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Record W3096444709 · doi:10.1145/3410404.3414246

The Potential Disconnect between Time Perception and Immersion: Effects of Music on VR Player Experience

2020· article· en· W3096444709 on OpenAlexaff
Katja Rogers, Maximilian Milo, Michael Weber, Lennart E. Nacke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsImmersion (mathematics)PerceptionOperationalizationVirtual realityEmpirical researchPsychologyMultimediaTime perceptionComputer scienceCognitive psychologyGame mechanicsHuman–computer interactionMathematics

Abstract

fetched live from OpenAlex

How much music contributes to player experience (PX) in virtual reality (VR) games remains unclear in the games user research literature. A core factor of PX in VR games that has not been studied before (in relation to audio or otherwise) is time perception. Thus, we provide the first empirical exploration of how music affects time perception in a VR game. In a user study (N=64), we investigated the effects of music on PX and time perception (operationalized as retrospective time estimation). Participants retrospectively perceived time to pass significantly quicker in the VR game when music was present, but reported no difference in PX components, including immersion. This contributes to ongoing discourse on the surprising lack of music effects in VR games. Moreover, our results highlight the need to re-conceptualize our understanding of the relationship between time perception and immersion in games.

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)0.002
metaresearch head score (Gemma)0.019
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.035
GPT teacher head0.248
Teacher spread0.214 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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