Scaffolding Player Location Awareness through Audio Cues in First-Person Shooters
Bibliographic record
Abstract
Digital games require players to learn various skills, which is often accomplished through play itself. In multiplayer games, novices can feel overwhelmed if competing against better players, and can fail to improve, which may lead to unsatisfying play and missed social play opportunities. To help novices learn the requisite skills, we first determined how experts accomplish an important task in multiplayer FPS games -- locating their opponent. After determining that an understanding of audio cues and how to leverage them was critical, we designed and evaluated two systems for introducing this skill of locating opponents through audio cues -- a training system, and a modified game interface. We found that both systems improved accuracy and confidence, but that the training system led to more audio cues being recognized. Our work may help people of disparate skill play together, by scaffolding novices to learn and use a strategy commonly employed by experts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".