Detecting Differences Between Concealed and Unconcealed Emotions Using iMotions EMOTIENT
Bibliographic record
Abstract
Biometric analysis is everywhere – even in our cell phone security through facial and fingerprint recognition. It has recently become widely useful in forensic settings as well, being used for facial, fingerprint/palmprint, iris, and voice identification1. Using the iMotions Facial Expression Analysis software, I looked at detection differences between concealed and unconcealed emotions when presented with various stimuli, specifically looking at the time in percentage that each emotion was elicited throughout the stimuli. Fourteen participants, eight females (F) and six males (M), were shown seven different videos aimed at eliciting specific emotions to be measured by the iMotions software. Prior to exposure to the stimuli, seven of these fourteen participants (4F, 3M) were asked to conceal their emotions while watching the following videos. The seven different emotions that were measured by the software include contempt, disgust, fear, joy, anger, surprise, and sadness. The alternative hypothesis states that individuals who concealed their emotions during presented stimuli will have significantly less detectable emotions elicited in comparison to individuals who were not asked to conceal their emotions. The null hypothesis states that there will be no significant difference between detectable emotions of individuals of the concealed group and the unconcealed group. There was no statistically significant difference of emotion detected between the overall concealed and unconcealed participant averages with regards to time (%) (p=0.07, a ≥0.05).
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.
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 | 0.003 |
| 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) | 0.004 | 0.001 |
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".