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Record W2594138553 · doi:10.1177/1046878116688236

The Impact of Health-Related User Interface Sounds on Player Experience

2017· article· en· W2594138553 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSimulation & Gaming · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of WaterlooOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHuman–computer interactionInterface (matter)Sound (geography)Game designComputer scienceVideo gameCharacter (mathematics)PsychologyCognitive psychologyMultimediaAcoustics

Abstract

fetched live from OpenAlex

Background. Understanding how sound functions on informational and emotional levels within video games is critical to understanding player experience of games. User interface sounds, such as player-character health, are a pivotal component of gameplay across many video game genres, yet have not been studied in detail. Method. To address this research gap in user interface sounds, we present two studies: The first study examines the impact of the presence or absence of player-health sounds on player experience. The second study explores the impact of the types of sound used to indicate player health. We use mixed methods with qualitative and physiological measures. Results. Our results reveal that despite the presence of visual cues, sound is still important to game design for conveying health-related information and that the type of sound affects player experience.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.123
GPT teacher head0.416
Teacher spread0.293 · 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