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Record W4407356666 · doi:10.1109/tvcg.2025.3549135

Behavioral Measures of Copresence in Co-located Mixed Reality

2025· article· en· W4407356666 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Visualization and Computer Graphics · 2025
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersAgence Nationale de la Recherche
KeywordsComputer scienceHuman–computer interactionVirtual reality

Abstract

fetched live from OpenAlex

When several people are co-located and immersed in a mixed reality environment, they may feel like they share the virtual environment or not. This feeling of copresence, along with its parent dimensions of social presence and presence, has been mostly studied by relying on subjective measures gathered through questionnaires. As a way to address the drawbacks of this approach, we introduce a protocol to gather behavioral measures in the context of co-located mixed reality. As a pair of participants avoid obstacles moving towards them, their errors, gaze, interpersonal distance, and timing are measured. By combining subjective measures gathered through a questionnaire drawing from previous studies on social presence with behavioral measures, we demonstrate new ways to assess how users experience copresence. We illustrate this protocol by evaluating the effect of visual feedback on collaborators' activity. The results of this experiment suggest the capability of our protocol by revealing the effect of visual feedback on both objective and subjective measures.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.354
Teacher spread0.291 · 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