Effects of personality, interrogation techniques and plausibility in an experimental false confession paradigm
Bibliographic record
Abstract
Purpose. The goal of the present study was to investigate the effects of personality variables, interrogation techniques and the plausibility level of an alleged transgression on the experimental elicitation of false confessions. Methods. Two hundred and nineteen undergraduate students assessed on measures of compliance, self‐esteem, locus of control and interrogative suggestibility participated in the paradigm. Experimental manipulations included minimization and maximization interrogation techniques and high and low plausibility of the alleged typing mistake to examine rates of false confession and internalization. Results. The overall false confession and internalization rates across all conditions were 43 and 10%, respectively. An increased likelihood of false confession behaviour was associated with higher Shift scores on the Gudjonsson Suggestibility Scale, the use of minimization interrogation techniques and an increase in the plausibility of the allegation. Females were more likely to falsely confess than males in the high plausibility condition, whereas Caucasian and Asian participants were equally likely to falsely confess. Personality variables, such as compliance, most influenced the behaviour of males and Asians. Conclusions. The results of this study offer insight into false confession behaviour, suggesting that individuals who have a tendency to change their responses in the face of negative feedback may be more prone to false confession behaviour. The findings also serve to highlight the dangers of using minimization interrogation techniques and elucidate the limited generalizability of the paradigm to situations in which the alleged transgression is less plausible.
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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 | 0.003 | 0.018 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".