MétaCan
Menu
Back to cohort
Record W1558569915 · doi:10.32920/25413115.v1

Towards a Model Human Cochlea: Sensory substitution for crossmodal audio-tactile displays

2024· article· en· W1558569915 on OpenAlexaff
Maria Karam, Frank Russo, Carmen Branje, Deborah I. Fels

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCrossmodalSensory substitutionSensory systemCochleaAudio visualSubstitution (logic)PsychologyComputer scienceCommunicationNeurosciencePerceptionMultimediaVisual perception

Abstract

fetched live from OpenAlex

We present a Model Human Cochlea (MHC): a sensory substitution technique for creating a crossmodal audio-touch display. This research is aimed at designing a chair-based interface to support deaf and hard of hearing users in experiencing musical content associated with film, and seeks to develop this multisensory crossmodal display as a framework for supporting research in enhancing sensory entertainment experiences for universal design. The MHC uses audio speakers as vibrotactile devices placed along the body to facilitate the expression of emotional elements that are associated with music. We present the results of our formative study, which compared the MHC to conventional audio speaker displays for communicating basic emotional information through touch. Results suggest that the separation of audio signals onto multiple vibrotactile channels is more effective at expressing emotional content than is possible using a complete audio signal as vibrotactile stimuli.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.087
GPT teacher head0.363
Teacher spread0.277 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicTactile and Sensory InteractionsFrench-language works237,207