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Record W4224993553 · doi:10.1145/3491102.3502067

The Effect of the Vergence-Accommodation Conflict on Virtual Hand Pointing in Immersive Displays

2022· article· en· W4224993553 on OpenAlexaff
Anil Ufuk Batmaz, Mayra Donaji Barrera Machuca, Junwei Sun, Wolfgang Stuerzlinger

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems · 2022
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsSimon Fraser UniversityHuawei Technologies (Canada)Dalhousie University
Fundersnot available
KeywordsAccommodationVergence (optics)Computer scienceComputer graphics (images)Computer visionHuman–computer interactionMultimediaPsychology

Abstract

fetched live from OpenAlex

Previous work hypothesized that for Virtual Reality (VR) and Augmented Reality (AR) displays a mismatch between disparities and optical focus cues, known as the vergence and accommodation conflict (VAC), affects depth perception and thus limits user performance in 3D selection tasks within arm’s reach (peri-personal space). To investigate this question, we built a multifocal stereo display, which can eliminate the influence of the VAC for pointing within the investigated distances. In a user study, participants performed a virtual hand 3D selection task with targets arranged laterally or along the line of sight, with and without a change in visual depth, in display conditions with and without the VAC. Our results show that the VAC influences 3D selection performance in common VR and AR stereo displays and that multifocal displays have a positive effect on 3D selection performance with a virtual hand.

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.017
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)0.0030.000

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 head0.053
GPT teacher head0.314
Teacher spread0.261 · 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 designBench or experimental
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

Citations56
Published2022
Admission routes1
Has abstractyes

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