When You Smile, Do I Smile? A Proposed Study Examining Conscious Level Emotion Regulation in a Developmental Sample
Bibliographic record
Abstract
The ability to conceal a proponent emotional response and instead, express a more socially appropriate facial expression has had phylogenetic and ontogenetic advantages throughout human evolution and development. Understanding the developmental trajectory of this type of emotion regulation is imperative and can be empirically examined using a facial mimicry paradigm to study inhibitory control. Facial mimicry enables an individual to imitate the emotional expression of a social other, whereas inhibitory control examines an individual’s ability to suppress a dominant responses in favour of a correct response. The present paper proposes a paradigm during which individuals are instructed to imitate the opposite facial expression of that displayed on a screen. In addition, the neural correlates of emotion regulation are proposed to be simultaneously measured using functional magnetic resonance imaging (fMRI). Expected results are discussed with a critical focus on clinical implications for individuals with deficits in emotion regulation, in particular, those with Autism Spectrum Disorders (ASD) and individuals prone to psychopathic symptoms in adulthood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".