Behavioral detection of emotional, high-stakes deception: Replication in a registered report.
Bibliographic record
Abstract
OBJECTIVE: We replicated research by ten Brinke and Porter (2012), who reported that a combination of four behavioral cues (word count, tentative words, upper face surprise, lower face happiness) could accurately discriminate deceptive murderers from genuinely distressed individuals, pleading for the return of a missing relative. HYPOTHESES: We hypothesized that each of the four behavioral cues identified in the original study would be similarly related (i.e., size, direction, significance) to veracity in a novel set of pleaders. With these cues as predictors, we also hypothesized that logistic regression models-separately testing the original and replication samples-would produce similar accuracy rates exceeding chance in discriminating genuine from deceptive pleaders. METHOD: We gathered a new sample of public appeals, including 82 genuine and 14 deceptive pleaders. After establishing ground truth, we transcribed video-recorded pleas and coded them for the presence of upper face surprise and lower face happiness. We used Linguistic Inquiry and Word Count to determine word count and the proportion of tentative words in each appeal. RESULTS: We found support for several hypotheses. Tentative words were used significantly more by deceptive (vs. genuine) pleaders in both the original and replication samples. Deceptive pleaders used significantly fewer words in both samples, although this relationship was significant only in the original sample. Liars in both samples smiled more than truth-tellers, although this relationship was statistically significant only in the replication sample. However, predictive accuracy was poor and did not differ from chance in the replication sample. CONCLUSIONS: Findings do not provide a tidy picture of the reliability of behavioral cues to deception. Although some behavioral cues did replicate across samples, others did not. More research will be necessary to understand the factors that produce variable findings across samples, despite using the same methods of investigation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".