Speed-dependent modulation of tactile edge orientation discrimination
Bibliographic record
Abstract
Abstract Previous studies investigating edge-orientation discrimination capacity either stimulated an immobilized finger at limited speeds, or did not manipulate movement speed during active exploration. Here we tested the effect of movement speed on edge-orientation discrimination, including very slow and fast speeds. Participants were instructed to move their finger across two pairs of edges while matching their speed to a visual cue. One edge pair was parallel and the other non parallel to varying degrees. Participants were asked to identify the non parallel pair of edges. We report three main findings. First, consistent with previous reports, when they were free to choose their movement speed, participants moved at an average speed of ∼29 mm/s (range: 15-52 mm/s). Second, there was no correlation between a participant’s natural speed and their edge orientation discrimination capacity. Third, participants got better at edge orientation discrimination at slower than average speeds (5mm/s), and worse at higher than average speeds (90 and 180 mm/s). This change in performance was correlated with their relative change in movement speed. New and Noteworthy Here we demonstrate speed-dependent variation in edge-orientation discrimination during active tactile exploration, with improved performance at very slow speeds. Changes in discrimination capacity as a function of speed are correlated with deviations from a participant’s natural speed.
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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 | 0.000 | 0.002 |
| 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".