Physical determinants of the shape of the psychophysical curve relating tactile roughness to raised-dot spacing: implications for neuronal coding of roughness
Bibliographic record
Abstract
There are conflicting reports as to whether the shape of the psychometric relation between perceived roughness and tactile element spacing [spatial period (SP)] follows an inverted U-shape or a monotonic linear increase. This is a critical issue because the former result has been used to assess neuronal codes for roughness. We tested the hypothesis that the relation's shape is critically dependent on tactile element height (raised dots). Subjects rated the roughness of low (0.36 mm)- and high (1.8 mm)-raised-dot surfaces displaced under their fingertip. Inverted U-shaped curves were obtained as the SP of low-dot surfaces was increased (1.3-6.2 mm, tetragonal arrays); a monotonic increase was observed for high-dot surfaces. We hypothesized that roughness is not a single sensory continuum across the tested SPs of low-dot surfaces, predicting that roughness discrimination would show deviations from the invariant relation between threshold (ΔS) and the value of the standard (S) surface (Weber fraction, ΔS/S) expected for a single continuum. The results showed that Weber fractions were increased for SPs on the descending limb of the inverted U-shaped curve. There was also an increase in the Weber fraction for high-dot surfaces but only at the peak (3 mm), corresponding to the SP at which the slope of the psychometric function showed a modest decline. Together the results indicate that tactile roughness is not a continuum across low-dot SPs of 1.3-6.2 mm. These findings suggest that correlating the inverted U-shaped function with neuronal codes is of questionable validity. A simple intensive code may well contribute to tactile roughness.
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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".