Perceptual biases in facial emotion recognition in borderline personality disorder.
Bibliographic record
Abstract
Individuals with borderline personality disorder (BPD) have biases in facial emotion recognition, which may underlie many of the core features of this disorder. Although they are known to misperceive specific prototypic expressions of emotion (i.e., those displayed at full emotional intensity), patients with this disorder may also show biases in their perceptions of emotions that are expressed at lower levels of emotional intensity. Females with BPD (n = 31) and IQ- and demographically matched nonpsychiatric controls (n = 28) completed a task assessing the recognition of neutral as well as happy and sad facial expressions at mild, moderate, and prototypic emotional intensities. Whereas patients with BPD were more likely than controls to ascribe an emotion to a neutral facial expression, they did not consistently attribute a more negative or positive valence to these faces as compared with controls. Patients were also more likely to perceive mildly sad facial expressions as more intensely sad, and this finding could not be attributed to depressed mood. The results of this study suggest that perceptions of even subtle expressions of negative affect in faces may be subjectively magnified by individuals with BPD, although there was no consistent evidence for a negative perceptual bias for faces displaying a neutral expression. These biases in facial emotion perception for patients with BPD may contribute to difficulties understanding others' emotional states and to problems engaging effectively in social interactions.
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 | 0.004 |
| 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) | 0.001 | 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 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".