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Record W4234105982 · doi:10.1177/0265407520919991

No effect of attachment avoidance on visual disengagement from a romantic partner’s face

2020· article· en· W4234105982 on OpenAlexafffund
Shayne Sanscartier, Jessica A. Maxwell, Penelope Lockwood

Bibliographic record

VenueJournal of Social and Personal Relationships · 2020
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyDisengagement theoryClosenessRomanceContext (archaeology)Social psychologyDevelopmental psychologyFacial expressionCommunication

Abstract

fetched live from OpenAlex

Attachment avoidance (discomfort with closeness and intimacy) has been inconsistently linked to visual disengagement from emotional faces, with some studies finding disengagement toward specific emotional faces and others finding no effects. Although most studies use stranger faces as stimuli, it is likely that attachment effects would be most pronounced in the context of attachment relationships. The present study ( N = 92) combined ecologically valid stimuli (i.e., pictures of romantic partner’s face) with eye-tracking methods to more precisely test whether highly avoidant individuals are faster at disengaging from emotional faces. Unexpectedly, attachment avoidance had no effect on saccadic reaction time, regardless of face type or emotion. Instead, all participants took longer to disengage from romantic partner faces than from strangers’ faces, although this effect should be replicated in the future. Our results suggest that romantic attachments capture visual attention on an oculomotor level, regardless of one’s personal attachment orientations.

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)0.004
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.093
GPT teacher head0.384
Teacher spread0.291 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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