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Record W1976494093 · doi:10.1145/1180995.1181053

Haptic phonemes

2006· article· en· W1976494093 on OpenAlex
Mario Enriquez, Karon E. MacLean, Christian Chita

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHaptic technologyLearnabilityComputer scienceRecallHaptic perceptionStereotaxyStimulus (psychology)WaveformSet (abstract data type)Speech recognitionArtificial intelligencePsychologyCognitive psychology

Abstract

fetched live from OpenAlex

A haptic phoneme represents the smallest unit of a constructed haptic signal to which a meaning can be assigned. These haptic phonemes can be combined serially or in parallel to form haptic words, or haptic icons, which can hold more elaborate meanings for their users. Here, we use phonemes which consist of brief (<2 seconds) haptic stimuli composed of a simple waveform at a constant frequency and amplitude. Building on previous results showing that a set of 12 such haptic stimuli can be perceptually distinguished, here we test learnability and recall of associations for arbitrarily chosen stimulus-meaning pairs. We found that users could consistently recall an arbitrary association between a haptic stimulus and its assigned arbitrary meaning in a 9-phoneme set, during a 45 minute test period following a reinforced learning stage.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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

Quick stats

Citations103
Published2006
Admission routes1
Has abstractyes

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