Here Comes No Boom! The Lack of Sound Feedback Effects on Performance and User Experience in a Gamified Image Classification Task
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".