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 inNeuroscience and biobehavioral reviews Vol. 127; pp. 820 - 836
Main Authors Xu, Pengfei, Peng, Shaoling, Luo, Yue-jia, Gong, Gaolang
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2021
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ISSN0149-7634
1873-7528
1873-7528
DOI10.1016/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.
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
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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
URI https://dx.doi.org/10.1016/j.neubiorev.2021.05.023
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