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Record W2167792477 · doi:10.1109/vr.2001.913793

Tolerance of temporal delay in virtual environments

2002· article· en· W2167792477 on OpenAlexaff
Robert S. Allison, Laurence R. Harris, Michael Jenkin, Urszula Jasiobedzka, James E. Zacher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsYork University
Fundersnot available
KeywordsOscillopsiaIllusionPerceptionLatency (audio)Simulator sicknessVirtual machineComputer scienceVirtual realityOptical head-mounted displayPhase lagLagComputer visionEye movementArtificial intelligencePsychologyCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

To enhance presence, facilitate sensory motor performance, and avoid disorientation or nausea, virtual-reality applications require the perception of a stable environment. End-end tracking latency (display lag) degrades this illusion of stability and has been identified as a major fault of existing virtual-environment systems. Oscillopsia refers to the perception that the visual world appears to swim about or oscillate in space and is a manifestation of this loss of perceptual stability of the environment. The effects of end-end latency and head velocity on perceptual stability in a virtual environment were investigated psychophysically. Subjects became significantly more likely to report oscillopsia during head movements when end-end latency or head velocity were increased. It is concluded that perceptual instability of the world arises with increased head motion and increased display lag. Oscillopsia is expected to be more apparent in tasks requiring real locomotion or rapid head movement.

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)0.008
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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

Citations163
Published2002
Admission routes1
Has abstractyes

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