Communication channels and awareness cues in collocated collaborative time-critical gaming
Bibliographic record
Abstract
During collaborative gameplay players make use of various methods to become aware of the overall game status, develop strategies, and convey information to other players. Efficient and effective use of these methods is essential, especially during fast-paced time-critical collaborative games such as first-person shooters. This paper presents an observational study aimed to understand the communication channels and awareness cues used by players during gameplay to collaboratively achieve the game objectives. The study revealed that players utilize a variety of unconventional communication channels and awareness cues in both the physical and virtual environments to compensate for the inability to use commonly available collaborative human interaction mechanisms, such as eye gaze and gesturing, during gameplay. Players tended to use only auditory cues from their partner in the physical environment, while relying heavily on interacting with their partner through the virtual environment, using a variety of central and peripheral cues to maintain awareness during gameplay. Implications of these findings are discussed and recommendations for improving the quality of gameplay are provided.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".