Subjective salience ratings are a reliable proxy for physiological measures of arousal
Bibliographic record
Abstract
Pain is an inherently salient multidimensional experience that signals potential bodily threats and promotes nocifensive behaviours. Any stimulus can be salient depending on its features and context. This poses a challenge in delineating pain-specific processes in the brain, rather than salience-driven activity. It is thus essential to salience match control (innocuous) stimuli and noxious stimuli, to remove salience effects, when aiming to delineate pain-specific mechanisms. Previous studies have salience-matched either through subjective salience ratings or the skin conductance response (SCR). The construct of salience is not intuitive, and thus matching through self-report poses challenges. SCR is used as a proxy measure that captures physiological arousal, which overcomes the nebulous construct of salience. However, SCR cannot be used to salience-match in real-time (i.e., during an experiment) and assumes an association between salience and physiological arousal elicited by painful and non-painful stimuli, but this has not been explicitly tested. To determine whether salience and physiological arousal are associated, thirty-five healthy adults experienced 30 heat pain and 30 non-painful electric stimuli of varying intensities. Stimuli were subjectively matched for salience and SCR was measured to each presentation. A linear mixed model found no differences in SCR between salience-matched heat and electric stimuli. A mediation analysis showed that salience fully mediated the relationship between stimulus intensity and SCR (proportion mediated=83%). In conclusion, salience and physiological arousal are associated, and subjective salience ratings are a suitable for salience-matching pain with non-painful stimuli. Future work can thus use subjective salience ratings to delineate pain-specific processes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".