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Record 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 on OpenAlexaboutno aff
Jay J. Van Bavel, William A. Cunningham

Bibliographic record

VenueEuropean Review of Social Psychology · 2010
Typereview
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyIdentity (music)PerceptionSocial identity theorySocial neuroscienceSalientSocial psychologySocial cognitionSocial groupCognitive psychologyCognitive scienceCognitionAestheticsArt

Abstract

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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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.119
GPT teacher head0.452
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations77
Published2010
Admission routes1
Has abstractyes

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