The Forensic biometric analysis of changes in facial response provoked by emotional arousal during initial and subsequent exposure to stimuli
Bibliographic record
Abstract
The purpose of this research is to determine if recall of the same stimuli produces similar emotional reactions as the initial response. Humans experience involuntary and voluntary responses when encountered with a stimulus. Facial expressions are the natural way to deduce and interpret the emotional state of that person. Specialized software called iMotions biometric software can virtually mold itself to the structure of one’s face to interpret and infer the personalized emotional states. There are seven main emotions detected by the iMotions emotient FACET facial expression recognition and analysis software; joy, sadness, anger, surprise, contempt, disgust, and fear. 11 respondents, 6 females and 5 males, 20-23 years of age participated in this study. Seven videos were shown to each respondent corresponding to each emotion analyzed. Two sessions took place for this procedure; in the first the respondents viewed the stimuli for the initial exposure, following the general procedure, in the second session, respondents viewed the same stimuli again, as the recalled exposure. It was expected that a decrease in every emotion for every respondent on the recalled exposure compared to the initial would occur. The emotion time percent values generated by the iMotions software confirmed that previously being exposed to a stimulus reduces the level of emotion the second time in comparison to the first and that recall of the same stimuli produces a lesser emotional reaction in comparison to the initial response. It can be concluded that each respondent exhibited similar levels of reaction from the initial exposure to the recalled exposure.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".