When is a Haptic Message Like an Inside Joke? Digitally Mediated Emotive Communication Builds on Shared History
Bibliographic record
Abstract
Touch is valued for supporting emotional bonds. How can people access its warmth and nuance remotely, when tech-mediated proxies are so different from direct touch? We assessed the viability of haptic animations as affect-embedded tactile messages, highlighting findings which demonstrate how crucial relationship and shared history is in influencing these expressions in design and interpretation. To investigate haptic messaging, we first identified a set of 10 common emotion-imbued scenarios by surveying 201 people in distance relationships. Then, using a novel prototype of a wearable spatial vibrotactile display, 10 intimate dyads designed 167 haptic encodings matching the provided scenarios plus 17 user-defined “wildcards”. A week later, 21 individuals interpreted sentiment from encodings designed by themselves, a partner or a stranger. We examined design strategies, engagement, and compared humanversusmachine interpretation accuracy. A striking finding was participants’ facile use of shared context when it was available, building on “inside stories” to communicate subtle meanings with high effectiveness despite the unfamiliar medium, and doing so with evident fun. We analyze recognition accuracy and share insights on what it might take to make interpersonal haptic messaging work.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".