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Enregistrement W2115599452 · doi:10.1080/10463283.2010.543314

A social neuroscience approach to self and social categorisation: A new look at an old issue

2010· review· en· W2115599452 sur OpenAlexaboutno aff
Jay J. Van Bavel, William A. Cunningham

Notice bibliographique

RevueEuropean Review of Social Psychology · 2010
Typereview
Langueen
DomaineSocial Sciences
ThématiqueSocial and Intergroup Psychology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyIdentity (music)PerceptionSocial identity theorySocial neuroscienceSalientSocial psychologySocial cognitionSocial groupCognitive psychologyCognitive scienceCognitionAestheticsArt

Résumé

récupéré en direct d'OpenAlex

Abstract We take a social neuroscience approach to self and social categorisation in which the current self-categorisation(s) is constructed from relatively stable identity representations stored in memory (such as the significance of one's social identity) through iterative and interactive perceptual and evaluative processing. This approach describes these processes across multiple levels of analysis, linking the effects of self-categorisation and social identity on perception and evaluation to brain function. We review several studies showing that self-categorisation with an arbitrary group can override the effects of more visually salient, cross-cutting social categories on social perception and evaluation. The top-down influence of self-categorisation represents a powerful antecedent-focused strategy for suppressing racial bias without many of the limitations of a more response-focused strategy. Finally we discuss the implications of this approach for our understanding of social perception and evaluation and the neural substrates of these processes. Keywords: Social neuroscienceSocial identitySocial categoriesSelf-categorisationSocial perceptionSocial cognitionEvaluationAttitudesIntergroup relationsPrejudiceRacial biasAutomaticityNew lookControlTop-downSalienceAmygdalaFusiform gyrusIndividuationCategorisation Acknowledgments This manuscript was part of the Jay Van Bavel's PhD Dissertation at the University of Toronto. The authors would like to thank Benjamin Giguere, Jillian Swencionis, Y. Jenny Xiao, Michael Wohl, Miles Hewstone, Wolfgang Stroebe, and four anonymous reviewers for their thoughtful comments on various stages of this manuscript. This research was supported by grants from the Social Sciences and Humanities Research Council of Canada to Jay Van Bavel and the National Science Foundation (BCS-0819250) to William Cunningham. Notes 1 Although it is beyond the scope of the current paper, we direct interested readers to a forthcoming issue of Social Cognition in which the promise and limitations of social neuroscience are discussed in greater detail. 2 Social psychologists traditionally differentiate aspects of social categorisation (classifying others according to categorical markers), stereotyping (the activation and application of semantic information about others based on their category membership) and prejudice (the evaluation of others based on their social category membership) (Fiske & Neuberg, 1990; Kunda & Sinclair, Citation1999). Although all three subcomponents often work in concert, their co-occurrence when perceiving others is not necessary. Moreover, while our research focuses on the implications of social categorisation for evaluation, the causal order may be reversed (e.g., Hugenberg & Bodenhausen,Citation 2004). 3 It is important to note that the current experiments employed a modified version of the minimal group paradigm (Tajfel et al., Citation1971): to enhance self-categorisation participants were told that the Lions and Tigers were in competition and saw their own face appear during the learning task. In addition, participants actually had to learn ingroup and outgroup faces prior to completing the dependent measures. Although this variant of the minimal group paradigm departed from the classic version, we have replicated these results in follow-up studies in which participants did not see their own face and in which there was no reference to competition. We therefore feel confident in loosely describing the groups in these studies as minimal groups. However, it does remain an open question whether mere categorisation is sufficient to override automatic racial bias (see Van Bavel & Cunningham, Citation2009a, for a discussion). 4 There is some evidence that perceiving others as specific individuals does not lead to enhanced FFA activity (Kriegeskorte, Formisano, Sorger, & Goebel, Citation2007). Nevertheless we use the term individuation to reflect the in-depth structural analysis of faces that is well established within the FFA literature (Kanwisher & Yovel, Citation2006). 5 The negative correlation between amygdala activity and ACC and lateral PFC activity to Black compared to White faces between the subliminal and supraliminal conditions occurred in a relatively simple perceptual task that was not explicitly focused on control. This raises a question about what exactly it is that White participants are to suppress or inhibit when they see a Black face, and why they would feel motivated to do so in a mere perceptual task (Amodio, Citation2008). This issue has actually been directly addressed by Richeson and colleagues (2003) in which differential engagement of the dlPFC during an almost identical task mediated the relationship between implicit measures of racial bias and impairment on a classic cognitive control task (the Stroop) following an interracial interaction. In addition, there is extensive evidence in the social psychological literature showing that people attempt to control their racial bias in a host of situations and tasks that do not explicitly require control, especially when people are motivated by personal beliefs or values to be egalitarian (see Crandall & Eshleman, Citation2003 for a review). Thus egalitarian participants may attempt to control emotional and cognitive responses to race, even when control is not explicitly required for the task. 6 Note that categories, like race, may be difficult to ignore if they are central to an individual's self-definition. Thus, creating alternative, meaningful bases for categorisation in conjunction with existing categories like race may bypass the reactive effects of distinctiveness threat. Indeed, future research should examine whether individual differences in the centrality of race moderate the effects of our mixed-race manipulation on intergroup bias.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,807
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,002
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,119
Tête enseignante GPT0,452
Écart entre enseignants0,333 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations77
Publié2010
Routes d'admission1
Résumé présentoui

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