More than meets the eye: visual attention biases in individuals reporting chronic pain
Bibliographic record
Abstract
The present study used eye-tracking technology to assess whether individuals who report chronic pain direct more attention to sensory pain-related words than do pain-free individuals. A total of 113 participants (51 with chronic pain, 62 pain-free) were recruited. Participants completed a dot-probe task, viewing neutral and sensory pain-related words while their reaction time and eye movements were recorded. Eye-tracking data were analyzed by mixed-design analysis of variance with group (chronic pain versus pain-free) as the between-subjects factor, and word type (sensory pain versus neutral) as the within-subjects factor. Results showed a significant main effect for word type: all participants attended to pain-related words more than neutral words on several eye-tracking parameters. The group main effect was significant for number of fixations, which was greater in the chronic pain group. Finally, the group by word type interaction effect was significant for average visit duration, number of fixations, and total late-phase duration, all greater for sensory pain versus neutral words in the chronic pain group. As well, participants with chronic pain fixated significantly more frequently on pain words than did pain-free participants. In contrast, none of the effects for reaction time were significant. The results support the hypothesis that individuals with chronic pain display specific attentional biases toward pain-related stimuli and demonstrate the value of eye-tracking technology in measuring differences in visual attention variables.
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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 | 0.005 |
| 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 | 0.000 |
| 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".