Pain Tolerance Predicts Spatial But Not Temporal Vision Thresholds in Human Adults
Bibliographic record
Abstract
Purpose: Previously (VSS 2013), we reported a surprising relationship between two seemingly independent sensory modalities, namely human vision and pain. Specifically, adults' ability to tolerate heat and pressure pain was negatively correlated with performance on tests of spatial contrast sensitivity (CS). However, this effect was found only within a limited sample of adults who were tested repeatedly in order to reduce intra-subject variability. To better assess the robustness of this effect, and to explore the possible neural mechanisms that may underlie sensory interactions, we evaluated the relationship between pain and vision (both spatial and temporal) in a much larger group of young adults. Methods: Measures of spatial contrast sensitivity (FACT, Vector Vision, Rabin) and temporal photopic and mesopic flicker fusion thresholds were assessed binocularly in 105 healthy young adults (M = 23 yr; 62 females, 43 males). Within the same session, threshold and tolerance to both contact heat (arm) and pressure pain (pinky finger) were also assessed. Results: Correlational analyses revealed a strong relationship between all measures of CS and heat pain tolerance (all r > - 0.65), although results for pressure pain were more modest. Specifically, those who showed lower tolerance for heat pain (were more sensitive to pain) also showed higher levels of contrast sensitivity. Conversely, measures of critical flicker fusion thresholds appeared unrelated to any of the pain measures. Conclusions: Human adults show a relationship between heat pain sensitivity and spatial vision, but not between pain and the present measures of temporal vision. Given that dopamine is heavily involved in the processing of both pain and spatial information in the CNS, this raises the interesting possibility that the observed co-variation in sensitivity may be explained by dopaminergic involvement. Meeting abstract presented at VSS 2014
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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) | 0.002 | 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".