Emulating human attention-getting practices with wearable haptics
Bibliographic record
Abstract
Our computers often need to get our attention, but have inadequate means of modulating the intrusiveness with which they do so. Humans commonly use social touch to gain one another's attention. In this paper, we describe an early exploration of how an expressive, wearable or holdable haptic display could emulate human social practices with the goal of evoking comparable responses from users. It spans three iterations of rapid prototyping and user evaluation, beginning with broad-ranging physical brainstorming, before proceeding to higher-fidelity actuated prototypes. User reactions were incorporated along the way, including an assessment of the low-fidelity prototypes' expressiveness. Our observations suggest that, using simple and potentially unintrusive body-situated mechanisms like a bracelet, it is possible to convey a range of socially gradable attention-getting expressions to be useful in real contexts.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".