Defining Haptic Experience: Foundations for Understanding, Communicating, and Evaluating HX
Bibliographic record
Abstract
Haptic technology is maturing, with expectations and evidence that it will contribute to user experience (UX). However, we have very little understanding about how haptic technology can influence people's experience. Researchers and designers need a way to understand, communicate, and evaluate haptic technology's effect on UX. From a literature review and two studies - one with haptics novices, the other with expert hapticians - we developed a theoretical model of the factors that constitute a good haptic experience (HX). We define HX and propose its constituent factors: design parameters of Timeliness, Density, Intensity, and Timbre; the cross-cutting concern of Personalization; usability requirements of Utility, Causality, Consistency, and Saliency; and experiential factors of Harmony, Expressivity, Autotelics, Immersion, and Realism as guiding constructs important for haptic experience. This model will help guide design and research of haptic systems, inform language around haptics, and provide the basis for evaluative instruments, such as checklists, heuristics, or questionnaires.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".