Neuronal correlates of tactile speed in primary somatosensory cortex
Bibliographic record
Abstract
Moving stimuli activate all of the mechanoreceptive afferents involved in discriminative touch, but their signals covary with several parameters, including texture. Despite this, the brain extracts precise information about tactile speed, and humans can scale the tangential speed of moving surfaces as long as they have some surface texture. Speed estimates, however, vary with texture: lower estimates for rougher surfaces (increased spatial period, SP). We hypothesized that the discharge of cortical neurons playing a role in scaling tactile speed should covary with speed and SP in the same manner. Single-cell recordings (n = 119) were made in the hand region of primary somatosensory cortex (S1) of awake monkeys while raised-dot surfaces (longitudinal SPs, 2-8 mm; periodic or nonperiodic) were displaced under their fingertips at speeds of 40-105 mm/s. Speed sensitivity was widely distributed (area 3b, 13/25; area 1, 32/51; area 2, 31/43) and almost invariably combined with texture sensitivity (82% of cells). A subset of cells (27/64 fully tested speed-sensitive cells) showed a graded increase in discharge with increasing speed for testing with both sets of surfaces (periodic, nonperiodic), consistent with a role in tactile speed scaling. These cells were almost entirely confined to caudal S1 (areas 1 and 2). None of the speed-sensitive cells, however, showed a pattern of decreased discharge with increased SP, as found for subjective speed estimates in humans. Thus further processing of tactile motion signals, presumably in higher-order areas, is required to explain human tactile speed scaling.
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 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 | 0.000 |
| 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) | 0.000 | 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".