Is The Bluff Enough? Examining the Effect of Different Variants of False Evidence on False Confessions
Bibliographic record
Abstract
At present, the majority of false confessions are the result of psychologically manipulative interrogation tactics. Interrogators may use the false evidence ploy or the bluff ploy to elicit confessions. Unfortunately, research suggests that these interrogation tactics increase the risk of false confessions. At this time, research on the differential impact of the false evidence ploy and the bluff ploy is inconclusive, and there is little known about whether certain variants of false evidence are differentially powerful in eliciting false confessions. The present study examined the following: 1) the differential effect of the false evidence ploy and the bluff ploy on false confessions, and 2) the differential effect of three variants of false evidence on false confessions. The present study used a 2 (ploy: false evidence vs. bluff) by 3 (evidence variant: photograph vs. physical vs. eyewitness) between-subjects design. Participants (N=218) completed a logical reasoning task on a computer and were accused of violating the experimental protocol by pressing the space bar and seeing the answer. Participants were either shown faked (false) evidence, or told this evidence could be examined at a later date (bluff), and were then prompted to sign a confession statement. Results demonstrated that participants in the photograph evidence condition were more likely to falsely confess and to internalize guilt than participants in the physical evidence condition and eyewitness testimony condition. Results also demonstrated that participants in the false evidence condition were more likely to falsely confess and internalize guilt than participants in the bluff condition. The policy implications of these 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 | 0.010 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".