An Eye Tracking Investigation of Pain Decoding Based on Older and Younger Adults’ Facial Expressions
Bibliographic record
Abstract
Nonverbal pain cues such as facial expressions, are useful in the systematic assessment of pain in people with dementia who have severe limitations in their ability to communicate. Nonetheless, the extent to which observers rely on specific pain-related facial responses (e.g., eye movements, frowning) when judging pain remains unclear. Observers viewed three types of videos of patients expressing pain (younger patients, older patients without dementia, older patients with dementia) while wearing an eye tracker device that recorded their viewing behaviors. They provided pain ratings for each patient in the videos. These observers assigned higher pain ratings to older adults compared to younger adults and the highest pain ratings to patients with dementia. Pain ratings assigned to younger adults showed greater correspondence to objectively coded facial reactions compared to older adults. The correspondence of observer ratings was not affected by the cognitive status of target patients as there were no differences between the ratings assigned to older adults with and without dementia. Observers' percentage of total dwell time (amount of time that an observer glances or fixates within a defined visual area of interest) across specific facial areas did not predict the correspondence of observers' pain ratings to objective coding of facial responses. Our results demonstrate that patient characteristics such as age and cognitive status impact the pain decoding process by observers when viewing facial expressions of pain in others.
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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 | 0.000 | 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".