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Record W1989222389 · doi:10.1145/2457450.2457451

Human perception of haptic-to-video and haptic-to-audio skew in multimedia applications

2013· article· en· W1989222389 on OpenAlexaff
Juan M. Silva, Mauricio Orozco, Jongeun Cha, Abdulmotaleb El Saddik, Emil M. Petriu

Bibliographic record

VenueACM Transactions on Multimedia Computing Communications and Applications · 2013
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHaptic technologyComputer scienceAsynchrony (computer programming)PerceptionHaptic perceptionMultimediaVideo gameAsynchronous communicationHuman–computer interactionArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

The purpose of this research is to assess the sensitivity of humans to perceive asynchrony among media signals coming from a computer application. Particularly we examine haptic-to-video and haptic-to-audio skew. For this purpose we have designed an experimental setup, where users are exposed to a basic multimedia presentation resembling a ping-pong game. For every collision between a ball and a racket, the user is able to perceive auditory, visual, and haptic cues about the collision event. We artificially introduce negative and positive delay to the auditory and visual cues with respect to the haptic stream. We subjectively evaluate the perception of inter-stream asynchrony perceived by the users using two types of haptic devices. The statistical results of our evaluation show perception rates of around 100 ms regardless of modality and type of device.

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)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.319
Teacher spread0.279 · 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 designObservational
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

Citations25
Published2013
Admission routes1
Has abstractyes

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