MétaCan
Menu
Back to cohort
Record W4406212671 · doi:10.1145/3711842

A Unified Model for Haptic Experience

2025· article· en· W4406212671 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACM Transactions on Computer-Human Interaction · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaEuropean CommissionOntario Research Foundation
KeywordsHaptic technologyHuman–computer interactionComputer scienceUser experience designKey (lock)Experience designStereotaxySimulation

Abstract

fetched live from OpenAlex

Designed haptic feedback—technology-mediated touch feedback—has the potential to mediate positive and meaningful experiences. These experiences are rich and complex in nature and thus challenging to design. Established User Experience (UX) and Haptic Experience (HX) models describe the design of experiences; however, they are too general and evaluation-focused to inform haptic experience design. We review 104 publications designing haptic experiences and analyse how researchers consider pragmatic, hedonic, and eudaimonic qualities of haptic experience. Our findings show that researchers mainly engage with the pragmatic qualities of the experience. We thus propose a unified model for HX for understanding the design of haptic experiences, combining key elements of UX and HX research to give haptic designers a tool for thinking about the rich and complex haptic experiences elicited by their designs. This raises open questions for haptic experience research, as designing mediated touch experiences through haptic technology remains challenging.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.358
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it