MétaCan
Menu
Back to cohort
Record W2141829608 · doi:10.1145/1190036.1190041

Personal space in virtual reality

2006· article· en· W2141829608 on OpenAlexaff
Laurie M. Wilcox, Robert S. Allison, Samuel Elfassy, Cynthia Grelik

Bibliographic record

VenueACM Transactions on Applied Perception · 2006
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsYork University
Fundersnot available
KeywordsStereoscopyVirtual realitySpace (punctuation)Sense of presenceInterpersonal communicationProjection (relational algebra)Computer scienceHuman–computer interactionPsychologyMultimediaComputer visionSocial psychology

Abstract

fetched live from OpenAlex

Improving the sense of “presence” is a common goal of three-dimensional (3D) display technology for film, television, and virtual reality. However, there are instances in which 3D presentations may elicit unanticipated negative responses. For example, it is well established that violations of interpersonal space cause discomfort in real-world situations. Here we ask if people respond similarly when viewing life-sized stereoscopic images. Observers rated their level of comfort in response to animate and inanimate objects in live and virtual (stereoscopic projection) viewing conditions. Electrodermal activity was also recorded to monitor their physiological response to these stimuli. Observers exhibited significant negative reactions to violations of interpersonal space in stereoscopic 3D displays, which were equivalent to those experienced in the natural environment. These data have important implications for the creation of 3D media and the use of virtual reality systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.024
GPT teacher head0.269
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations128
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueACM Transactions on Applied PerceptionSame topicVirtual Reality Applications and ImpactsFrench-language works237,207