Sounds of the Past: Creating Immersion in Video Games Through Diegetic, Pre-Existing Music
Bibliographic record
Abstract
The modern state of gaming has seen a variety of interactive narratives play out in a variety of worlds. A major category of setting in video games is ‘the past’, whether that be a form of realistic fiction, an alternate history, or something which draws inspiration from some aspect of the real past to create an entirely imagined location. Despite the differences in their likenesses to our own world, many games referencing the past share a commonality in their musical soundtracks: the use of pre-existing music. Songs like “Drunken Sailor” or “La Cucaracha” are prolific, but there exists an ever-growing variety with less well-known examples. An interesting pattern which emerges in the temporal space of these games is the diegetic use of pre-existing music. Using pre-existing music comes with the risk of not aligning with a player’s expectations for a given piece, and using diegetic music creates a risk of misalignment between the action the player sees and the audio they hear. Combining these two modalities could initially seem to be not worth the collective risks, but games like Pentiment (2022), Assassin’s Creed: Unity (2014), and Life Is Strange: True Colors (2021) all successfully integrate their pre-existing audio with game narratives. What purpose does pre-existing, diegetic music serve to warrant its widespread inclusion in such games? An analysis of the three games and their respective examples will demonstrate the importance of integrating player expectations and experiences with the physical space established in the game to produce a more immersive 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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".