Selective attention towards painful faces among chronic pain patients: Evidence from a modified version of the dot-probe
Bibliographic record
Abstract
Evidence that patients with chronic pain selectively attend to pain-related stimuli presented in modified Stroop and dot-probe paradigms is mixed. The pain-related stimuli used in these studies have been primarily verbal in nature (i.e., words depicting themes of pain). The purpose of the present study was to determine whether patients with chronic pain, relative to healthy controls, show selective attention for pictures depicting painful faces. To do so, 170 patients with chronic pain and 40 age- and education-matched healthy control participants were tested using a dot-probe task in which painful, happy, and neutral facial expressions were presented. Selective attention was denoted using the mean reaction time and the bias index. Results indicated that, while both groups shifted attention away from happy faces (and towards neutral faces), only the control group shifted attention away from painful faces. Additional analyses were conducted on chronic pain participants after dividing them into groups on the basis of fear of pain/(re)injury. The results of these analyses revealed that while chronic pain patients with high and low levels of fear both shifted attention away from happy faces, those with low fear shifted attention away from painful faces, whereas those with high fear shifted attention towards painful faces. These results suggest that patients with chronic pain selectively attend to facial expressions of pain and, importantly, that the tendency to shift attention towards such stimuli is positively influenced by high fear of pain/(re)injury. Implications of the findings and future research directions are discussed.
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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 | 0.000 | 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) | 0.002 | 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".