The perceived roughness of resistive virtual textures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it