Emotional content analysis among psychopathic individuals during emotional induction by IAPS pictures
Bibliographic record
Abstract
Background Emotional processes among psychopathic individuals have been consistently investigated. Although content analysis is interesting for evaluating emotional characteristics, few data exist concerning Psychopath’s speech content following affective and neutral images. Method Population included male forensic inpatients (n=47) from Security Hospital. The inpatients were divided into: “Psychopaths” (n=24, PCL-R total score >25), “Intermediates” (n=12, score from 15 to 24.9) and “Non-psychopaths” (n=11, score <14.9). TROPES analyses and EMOTAIX scenario tools examined the narrative’s emotional characteristics. We tested the hypothesis that psychopaths report fewer emotional words on all images, particularly on negative-valence images. Results Our results on the whole do not support this hypothesis but suggested rather a specific discordance in the verbal emotional treatment (exclusively PCL-R interpersonal factor) but not in terms of the subjective evaluation. Moreover, this factor was positively correlated with the number of the self-referring pronouns (“I”, me) setting whereas PCL-R Social Deviance factor was positively correlated with action verbs. Conclusion Speech outputs of psychopaths present specificities in terms of emotional content and verbal setting. The results are congruent with the notion that psychopathy combines both functionality and subtle impairment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".