The truth about lies: What works in detecting high‐stakes deception?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we provide our view of the current understanding of high‐stakes lies often occurring in forensic contexts. We underscore the importance of avoiding widespread pitfalls of deception detection and challenging prevailing assumptions concerning strategies for catching liars. The promise and limitations of each of non‐verbal/body language, facial, verbal/linguistic, and physiological channels in detecting deception are discussed. In observing the absence of a single cue or behavioural channel that consistently reveals deception, a holistic approach with concurrent attention to multiple channels of a target's behaviour (ideally videotaped for review) and changes from baseline behaviour is recommended whenever possible. Among the best‐validated cues to be considered together include: illustrators, blink and pause rate, speech rate, vague descriptions, repeated details, contextual embedding, reproduction of conversations, and emotional ‘leakage’ in the face. While advocating a reliance on empirical evidence, we observe that few studies of high‐stakes deception yet have been conducted. Further, some manifestations of lying are highly idiosyncratic and difficult to address in quantitative research, pointing to the need for keen observation skills, and psychological insight. A recurring theme is the need for the field to devise innovative approaches for studying high‐stakes lies to promote ecological validity. Ultimately, such work will provide a strong foundation for the responsible application of deception research in forensic and security settings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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 it