Bibliographic record
Abstract
Personality characteristics alter the way we perceive things, including emotions.I focus on several negative personality characteristics -psychopathy, narcissism, and Machiavellianismknown collectively as the "Antagonistic Triad" (AT), or more commonly, the "Dark Triad".Previous research has shown that individuals who score higher in psychopathy have more difficulty identifying emotions presented in video clips, especially clips showing fear.However, it remains unclear how the other components of the AT are related to emotion perception as previous research findings are mixed.To evaluate the potential relationship between AT components and emotion perception, a sample of undergraduates first completed the FFM ATM personality test, designed to measure AT characteristics.Participants then completed an emotion identification task with video clips showing individuals expressing emotions from the following categories: happy, sad, fear, disgust.The videos presented dynamic expressions of emotion rather than static images.Emotion identification accuracy was analyzed as a function of participants' score on AT traits.High AT scores for psychopathy and narcissism were associated with lower accuracy in the perceptual task, especially for fear; the same effect was observed for Machiavellianism to a lesser degree.Theoretical implications of the current findings 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".