Biased emotional attention in post-traumatic stress disorder: a help as well as a hindrance?
Bibliographic record
Abstract
BACKGROUND: From a cognitive neuroscience perspective, the emotional attentional bias in post-traumatic stress disorder (PTSD) could be conceptualized either as emotional hyper-responsiveness or as reduced priming of task-relevant representations due to dysfunction in 'top-down' regulatory systems. We investigated these possibilities both with respect to threatening and positive stimuli among traumatized individuals with and without PTSD. METHOD: Twenty-two patients with PTSD, 21 trauma controls and 20 non-traumatized healthy participants were evaluated on two tasks. For one of these tasks, the affective Stroop task (aST), the emotional stimuli act as distracters and interfere with task performance. For the other, the emotional lexical decision task (eLDT), emotional information facilitates task performance. RESULTS: Compared to trauma controls and healthy participants, patients with PTSD showed increased interference for negative but not positive distracters on the aST and increased emotional facilitation for negative words on the eLDT. CONCLUSIONS: These findings document that hyper-responsiveness to threat but not to positive stimuli is specific for patients with PTSD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".