Exploring the impact of gentle stroking touch on psychophysiological regulation of inhibitory control
Bibliographic record
Abstract
Touch has been shown to regulate emotions, stress responses, and physical pain. However, its impact on cognitive functions, such as inhibitory control, remains relatively understudied. In this experiment, we explored the effects of low-force, slow-moving touch-designed to optimally activate unmyelinated cutaneous low-threshold mechanoreceptor C-tactile (CT) afferents in human hairy skin-on inhibitory control and its psychophysiological correlates using the Stroop Task, a classic paradigm commonly employed to assess inhibitory control capacity. The Stroop Task was repeated twice before and once after receiving either gentle touch or no-touch. Participants were assigned to two groups: the touch group (n = 36), which received low-force, slow-moving touch on their forearms at a stroking velocity of ~3 cm/s, and the no-touch group (n = 36), which did not receive any touch stimulation. Changes in autonomic nervous system activity were also assessed by measuring heart rate variability (HRV) and skin conductance levels before and during cognitive performance. Compared to the no-touch group, participants who received gentle, low-force, slow-moving touch demonstrated faster responses and higher HRV during the Stroop Task. Additionally, within the touch group, individuals with higher HRV exhibited even quicker performance on the cognitive task. While we cannot draw definitive conclusions regarding the CT velocity-specific effect, these results provide preliminary evidence that low-force, slow-moving touch may influence cognitive processes involved in the inhibitory control of goal-irrelevant stimuli.
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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".