MétaCan
Menu
Back to cohort
Record W2061875837 · doi:10.1117/12.906762

Stereoscopic 3D video games and their effects on engagement

2012· article· en· W2061875837 on OpenAlexaff
Andrew Hogue, Bill Kapralos, Chris Zerebecki, Mina Tawadrous, Brodie Stanfield, Urszula Hogue

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsStereoscopyComputer scienceVideo gameImmersion (mathematics)Context (archaeology)MultimediaGame mechanicsHuman–computer interactionComputer graphics (images)Computer vision

Abstract

fetched live from OpenAlex

With television manufacturers developing low-cost stereoscopic 3D displays, a large number of consumers will undoubtedly have access to 3D-capable televisions at home. The availability of 3D technology places the onus on content creators to develop interesting and engaging content. While the technology of stereoscopic displays and content generation are well understood, there are many questions yet to be answered surrounding its effects on the viewer. Effects of stereoscopic display on passive viewers for film are known, however video games are fundamentally different since the viewer/player is actively (rather than passively) engaged in the content. Questions of how stereoscopic viewing affects interaction mechanics have previously been studied in the context of player performance but very few have attempted to quantify the player experience to determine whether stereoscopic 3D has a positive or negative influence on their overall engagement. In this paper we present a preliminary study of the effects stereoscopic 3D have on player engagement in video games. Participants played a video game in two conditions, traditional 2D and stereoscopic 3D and their engagement was quantified using a previously validated self-reporting tool. The results suggest that S3D has a positive effect on immersion, presence, flow, and absorption.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicVirtual Reality Applications and ImpactsFrench-language works237,207