Fast Calibration of Haptic Texture Synthesis Algorithms
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Bibliographic record
Abstract
Calibrating displays can be a time-consuming process. We describe a fast technique for adjusting the subjective experience of roughness produced by different haptic texture synthesis algorithms. Its efficiency is due to the exponential convergence of the ldquomodified binary search methodrdquo (mobs) applied to find points of subjective equivalence between virtual haptic textures synthesized by different algorithms. The method was applied to find the values of the coefficient of friction in a friction-based texture algorithm that yield the same perception of roughness as the normal-force variations of conventional texture synthesis algorithms. Our main result is a table giving the perceptual equivalence between parameters having different physical dimensions. A similar method could be applied to other perceptual dimensions provided that the controlling parameter be monotonically related to a subjective estimate.
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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 | 0.000 | 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) | 0.000 | 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