Single-shot detection of microscale tactile features
Bibliographic record
Abstract
Abstract Tactile detection of very small features requires relative motion between the fingertip and a surface. The specific movement strategies that people use may be critical to maximize detection ability but little is known about the movement strategies people employ to support such detection. Here, human participants actively scanned a fingertip across a pair of silica wafers to detect which of the two contained a microscale feature (2, 6, and 10 μm height and 525 μm diameter). We constrained fingertip movement to ensure that participants would always contact the feature and would only contact the feature once. These procedures encouraged participants to use strategies that optimized detection rather than search and thus allowed us to more directly link movement strategies to detection. We also investigated the effects of fingertip movement direction and the finger used on detection. We found that participants were able to consistently detect microscale features as small as 2 μm on the basis of a single contact event. The contact forces that participants used were substantially higher than those observed in previous studies focused on tactile search or geometric feature extraction. Scanning speeds were slower than those found during tactile search but faster than those reported during geometric feature extraction. Taken in conjunction with the associations between detection and finger used as well as scan direction, our results suggest that control and consistency of fingertip movement may be a primary consideration for movement strategies that optimize tactile detection.
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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 | 0.001 |
| 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) | 0.002 | 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".