The Curious Case of Courtroom Liars’ Credibility
Bibliographic record
Abstract
How do we evaluate people who provide false information? The current studies uncover a context in which people who intentionally lie are perceived as more credible than those who unintentionally mix up information. Across three studies (total N=1196), participants read about an incident witnessed by targets who, when queried, either lied about or mixed up information. Participants then evaluated those targets. In Study 1, we demonstrate that in a courtroom, targets who lie (versus mix up information) are judged as more credible. We next test two boundary conditions, showing that the effect may be constrained by particular contextual characteristics of a courtroom (Study 2) and that the misinformation needs to be unrelated to the information on which the target’s advice or testimony is sought (Study 3). The current research suggests that under specific circumstances, perceivers may evaluate targets who lie as more credible than those who mix up information.
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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 | 0.026 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".