Keeping an eye on pain: investigating visual attention biases in individuals with chronic pain using eye-tracking methodology
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it