Using an ideal observer analysis to investigate the visual perceptual efficiency of individuals with a history of non-suicidal self-injury when identifying emotional expressions
Bibliographic record
Abstract
Individuals who engage in non-suicidal self-injury (NSSI) often report significant interpersonal difficulties, with studies lending support to the idea of impaired social interactions. Perceptual processing deficits of facial expressions have also been associated with interpersonal difficulties, yet little research has assessed how individuals with a history of NSSI (HNSSI) process facial emotions. This study used an ideal observer analysis to assess emotion processing capabilities of these individuals. A total of 30 HNSSI and 31 controls were presented with static images of various facial expressions (fear, anger, disgust, happiness, sadness, surprise) at three intensity levels (50%, 75% and 100% emotion expressivity). Recognition of emotions were measured by signal-proportion thresholds, efficiency scores, and unbiased hit rate. Error responses were also recorded to investigate errors biases made by each group. No significant differences between HNSSI and controls were found in signal-proportion thresholds or efficiency scores. Decreased accuracy of HNSSI participants for recognizing fearful expressions was observed. An increased likelihood of mistaking angry for happy expressions and a decreased likelihood of mistaking sad for surprised expressions were recorded for the HNSSI group compared to controls. These findings provide support to the literature reporting deficits in accurate emotion identification for those engaged in NSSI behaviours.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".