Keeping an eye on pain: investigating visual attention biases in individuals with chronic pain using eye-tracking methodology
Bibliographic record
Abstract
Attentional biases to painful stimuli are evident in individuals with chronic pain, although the directional tendency of these biases (ie, toward or away from threat-related stimuli) remains unclear. This study used eye-tracking technology, a measure of visual attention, to evaluate the attentional patterns of individuals with and without chronic pain during exposure to injury-related and neutral pictures. Individuals with (N=51) and without chronic pain (N=62) completed a dot-probe task using injury-related and neutral pictures while their eye movements were recorded. Mixed-design analysis of variance evaluated the interaction between group (chronic pain, pain-free) and picture type (injury-related, neutral). Reaction time results showed that regardless of chronic pain status, participants responded faster to trials with neutral stimuli in comparison to trials that included injury-related pictures. Eye-tracking measures showed within-group differences whereby injury-related pictures received more frequent fixations and visits, as well as longer average visit durations. Between-group differences showed that individuals with chronic pain had fewer fixations and shorter average visit durations for all stimuli. An examination of how biases change over the time-course of stimulus presentation showed that during the late phase of attention, individuals with chronic pain had longer average gaze durations on injury pictures relative to pain-free individuals. The results show the advantage of incorporating eye-tracking methodology when examining attentional biases, and suggest future avenues of research.
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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 | 0.000 |
| 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".