’ little work has been done to predict the viewpoints of specific ‘individuals’ using neural synchrony measures. Furthermore, the studies that have made predictions using synchrony-based classification at the individual level used expensive and immobile neuroimaging equipment (e.g. functional magnetic resonance imaging) in highly controlled laboratory settings, which may not generalize to real-world contexts. Thus, this study uses a simple synchrony-based classification method, which we refer to as the ‘neural reference groups’ approach, to predict individuals’ dispositional attitudes from data collected in a mobile ‘pop-up neuroscience’ lab. Using functional near-infrared spectroscopy data, we predicted individuals’ partisan stances on a sociopolitical issue by comparing their neural timecourses to data from two partisan neural reference groups. We found that partisan stance could be identified at above-chance levels using data from dorsomedial prefrontal cortex. These results indicate that the neural reference groups approach can be used to investigate naturally occurring, dispositional differences anywhere in the world. Oxford University Press 2020-10-07 /pmc/articles/PMC7812626/ /pubmed/33025001 http://dx.doi.org/10.1093/scan/nsaa115 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Manuscript
Dieffenbach, Macrina C
Gillespie, Grace S R
Burns, Shannon M
McCulloh, Ian A
Ames, Daniel L
Dagher, Munqith M
Falk, Emily B
Lieberman, Matthew D
Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS
title Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS
title_full Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS
title_fullStr Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS
title_full_unstemmed Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS
title_short Neural reference groups: a synchrony-based classification approach for predicting attitudes using fNIRS
title_sort neural reference groups: a synchrony-based classification approach for predicting attitudes using fnirs
topic Original Manuscript
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812626/
https://www.ncbi.nlm.nih.gov/pubmed/33025001
http://dx.doi.org/10.1093/scan/nsaa115
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