Attentional bias and its temporal dynamics among war veterans suffering from chronic pain: investigating the contribution of post-traumatic stress symptoms
Bibliographic record
Abstract
Background: Cognitive models propose that attentional dysregulation, including an attentional bias towards threat, is one potential mechanism underlying the association between chronic pain and post-traumatic stress symptoms (PTSS). We assessed the attentional bias for painful facial expressions and its relationship with PTSS, using both traditional and variability-based attentional bias measures, among veterans with chronic pain and PTSS and controls.Method: Fifty-four veterans with chronic pain and 30 age/education-matched controls participated in this investigation. Participants completed a self-report measure of PTSS and a dot-probe task with painful/happy/neutral facial expressions. Attention was assessed using both traditional and variability-based reaction time measures of attentional bias.Results: Veterans directed attention away from painful facial expressions (i.e., avoidance) relative to both the control group (between-subject effect) and relative to zero. Veterans showed significantly elevated attentional bias variability for happy and painful expressions compared to controls. Veteran’s attentional bias variability for happy expressions was correlated with PTSS and symptoms of depression.Conclusion: Veterans with chronic pain and PTSS avoided pain-related stimuli and displayed an overall attentional dysfunction for emotional facial expressions. Avoidance of pain cues may be a coping strategy that these individuals develop under stressful conditions. Implications, limitations, and directions for future are discussed.
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 | 0.002 |
| 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".