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Record W4225137109 · doi:10.1145/3491102.3517581

Here Comes No Boom! The Lack of Sound Feedback Effects on Performance and User Experience in a Gamified Image Classification Task

2022· article· en· W4225137109 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.

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsValence (chemistry)ArousalImmersion (mathematics)Human–computer interactionComputer scienceBoomAffect (linguistics)Task (project management)MultimediaPsychologyCognitive psychologySocial psychologyEngineeringCommunicationMathematics

Abstract

fetched live from OpenAlex

Sound effects (SFX) complement the visual feedback provided by gamification elements in gamified systems. However, the impact of SFX has not been systematically studied. To bridge this gap, we investigate the effects of SFX—supplementing points (as a gamification element)—on task performance and user experience in a gamified image classification task. We created 18 SFX, studied their impact on perceived valence and arousal (N = 49) and selected four suitable SFX to be used in a between-participants user study (N = 317). Our findings show that neither task performance, affect, immersion, nor enjoyment were significantly affected by the sounds. Only the pressure/tension factor differed significantly, indicating that low valence sounds should be avoided to accompany point rewards. Overall, our results suggest that SFX seem to have less impact than expected in gamified systems. Hence, using SFX in gamification should be a more informed choice and should receive more attention in gamification research.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.629

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 studies0.0010.000
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 head0.190
GPT teacher head0.347
Teacher spread0.157 · 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