MétaCan
Menu
← Back to cohort
Record W6948055384 · doi:10.5061/dryad.kh1893225

Data from: Stressed connections: cortisol levels following acute psychosocial stress disrupt affiliative mimicry in humans

2020· dataset· en· W6948055384 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMcGill University
Fundersnot available
KeywordsMimicryFacial electromyographyStressorFacial expressionStress (linguistics)Social stressPsychosocialEndocrine system

Abstract

fetched live from OpenAlex

Mimicry, and especially spontaneous facial mimicry, is a rudimentary element of social–emotional experience that is well-conserved across numerous species. Although such mimicry is thought to be a relatively automatic process, research indicates that contextual factors can influence mimicry, especially in humans. Here, we extend this work by investigating the effect of acute psychosocial stress on spontaneous facial mimicry. Participants performed a spontaneous facial mimicry task with facial electromyography (fEMG) at baseline and approximately one month later, following an acute psychosocial stressor (Trier Social Stress Test). Results show that the magnitude of the endocrine stress response reduced zygomaticus major reactivity, and specifically spontaneous facial mimicry for positive social stimuli (i.e. smiles). Individuals with higher levels of the stress hormone cortisol showed a more blunted fEMG response to smiles, but not to frowns. Conversely, stress had no effect on corrugator supercilii activation (i.e. frowning to frowns). These findings highlight the importance of the biological stress response system in this basic element of social–emotional experience.

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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0230.022

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 head0.104
GPT teacher head0.317
Teacher spread0.213 · 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
GenreDataset

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicSpecies Distribution and Climate Change→French-language works237,207→