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Record W2135607233 · doi:10.1371/journal.pone.0112990

Inhibition of Personally-Relevant Angry Faces Moderates the Effect of Empathy on Interpersonal Functioning

2015· article· en· W2135607233 on OpenAlexafffund
Vanessa Iacono, Mark A. Ellenbogen, Alexa L. Wilson, Philip Desormeau, Rami Nijjar

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEmpathyPsychologyInterpersonal communicationAngerFacial expressionCognitionEmpathic concernDevelopmental psychologySocial cognitionInterpersonal relationshipClinical psychologyPerspective-takingSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

While empathy is typically assumed to promote effective social interactions, it can sometimes be detrimental when it is unrestrained and overgeneralized. The present study explored whether cognitive inhibition would moderate the effect of empathy on social functioning. Eighty healthy young adults underwent two assessments six months apart. Participants' ability to suppress interference from distracting emotional stimuli was assessed using a Negative Affective Priming Task that included both generic and personally-relevant (i.e., participants' intimate partners) facial expressions of emotion. The UCLA Life Stress Interview and Empathy Quotient were administered to measure interpersonal functioning and empathy respectively. Multilevel modeling demonstrated that higher empathy was associated with worse concurrent interpersonal outcomes for individuals who showed weak inhibition of the personally-relevant depictions of anger. The effect of empathy on social functioning might be dependent on individuals' ability to suppress interference from meaningful emotional distractors in their environment.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.303
Teacher spread0.227 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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