Distinguishability of periodic haptic stimuli in the frequency domain
Bibliographic record
Abstract
A current issue in human-computer interaction is the design of haptic stimuli. Recent studies reported that humans can successfully recognize various haptic stimuli, and thus suggest methods of designing distinguishable haptic stimuli. However, such methods have been complicated and inconsistent, indicating a need for exploring different parameters associated with human perception. Therefore we conducted this pilot study on the distinguishability of haptic stimuli. We took advantage of frequency domain analysis to explore the potential of a parameter of relative percent power difference (%PD). The Fourier series was used to design a range of synthetic haptic stimuli using approximations of square and saw-tooth signals of the same fundamental frequency and amplitude. The stimuli differed by the range of harmonic components in each series. Preliminary results revealed that stimuli based on the saw-tooth signal were more distinguishable than their square based counterparts. While investigating the parameter of %PD to measure differentiability, we observed that participants had more difficulty in distinguishing stimuli with smaller relative %PD than stimuli with greater relative %PD. A considerable change of differentiability between 10 and 35 %PD pointed towards potential just-noticeable-difference for distinguishability. Further investigation is needed to support these findings.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".