Examining motivational profiles in the dark personality tetrad using an approach-avoidance conflict task
Bibliographic record
Abstract
The present studies evaluated motives associated with the Dark Tetrad traits (Machiavellianism, narcissism, psychopathy, and sadism) using a computerized approach-avoidance conflict task (AACT). Study 1 used an Emoticon AACT with a forced choice between smiley and frowny face icons. In Phase 1, participants (n = 197) were shown a positive image if they chose to move a stick-figure manikin toward the smiley icon, and were shown a negative image if they chose the frowny icon. In Phase 2, they were offered a varying number of points (0, 1, 5, 25, or 50) for choosing the frowny icon. We found that sadism, Machiavellianism, and psychopathy were associated with accepting fewer points to approach the frowny icon that cued a negative image. This same pattern manifested for participants with low empathy levels, particularly when affective resonance was low and affective dissonance was high. Study 2 (n = 191) used an Image AACT where the choice alternatives were positive and negative images, which produced smiley or frowny icons, respectively. Sadism and dissonant emotional tendencies predicted choices directed toward negative images in Phase 2. In Studies 3 (n = 288) and 4 (n = 276), we confirmed our Emoticon AACT findings using gender-balanced samples. Studies 3 and 4 also introduced a viewing-time task (VTT). Sadism and dissonant emotional tendencies predicted decreased viewing times for positive images, but did not predict increased viewing times for negative images, suggesting that negative emotional reactions produced by the positive images were the primary motivating factor in the present AACT. Overall, our findings serve as further evidence of the different motives underlying socially aversive tendencies.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".