Reliability of Tactile Perception and Suppression Measurements
Bibliographic record
Abstract
Tactile signals arising on one's own body allow estimation of one's sensory state and foster interactions with the environment. However, tactile perception can be influenced by various factors. For instance, tactile perception is suppressed on a moving limb compared to when it is resting, a phenomenon termed tactile suppression. Here, we examine whether tactile perception during resting and during movement is robust over shorter and longer time intervals. Participants had to detect tactile stimuli of various intensities on their index finger while this finger was resting or moving (finger extension). This detection task was performed in four sessions on separate days across a period of one month. We found that tactile perception during resting is robust within single sessions and across days. However, tactile perception during movement changed across days, but these changes lacked a clear systematic pattern. We further show that temporal changes in perception alone cannot fully account for the previously reported tactile suppression effects. Finally, split-half correlations reveal high consistency in the estimated perceptual measures, demonstrating that estimates of tactile perception are robust across measurement points.
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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 | 0.008 | 0.048 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".