The perceived roughness of resistive virtual textures
Bibliographic record
Abstract
In previous work, we demonstrated that people reliably perceive variations in surface roughness when textured surfaces are explored with a rigid link between the surface and the skin [e.g., Klatzky and Lederman 1999; Klatzky et al. 2003]. Parallel experiments here investigated the potential of a force-feedback mouse to render surfaces varying in roughness. The stimuli were surfaces with alternating regions of high and low resistance to movement in the x (frontal) dimension (called ridges and grooves, respectively). Experiment 1 showed that magnitude ratings of roughness varied systematically with the spatial period of the resistance variation. Experiments 2 and 3 used a factorial design to disentangle the contributions of ridge and groove width. The stimuli constituted eight values of groove width at each of five levels of ridge width (Experiment 2) or the reverse (Experiment 3). Roughness magnitude increased with ridge width while remaining essentially invariant over groove width. Kinematic variations in exploration were observed across the surfaces. The data point to the promise of using inexpensive devices to create virtual textural variations under conditions of unconstrained exploration.
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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 | 0.006 |
| 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".