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Record W2052209655 · doi:10.1145/2543698.2543703

The Effect of Vibrotactile Stimulation on the Emotional Response to Horror Films

2013· article· en· W2052209655 on OpenAlexaff
Carmen Branje, Gabe Nespoil, Frank Russo, Deborah I. Fels

Bibliographic record

VenueComputers in entertainment · 2013
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSkin conductanceFidelityHigh fidelityPsychologyAudiologyComputer scienceMultimediaAcousticsEngineeringMedicineTelecommunicationsBiomedical engineering

Abstract

fetched live from OpenAlex

Over the last decade consumers have witnessed a dramatic increase in the use of low fidelity, discrete vibrotactile feedback to enhance or replace audio stimuli in entertainment systems. However, use of high-resolution continuous vibrotactile displays remains quite uncommon. The Emoti-Chair is a high-resolution continuous vibrotactile display that may be driven by any type of audio signal, and is purported to convey the emotional properties of sound through organized vibrotactile stimulation. In the current study, we examined the ability of the Emoti-Chair to convey the emotional content of the soundtrack in a horror film. Increases in skin conductance levels were observed when vibrotactile stimuli were added to audio/visual film content. These results provide evidence that users can augment the communication of emotion in film through use of a high-resolution continuous vibrotactile display.

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)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0030.000

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 head0.017
GPT teacher head0.311
Teacher spread0.294 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

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