MétaCan
Menu
Retour à la cohorte
Enregistrement W3194816738 · doi:10.3724/sp.j.1042.2021.01783

The neural mechanism of self-face recognition: An ALE meta-analysis of fMRI studies

2021· article· en· W3194816738 sur OpenAlexaboutno aff
Yuting Na, Yuwen Zhao, Lili Guan

Notice bibliographique

RevueAdvances in Psychological Science · 2021
Typearticle
Langueen
DomaineNeuroscience
ThématiqueFace Recognition and Perception
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMechanism (biology)PsychologyFacial recognition systemMeta-analysisComputer scienceNeuroscienceCognitive psychologyPattern recognition (psychology)MedicinePhysics

Résumé

récupéré en direct d'OpenAlex

Self-face recognition reflects the process whereby someone can recognize their own face by distinguishing it from the other. Generally, people recognize self-faces faster than they do other faces, and self-face recognition can elicit enhanced brain activity compared with that of other face recognitions. Researching self-face recognition is valuable because of its close relationship with self-awareness. Recently, many studies used functional magnetic resonance imaging (fMRI) to investigate the neural basis of self-face recognition. However, there are no consistent results regarding the key brain regions involved in self-face recognition. Therefore, in the current study, a quantitative meta-analysis of fMRI studies, using activation likelihood estimation (ALE), was performed to localize the neural structures engaged in recognizing self-face. Twenty-seven studies involving 635 participants met the inclusion criteria. The meta-analysis was conducted in the standard Montreal Neurological Institute (MNI) space, and we translated results reported using Talairach coordinates into MNI coordinates. The statistical analysis of the transformed foci was validated using the Monte Carlo Simulation (1,000 permutations) with a cluster-forming voxel-level threshold at uncorrected p < 0.001 combined with cluster-size correction using family-wise error at p < 0.05. We used Mango software to project the activation coordinates onto a brain template to provide a visual representation of activation distributions. Results showed that the contrast of self-face versus other-face displayed increased activations of the right superior parietal lobule/precuneus/middle occipital gyrus, middle frontal gyrus, inferior frontal gyrus, fusiform gyrus, postcentral gyrus, insula, and left precuneus. There was no active region in the contrast of other-face versus self-face. Based on the meta-analysis results and on previous event-related potential (ERP) studies, self-face recognition may involve two levels of processing, perceptual integration processing and the accompanying process of evaluation and emotional response. In the process of recognizing self-face, the occipital gyrus, fusiform gyrus, and precuneus are involved in the perceptual integration process. The occipital cortices may be involved in the processing of self-related facial features in the early stages of face recognition. The fusiform gyrus is involved in low-level sensory processing, and it is also sensitive to the categorization of faces in terms of self versus nonself. The precuneus is recruited in the perceptual integration of self-related information. The superior parietal lobule, middle frontal gyrus, inferior frontal gyrus, and insula are mainly recruited in the evaluation and the emotional response at the middle and late stages of recognizing self-face. The superior parietal lobule and middle frontal gyrus have been shown to play an important role in the processing of evaluating self-face. Moreover, their activations reflect the influence of social and cultural factors on self-face recognition. The inferior frontal gyrus and insula are also involved in the processing of evaluating self-face. Furthermore, they play a direct role in the subjective emotional experience of viewing or evaluating self-face. In sum, the current meta-analysis reveals the neural basis of self-face recognition and suggests two levels of processing of self-face recognition (perceptual integration processing and the accompanying process of evaluation and emotional response). The current study provides support for investigating the neural mechanism of self-face recognition and, based on the limitations of previous studies, makes suggestions for future research. Future studies could use magnetoencephalography (MEG) or simultaneous EEG-fMRI to combine brain location and time course, thereby revealing the cognitive and neural mechanisms of self-face recognition. Close attention should be paid to the structural and functional connectivity of brain areas and brain networks and to the neural correlates of interoception and self-face recognition. Clinical studies should investigate abnormal neural activity in patients with self-processing impairment and explore the influence of threatening information on self-face recognition.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,108

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,031
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0080,042
Bibliométrie0,0060,008
Études des sciences et des technologies0,0010,001
Communication savante0,0040,002
Science ouverte0,0020,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,264
Tête enseignante GPT0,472
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeMéta-analyse
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAdvances in Psychological ScienceMême sujetFace Recognition and PerceptionTravaux en français237 207