Response Inhibition in borderline personality disorder assessed with a gamified stop signal task
Bibliographic record
Abstract
Borderline personality disorder (BPD) is characterized by pervasive difficulties with self-image, interpersonal relationships, emotion regulation and impulse control. Impulsive behaviors can be assessed using tasks such as the stop-signal task (SST). Traditional SSTs are repetitive and cognitively demanding, requiring sustained attention and effort over extended periods. This can be challenging, particularly for clinical populations who often experience difficulties with attention, emotional regulation, and frustration tolerance. In this study, we examined whether a gamified version of the SST (gSST) could effectively differentiate inhibitory control in patients with BPD compared to healthy controls (HC), and explored associations between behavioral performance and self-reported impulsivity. Fifty participants (25 BPD, 25 HC) completed the gSST and the UPPS-P impulsivity questionnaire. Patients with BPD showed significantly faster reaction times, more choice errors, and shorter stop-signal delays, indicating impairments in proactive inhibition. Evidence for reactive inhibition deficits was, however, inconclusive, as no significant group difference emerged for the primary measure, the stop-signal reaction time (SSRT). The SSRT correlated positively with self-reported positive urgency across the sample, suggesting a link between emotionally driven impulsivity and inhibitory control as assessed with a gSST. These findings highlight the potential of gamified cognitive tasks to provide sensitive, engaging, and ecologically valid measures of impulsivity, with implications for both clinical assessment and personalized intervention strategies in BPD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".