Visual Field Magnification and Touch Perception When Exploring Surfaces With the Index Finger and a Rigid Instrument
Bibliographic record
Abstract
OBJECTIVE: The aim of this project was to compare texture discrimination when both touch and vision were perturbed. BACKGROUND: Texture discrimination is important in the workplace. How textures are identified with the finger and with instruments when vision is magnified with lenses or video cameras is unclear. METHOD: Sandpaper was explored with the index finger or a metal instrument (hemostat), using normal or magnified vision. The forces generated during exploration were measured, and participants rated surface roughness. RESULTS: With the finger, the perception of roughness was unaffected with magnification; with the instrument, magnified surfaces were perceived as rougher (p < .05). Forces during finger exploration were unaffected by magnification; forces with the instrument increased under magnification (p < .05). CONCLUSION: Visual characteristics of the working field can influence the exploration and perception of materials. With the finger, mechanoreceptors that directly detect textures are activated, and with the instrument, receptors sensitive to vibrations are stimulated. APPLICATION: The higher forces produced when using instruments under magnification could lead to material damage. Attenuated perception of texture when exploring with tools may lead to difficulty in accurate touch perception. This could create problems in industrial tasks such as grading wool or identifying surface imperfections on manufactured materials, as well as in clinical settings such as dentistry or surgery in which instruments are used during tissue identification.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".