Facial expression recognition: A meta-analytic review of theoretical models and neuroimaging evidence
•Brain organizations of facial expression recognition support the constructionist hypothesis.•Common brain activation and connectivity distributions are shared by different discrete/dimensional facial expressions.•The amygdala-centered 'core' affect system are connected to distributed netw...
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Published in | Neuroscience and biobehavioral reviews Vol. 127; pp. 820 - 836 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.08.2021
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Subjects | |
Online Access | Get full text |
ISSN | 0149-7634 1873-7528 1873-7528 |
DOI | 10.1016/j.neubiorev.2021.05.023 |
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Abstract | •Brain organizations of facial expression recognition support the constructionist hypothesis.•Common brain activation and connectivity distributions are shared by different discrete/dimensional facial expressions.•The amygdala-centered 'core' affect system are connected to distributed networks.•The brain organizations of facial expression recognition are flexibly asymmetrical.
Discrimination of facial expressions is an elementary function of the human brain. While the way emotions are represented in the brain has long been debated, common and specific neural representations in recognition of facial expressions are also complicated. To examine brain organizations and asymmetry on discrete and dimensional facial emotions, we conducted an activation likelihood estimation meta-analysis and meta-analytic connectivity modelling on 141 studies with a total of 3138 participants. We found consistent engagement of the amygdala and a common set of brain networks across discrete and dimensional emotions. The left-hemisphere dominance of the amygdala and AI across categories of facial expression, but category-specific lateralization of the vmPFC, suggesting a flexibly asymmetrical neural representations of facial expression recognition. These results converge to characteristic activation and connectivity patterns across discrete and dimensional emotion categories in recognition of facial expressions. Our findings provide the first quantitatively meta-analytic brain network-based evidence supportive of the psychological constructionist hypothesis in facial expression recognition. |
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AbstractList | Discrimination of facial expressions is an elementary function of the human brain. While the way emotions are represented in the brain has long been debated, common and specific neural representations in recognition of facial expressions are also complicated. To examine brain organizations and asymmetry on discrete and dimensional facial emotions, we conducted an activation likelihood estimation meta-analysis and meta-analytic connectivity modelling on 141 studies with a total of 3138 participants. We found consistent engagement of the amygdala and a common set of brain networks across discrete and dimensional emotions. The left-hemisphere dominance of the amygdala and AI across categories of facial expression, but category-specific lateralization of the vmPFC, suggesting a flexibly asymmetrical neural representations of facial expression recognition. These results converge to characteristic activation and connectivity patterns across discrete and dimensional emotion categories in recognition of facial expressions. Our findings provide the first quantitatively meta-analytic brain network-based evidence supportive of the psychological constructionist hypothesis in facial expression recognition.Discrimination of facial expressions is an elementary function of the human brain. While the way emotions are represented in the brain has long been debated, common and specific neural representations in recognition of facial expressions are also complicated. To examine brain organizations and asymmetry on discrete and dimensional facial emotions, we conducted an activation likelihood estimation meta-analysis and meta-analytic connectivity modelling on 141 studies with a total of 3138 participants. We found consistent engagement of the amygdala and a common set of brain networks across discrete and dimensional emotions. The left-hemisphere dominance of the amygdala and AI across categories of facial expression, but category-specific lateralization of the vmPFC, suggesting a flexibly asymmetrical neural representations of facial expression recognition. These results converge to characteristic activation and connectivity patterns across discrete and dimensional emotion categories in recognition of facial expressions. Our findings provide the first quantitatively meta-analytic brain network-based evidence supportive of the psychological constructionist hypothesis in facial expression recognition. •Brain organizations of facial expression recognition support the constructionist hypothesis.•Common brain activation and connectivity distributions are shared by different discrete/dimensional facial expressions.•The amygdala-centered 'core' affect system are connected to distributed networks.•The brain organizations of facial expression recognition are flexibly asymmetrical. Discrimination of facial expressions is an elementary function of the human brain. While the way emotions are represented in the brain has long been debated, common and specific neural representations in recognition of facial expressions are also complicated. To examine brain organizations and asymmetry on discrete and dimensional facial emotions, we conducted an activation likelihood estimation meta-analysis and meta-analytic connectivity modelling on 141 studies with a total of 3138 participants. We found consistent engagement of the amygdala and a common set of brain networks across discrete and dimensional emotions. The left-hemisphere dominance of the amygdala and AI across categories of facial expression, but category-specific lateralization of the vmPFC, suggesting a flexibly asymmetrical neural representations of facial expression recognition. These results converge to characteristic activation and connectivity patterns across discrete and dimensional emotion categories in recognition of facial expressions. Our findings provide the first quantitatively meta-analytic brain network-based evidence supportive of the psychological constructionist hypothesis in facial expression recognition. |
Author | Gong, Gaolang Xu, Pengfei Peng, Shaoling Luo, Yue-jia |
Author_xml | – sequence: 1 givenname: Pengfei surname: Xu fullname: Xu, Pengfei email: xupf@szu.edu.cn organization: Center for Brain Disorders and Cognitive Science, Shenzhen Key Laboratory of Affective and Social Neuroscience, Shenzhen University, Shenzhen, China – sequence: 2 givenname: Shaoling surname: Peng fullname: Peng, Shaoling organization: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China – sequence: 3 givenname: Yue-jia surname: Luo fullname: Luo, Yue-jia organization: Center for Brain Disorders and Cognitive Science, Shenzhen Key Laboratory of Affective and Social Neuroscience, Shenzhen University, Shenzhen, China – sequence: 4 givenname: Gaolang surname: Gong fullname: Gong, Gaolang email: gaolang.gong@bnu.edu.cn organization: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China |
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Keywords | Meta-analytic connectivity modelling (MACM) Facial expression recognition Locationist hypothesis Constructionist hypothesis Activation likelihood estimation (ALE) |
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Snippet | •Brain organizations of facial expression recognition support the constructionist hypothesis.•Common brain activation and connectivity distributions are shared... Discrimination of facial expressions is an elementary function of the human brain. While the way emotions are represented in the brain has long been debated,... |
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SubjectTerms | Activation likelihood estimation (ALE) Constructionist hypothesis Facial expression recognition Locationist hypothesis Meta-analytic connectivity modelling (MACM) |
Title | Facial expression recognition: A meta-analytic review of theoretical models and neuroimaging evidence |
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