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Record W2139203871 · doi:10.1093/scan/nsr035

Intergroup differences in the sharing of emotive states: neural evidence of an empathy gap

2011· article· en· W2139203871 on OpenAlexafffund
Jennifer N. Gutsell, Michael Inzlicht

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2011
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersOntario Ministry of Research and Innovation
KeywordsEmpathyPsychologyIngroups and outgroupsProsocial behaviorOutgroupFeelingPrejudice (legal term)Social neuroscienceSocial psychologyDevelopmental psychologySocial cognitionCognitionNeuroscience

Abstract

fetched live from OpenAlex

Empathy facilitates prosocial behavior and social understanding. Here, however, we suggest that the most basic mechanism of empathy--the intuitive sharing of other's emotional and motivational states--is limited to those we like. Measuring electroencephalographic (EEG) alpha oscillations as people observed ingroup vs outgroup members, we found that participants showed similar activation patterns when feeling sad as when they observed ingroup members feeling sad. In contrast, participants did not show these same activation patterns when observing outgroup members and even less so the more they were prejudiced. These findings provide evidence from brain activity for an ingroup bias in empathy: empathy may be restricted to close others and, without active effort, may not extend to outgroups, potentially making them likely targets for prejudice and discrimination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.307
GPT teacher head0.354
Teacher spread0.047 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations204
Published2011
Admission routes2
Has abstractyes

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