Bibliographic record
Abstract
The purpose of this study was to determine what effect psychopathic traits have on the ability to express both genuine and feigned emotional expressions through a detailed analysis of facial characteristics of emotion. Despite the wide array of research on psychopathic traits and emotional dysfunction, most studies have focused on recognition rather than expression of emotion. Participants (n = 121) were assessed for psychopathic traits and randomly assigned into a feigned or genuine emotional condition, and asked to display each of the six core emotions (i.e., happiness, fear, anger, surprise, disgust, and sadness). Each face was then coded for the presence of facial musculature action units using a standardized coding system. Results indicated that those feigned group produced more authentic facial expressions than their genuine counterparts. Limited main effects were found related to psychopathy and overall facial expressions; however, interesting patterns of specific action units were noted. Specifically, those high in psychopathic traits engaged in more authentic and pronounced expressions of specific facial musculature movements in some emotional expressions (i.e., fear and disgust). Implications concerning methods of coding, emotion induction, and facial affective mimicry are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".