Toward Affective Handles for Tuning Vibrations
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
When refining or personalizing a design, we count on being able to modify or move an element by changing its parameters rather than creating it anew in a different form or location—a standard utility in graphic and auditory authoring tools. Similarly, we need to tune vibrotactile sensations to fit new use cases, distinguish members of communicative icon sets, and personalize items. For tactile vibration display, however, we lack knowledge of the human perceptual mappings that must underlie such tools. Based on evidence that affective dimensions are a natural way to tune vibrations for practical purposes, we attempted to manipulate perception along three emotion dimensions ( agitation , liveliness , and strangeness ) using engineering parameters of hypothesized relevance. Results from two user studies show that an automatable algorithm can increase a vibration’s perceived agitation and liveliness to different degrees via signal energy, while increasing its discontinuity or randomness makes it more strange . These continuous mappings apply across diverse base vibrations; the extent of achievable emotion change varies. These results illustrate the potential for developing vibrotactile emotion controls as efficient tuning for designers and end-users.
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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 | 0.001 | 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) | 0.001 | 0.001 |
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