Video killed the radio star? The influence of presentation modality on detecting high‐stakes, emotional lies
Bibliographic record
Abstract
Purpose In many contexts in which high‐stakes lies occur (such as security settings or the courtroom), observers must evaluate whether the stories they hear are credible. However, little research has evaluated the ability of observers to detect high‐stakes lies, nor the influence of the manner in which the deception is presented on judgment accuracy. This study investigated whether the presentation modality of high‐stakes lies influences both explicit and implicit deception detection accuracy. Methods Participants ( N = 231) were randomly assigned to one of four presentation modalities: audiovisual, video‐only, audio‐only, or transcript‐only and asked to evaluate the honesty of targets – half of whom were sincere and half deceptive killers – making a plea for the return of a missing relative both explicitly (direct lie/truth decision) and implicitly (via emotional reactions). Results Overall, explicit deception detection accuracy was slightly above chance ( M = 52.5%), and honest pleas were accurately identified at a higher rate than deceptive pleas. Although there were no differences in overall accuracy across modality, observers reading transcripts exhibited a truth bias, which resulted in them detecting truthful pleas at a higher rate than with the other groups. Although explicit accuracy was at the level of chance, implicit reactions indicated that observers were able to unconsciously discern liars from truth‐tellers. Conclusions Despite the high‐stakes nature of the lies presented here, they were difficult to detect. Lies presented via written language were missed at a higher rate when assessed using explicit but not implicit judgments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.081 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".