Human Pain Sensitivity Is Related To Visual But Not To Auditory Thresholds
Bibliographic record
Abstract
Purpose: Last year (VSS 2014), we reported that adults’ ability to tolerate pain predicted performance on tests of spatial contrast sensitivity (CS). More specifically, those who were more sensitive to pain also showed higher sensitivity to contrast. It was suggested that the observed co-variation may be due to dopaminergic involvement. Here, we further explore these multisensory interactions by examining the relationship between spatial vision, pain and hearing, the latter a sense that appears to be influenced even more so by dopamine (Gitelman et al, 2013). Methods: Two measures of spatial contrast sensitivity (FACT, Vector Vision ) and a full audiometric assessment (100 to 8000Hz) was conducted in 144 healthy young adults (M = 23 yr; 84 females, 60 males). Within the same session, pressure algometry was used to assess pain threshold and tolerance on the pinky finger. Results: Correlational analyses revealed a strong relationship between all measures of CS and pressure pain (all r > - 0.52). Again, lower pain sensitivity was related with higher spatial CS. In addition, individual contrast sensitivity functions (CSF) correlated positively (r = 0.46) with audibility functions (the auditory counterpart of the CSF). However, auditory performance was unrelated with pressure pain threshold or tolerance Conclusions: Human adults show a relationship between spatial vision and pain sensitivity, between vision and hearing thresholds, but not between pain and auditory functioning. These results suggest the prominence of vision within basic sensory interactions. However dopaminergic involvement as a primary mediator of multisensory functioning is questioned somewhat by these data. Meeting abstract presented at VSS 2015
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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 | 0.002 |
| 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) | 0.003 | 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".