Attentional processing of other’s facial display of pain: An eye tracking study
Bibliographic record
Abstract
The present study investigated the role of observer pain catastrophizing and personal pain experience as possible moderators of attention to varying levels of facial pain expression in others. Eye movements were recorded as a direct and continuous index of attention allocation in a sample of 35 undergraduate students while viewing slides presenting picture pairs consisting of a neutral face combined with either a low, moderate, or high expressive pain face. Initial orienting of attention was measured as latency and duration of first fixation to 1 of 2 target images (i.e., neutral face vs pain face). Attentional maintenance was measured by gaze duration. With respect to initial orienting to pain, findings indicated that participants reporting low catastrophizing directed their attention more quickly to pain faces than to neutral faces, with fixation becoming increasingly faster with increasing levels of facial pain expression. In comparison, participants reporting high levels of catastrophizing showed decreased tendency to initially orient to pain faces, fixating equally quickly on neutral and pain faces. Duration of the first fixation revealed no significant effects. With respect to attentional maintenance, participants reporting high catastrophizing and pain intensity demonstrated significantly longer gaze duration for all face types (neutral and pain expression), relative to low catastrophizing counterparts. Finally, independent of catastrophizing, higher reported pain intensity contributed to decreased attentional maintenance to pain faces vs neutral faces. Theoretical implications and further research directions are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".