Bibliographic record
Abstract
The widespread supposition that aspects of facial communication are uncontrollable and can betray a deceiver's true emotion has received little empirical attention. We examined the presence of inconsistent emotional expressions and "microexpressions" (1/25-1/5 of a second) in genuine and deceptive facial expressions. Participants viewed disgusting, sad, frightening, happy, and neutral images, responding to each with a genuine or deceptive (simulated, neutralized, or masked) expression. Each 1/30-s frame (104,550 frames in 697 expressions) was analyzed for the presence and duration of universal expressions, microexpressions, and blink rate. Relative to genuine emotions, masked emotions were associated with more inconsistent expressions and an elevated blink rate; neutralized emotions showed a decreased blink rate. Negative emotions were more difficult to falsify than happiness. Although untrained observers performed only slightly above chance at detecting deception, inconsistent emotional leakage occurred in 100% of participants at least once and lasted longer than the current definition of a microexpression suggests. Microexpressions were exhibited by 21.95% of participants in 2% of all expressions, and in the upper or lower face only.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".