Categorization of Emotional Facial Expressions in Humans with a History of Non-suicidal Self-injury
Bibliographic record
Abstract
In social animals, such as humans, accurate emotion expression categorization is important for appropriate social functioning. Inaccuracy in emotion categorization can lead to inadequate social behavior, commonly seen in various psychiatric disorders. Non-suicidal self-injury (NSSI) is a psychiatric symptom involving deliberate self-inflicted injury of one’s body, without intent to die. NSSI has been regarded as a dysfunctional coping strategy for managing intensely difficult feelings. Difficulties in social interactions have been reported by individuals who engage in NSSI, which may be related to their emotion categorization performance. Participants (17-25 yrs) with a history of NSSI and healthy controls viewed videos of faces changing over 10 s from neutral to a prototypical expression of sadness, disgust, surprise, fear, anger or happiness. They were instructed to stop each video as soon as they felt they recognized the emotion presented, thus indicating the minimum intensity of expression needed for categorization. They were then asked to categorize the expression. Minimum facial expression intensity, accuracy of categorization, and reaction time were the behavioral dependent variables of interest. NSSI participants showed significant advantages compared to controls in their ability to categorize negative emotion expressions, specifically fear, anger, disgust, and sadness. They also were able to recognize the ambiguous emotion of surprise at a lower stimulus intensity. To date, treatments for NSSI have high drop-out rates. Results from this research could be used to inform further development of therapies for the alleviation or prevention of NSSI.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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, 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".