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Record W2095066181 · doi:10.1145/2145204.2145291

Communication channels and awareness cues in collocated collaborative time-critical gaming

2012· article· en· W2095066181 on OpenAlexaff
Victor Cheung, Y.-L. Betty Chang, Stacey D. Scott

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVariety (cybernetics)Computer scienceHuman–computer interactionGazeSensory cueMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.378
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2012
Admission routes1
Has abstractyes

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