Crocodile tears: Facial, verbal and body language behaviours associated with genuine and fabricated remorse.
Bibliographic record
Abstract
Emotional deception is a common behaviour that can have major consequences if undetected. For example, the sincerity of an offender's expressed remorse is an important factor in sentencing and parole hearings. The present study was the first to investigate the nature of true and false remorse. We examined facial, verbal and body language behaviours associated with emotional deception in videotaped accounts of true personal transgressions accompanied by either genuine or falsified remorse. Analyses of nearly 300,000 frames indicated that descriptions of falsified remorse were associated with a greater range of emotional expressions. Further, sequential analyses revealed that negative emotions were more commonly followed by other emotions-rather than a return to neutral emotion-in falsified versus sincere remorse. Participants also exhibited more speech hesitations while expressing deceptive relative to genuine remorse. In general, the results suggest that falsified remorse may be conceived as an emotionally turbulent display of deliberate, falsified expressions and involuntary, genuine, emotional leakage. These findings are relevant to judges and parole board members who consider genuine remorse to be an important factor in sentencing and release decisions.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".