Automatic Classification of Children's Antisocial and Prosocial Lies Using Facial Expressions
Bibliographic record
Abstract
Research on the nonverbal facial expressions of children during lie-telling is extremely limited. As such, it is unknown whether the nonverbal behaviours of children telling an antisocial lie are the same or different when they tell a prosocial lie. The current study is the first to concurrently examine the facial movements of children during antisocial and prosocial lying. Through the use of the Computer Recognition Toolbox (Littlewort et al., 2011), an automated computer vision program using the Facial Action Coding System (Ekman Friesen, 1978), children's nonverbal behaviours were shown to be significantly different in terms of 8 different facial actions. Furthermore, linear support vector machine (SVM) analysis was successful in classifying children's lies with an average accuracy of 72.68%, significantly above chance levels. Implications and limitations are discussed.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".