Touching and feeling: differences in pleasant touch processing between glabrous and hairy skin in humans
Bibliographic record
Abstract
Previous functional magnetic resonance imaging studies in two rare patients, together with microneurography and psychophysical observations in healthy subjects, have demonstrated a system of mechanosensitive C-fiber tactile (CT) afferents sensitive to slowly moving stimuli. They project to the posterior insular cortex and signal pleasant aspects of touch. Importantly, CTs have not been found in the glabrous skin of the hand, yet it is commonly observed that glabrous skin touch is also perceived as pleasant. Here we asked if the brain processing of pleasant touch differs between hairy and glabrous skin by stroking the forearm and glabrous skin of the hand during positron emission tomography. The data showed that, when contrasting slow brush stroking on the forearm with slow brush stroking on the palm, there were significant activations of the posterior insular cortex and mid-anterior orbitofrontal cortex. The opposite contrast showed a significant activation of the somatosensory cortices. Although concurrent psychophysical ratings showed no differences in intensity or pleasantness ratings, a subsequent touch questionnaire in which subjects used a newly developed 'touch perception task' showed significant difference for the two body sites. Emotional descriptors received higher ratings on the forearm and sensory descriptors were rated more highly on the palm. The present findings are consistent with the hypothesis that pleasant touch from hairy skin, mediated by CT afferents, is processed in the limbic-related cortex and represents an innate non-learned process. In contrast, pleasant touch from glabrous skin, mediated by A-beta afferents, is processed in the somatosensory cortex and represents an analytical process dependent on previous tactile experiences.
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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 | 0.000 |
| 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".