Sub-optimal presentation of painful facial expressions enhances readiness for action and pain perception following electrocutaneous stimulation
Bibliographic record
Abstract
Observation of others' painful facial expressions has been shown to facilitate behavioral response tendencies and to increase pain perception in the observer. However, in previous studies, expressions were clearly visible to the observer and none of those studies investigated the effect of presence of peripheral stimulation on response tendencies. This study focuses on the effect of sub-optimal presentation of painful facial expressions in the presence and absence of an electrocutaneous stimulus. Twenty-two healthy individuals categorized arrow targets which were preceded by a sub-optimally presented facial expression (painful, happy, or neutral in different blocks). On half of the trials, aversive electrocutaneous stimulation was delivered to the wrist of the non-dominant hand between the presentation of facial expression and target (an arrow directing to right or left). Participants' task was to indicate direction of the arrow as soon as it appears on the screen by pressing the corresponding key on the keyboard and to rate their pain at the end of block. Analysis showed that responses were faster to targets preceded by aversive stimulation than to targets not preceded by stimulation, especially following painful expressions. Painfulness ratings were higher following painful expressions than following happy expressions. These findings suggest that sub-optimally presented painful expressions can enhance readiness to act to neutral, non-pain-related targets after aversive stimulation and can increase pain perception.
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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 | 0.000 | 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) | 0.002 | 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".