Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection
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
The question of whether discernible differences exist between liars and truth tellers has interested professional lie detectors and laypersons for centuries. In this article we discuss whether people can detect lies when observing someone’s nonverbal behavior or analyzing someone’s speech. An article about detecting lies by observing nonverbal and verbal cues is over-due. Scientific journals regularly publish overviews of research articles regarding nonverbal and verbal cues to deception, but they offer no explicit guidance about what lie detectors should do and should avoid doing to catch liars. We will present such guidance in the present article. The article consists of two parts. The first section focuses on pitfalls to avoid and outlines the major factors that lead to failures in catching liars. Sixteen reasons are clustered into three categories: (a) a lack of motivation to detect lies (because
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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