Edge orientation perception during active touch
Bibliographic record
Abstract
Previous studies investigating the perceptual attributes of tactile edge orientation processing have applied their stimuli to an immobilized fingertip. Here we tested the perceptual attributes of edge orientation processing when participants actively touched the stimulus. Our participants moved their finger over two pairs of edges, one pair parallel and the other nonparallel to varying degrees, and were asked to identify which of the two pairs was nonparallel. In addition to the psychophysical estimates of edge orientation acuity, we measured the speed at which participants moved their finger and the forces they exerted when moving their finger over the stimulus. We report four main findings. First, edge orientation acuity during active touch averaged 12.4°, similar to that previously reported during passive touch. Second, on average, participants moved their finger over the stimuli at ~20 mm/s and exerted contact forces of ~0.3 N. Third, there was no clear relationship between how people moved their finger or how they pressed on the stimulus and their edge orientation acuity. Fourth, consistent with previous work testing tactile spatial acuity, we found a significant correlation between fingertip size and orientation acuity such that people with smaller fingertips tended to have better orientation acuity. NEW & NOTEWORTHY Edge orientation acuity expressed by the motor system during manipulation is many times better than edge orientation acuity assessed in psychophysical studies where stimuli are applied to a passive fingertip. Here we show that this advantage is not because of movement per se because edge orientation acuity assessed in a psychophysical task, where participants actively move their finger over the stimuli, yields results similar to previous passive psychophysical studies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".