Tactile Speed Scaling: Contributions of Time and Space
Bibliographic record
Abstract
A major challenge for the brain is to extract precise information about the attributes of tactile stimuli from signals that co-vary with multiple parameters, e.g., speed and texture in the case of scanning movements. We determined the ability of humans to estimate the tangential speed of surfaces moved under the stationary fingertip and the extent to which the physical characteristics of the surfaces modify speed perception. Scanning speed ranged from 33 to 110 mm/s (duration of motion constant). Subjects could scale tactile scanning speed, but surface structure was essential because the subjects were poor at scaling the speed of a moving smooth surface. For textured surfaces, subjective magnitude estimates increased linearly across the range of speeds tested. The spatial characteristics of the surfaces influenced speed perception, with the roughest surface (8 mm spatial period, SP) being perceived as moving 15% slower than the smoother, textured surfaces (2-3 mm SP). Neither dot disposition (periodic, non periodic) nor dot density contributed to the results, suggesting that the critical factor was dot spacing in the direction of the scan. A single monotonic relation between subjective speed and temporal frequency (speed/SP) was obtained when the ratings were normalized for SP. This provides clear predictions for identifying those cortical neurons that play a critical role in tactile motion perception and the underlying neuronal code. Finally, the results were consistent with observations in the visual system (decreased subjective speed with a decrease in spatial frequency, 1/SP), suggesting that stimulus motion is processed similarly in both sensory systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".