Facilitating player progression by implementing procedural music in videogames
Bibliographic record
Abstract
While the multi-million-dollar videogame industry sees constant improvements in visuals and processing power with every new processor, gaming console, or graphics card release, is there any avenue to pursue new innovations in this discipline? One answer lies in the introduction of procedural music in videogames as an innovative means of overcoming the repetitive nature of traditionally composed game music and to enhance the immersive response of game music to player actions. While most studies are preoccupied with the descriptive evaluation of how entertaining procedural music is in comparison to traditionally composed music, we pursue a novel study on the utility of implementing procedural music in videogames as a tool for facilitating player progression. To do so we employ objective, quantitative measures to gather results that can quantify the utility of implementing procedural videogame music, unlike other studies. We demonstrate that users playing a game with a procedural music model that actively instructs and assists players can complete game levels in a significantly more time efficient manner and are more likely to rate the procedural music model as having significantly contributed to the game's entertainment and engagement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".