Time course of facial emotion processing in women with borderline personality disorder: an ERP study
Bibliographic record
Abstract
BACKGROUND: Borderline personality disorder (BPD) is characterized by a negative perception of others. Previous studies have revealed deficits and biases in facial emotion recognition. This study investigates the behavioural and electrophysiological correlates underlying facial emotion processing in individuals with BPD. METHODS: The present study was conducted between July 2012 and May 2014. In an emotion classification task, unmedicated female patients with BPD as well as healthy women had to classify faces displaying blends of anger and happiness while the electroencephalogram was recorded. We analyzed visual event-related potentials (ERPs) reflecting early (P100), structural (N170) and categorical (P300) facial processing in addition to behavioural responses. RESULTS: We included 36 women with BPD and 29 controls in our analysis. Patients with BPD were more likely than controls to classify predominantly happy faces as angry. Independent of facial emotion, women with BPD showed enhanced early occipital P100 amplitudes. Additionally, temporo-occipital N170 amplitudes were reduced at right hemispherical electrode sites. Centroparietal P300 amplitudes were reduced particularly for predominantly happy faces and increased for highly angry faces in women with BPD, whereas in healthy volunteers this component was modulated by both angry and happy facial affect. LIMITATIONS: Our sample included only women, and no clinical control group was investigated. CONCLUSION: Our findings suggest reduced thresholds for facial anger and deficits in the discrimination of facial happiness in individuals with BPD. This biased perception is associated with alterations in very early visual as well as deficient structural and categorical processing of faces. The current data could help to explain the negative perception of others that may be related to the patients' impairments in interpersonal functioning.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".