Feel–Play–Imagine: Structured introduction and imagination of haptics with storytellers
Bibliographic record
Abstract
Haptic technology offers new opportunities for interaction, yet remains inaccessible to people unfamiliar with the technology due to challenges in rapid prototyping and the absence of a widely understood vocabulary, making early-stage design communication difficult. To address these challenges, we developed Feel-Play-Imagine (FPI), a method for haptics experts to involve team members and stakeholders in the early stages of design, and explored its use in the context of storytelling. FPI involves introducing people to haptics through experiencing polished haptic experiences in context (Feel) and experimenting with alternative modalities (Play), then engaging in discussions using stories to imagine designed experiences (Imagine). We report on the results of using FPI in an ongoing co-design project and a lab study with 10 expert storytellers from various backgrounds. Our findings include the value of hands-on and playful experiences to learn about haptic technologies, the ability of FPI to support design decisions, the ability of our developed Worksheet to structure discussion in some contexts, and the need to support multimodal and gestural communication when discussing haptic and tangible interaction.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".