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Record W2561800237 · doi:10.1109/iisa.2016.7785403

Sound and stereoscopic 3D: Examining the effects of sound on depth perception in stereoscopic 3D

2016· article· en· W2561800237 on OpenAlexaff
Brian Cullen, Karen Collins, Andrew Hogue, Bill Kapralos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsOntario Tech UniversityUniversity of Waterloo
Fundersnot available
KeywordsStereoscopyPerceptionComputer scienceSound (geography)Surround soundDepth perceptionStereophonic soundVirtual realitySound perceptionSonificationVisualizationAcousticsHuman–computer interactionComputer visionArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Prior work focusing on 2D visuals primarily suggests that sound has a significant impact on visual perception. However, little work has considered what, if any, effects sound, including its spatial positioning and the addition of various auditory effects, have on our perception of stereoscopic 3D (S3D) imagery. Here we present the results of two experiments that were conducted to examine the effects of sound and various auditory conditions on stereoscopic 3D depth perception within a virtual (game) environment. Our results reveal that sound can have a significant effect on S3D depth perception. Results suggest that asynchronous audio-visual interactions, the type of sound, and various audio effects can influence distance perception within a virtual environment that incorporates S3D viewing. Our results have implications for game designers, who, with the appropriate use of sound and S3D interactions, can improve the player's experience within stereoscopic 3D-based virtual environments.

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 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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.997

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)0.0040.000

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 head0.058
GPT teacher head0.344
Teacher spread0.286 · 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 teacher head, not a consensus.

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

Citations6
Published2016
Admission routes1
Has abstractyes

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