Cry me a river: Identifying the behavioral consequences of extremely high-stakes interpersonal deception.
Bibliographic record
Abstract
Deception evolved as a fundamental aspect of human social interaction. Numerous studies have examined behavioral cues to deception, but most have involved inconsequential lies and unmotivated liars in a laboratory context. We conducted the most comprehensive study to date of the behavioral consequences of extremely high-stakes, real-life deception--relative to comparable real-life sincere displays--via 3 communication channels: speech, body language, and emotional facial expressions. Televised footage of a large international sample of individuals (N = 78) emotionally pleading to the public for the return of a missing relative was meticulously coded frame-by-frame (30 frames/s for a total of 74,731 frames). About half of the pleaders eventually were convicted of killing the missing person on the basis of overwhelming evidence. Failed attempts to simulate sadness and leakage of happiness revealed deceptive pleaders' covert emotions. Liars used fewer words but more tentative words than truth-tellers, likely relating to increased cognitive load and psychological distancing. Further, each of these cues explained unique variance in predicting pleader sincerity.
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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 | 0.006 |
| 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) | 0.001 | 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".