Tactile Perception of Nonpainful Unpleasantness in Relation to Perceived Roughness: Effects of Inter-Element Spacing and Speed of Relative Motion of Rigid 2-D Raised-Dot Patterns at Two Body Loci
Bibliographic record
Abstract
Rigid surfaces consisting of spatially jittered 2-D raised-dot patterns with different inter-element spacings were moved back and forth across the skin at three different speeds (10-fold range). Within each psychophysical experiment, participants numerically estimated the perceived magnitude of either unpleasantness (nonpainful) or roughness of 2-D raised-dot surfaces applied to two stationary body sites (experiment 1: fingers; experiment 2: forearm). The psychophysical functions for the two types of perceptual judgment were highly similar at both body loci; more specifically, the perceived magnitude of unpleasantness and roughness both increased monotonically as a power function of increasing inter-element spacing, with the rate of growth declining at the upper end of the continuum. These results suggest that inter-element spacing is a critical determinant of the perceived magnitude of unpleasantness (nonpainful), as well as of roughness. Each perceptual judgment also increased as a function of increasing relative speed at both body loci. However, the magnitude of this effect was significantly greater for perceived unpleasantness than for perceived roughness; conversely, the speed effect was significantly greater on the forearm than on the fingers. Several possible explanations for these findings are considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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".