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Record W3025743099 · doi:10.1109/vrw50115.2020.00193

Perceptual Distortions Between Windows and Screens: Stereopsis Predicts Motion Parallax

2020· article· en· W3025743099 on OpenAlex
Xiaoye Michael Wang, Anne Thaler, Siavash Eftekharifar, Adam O. Bebko, Nikolaus F. Troje

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

Venue2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsQueen's UniversityYork University
Fundersnot available
KeywordsParallaxStereopsisComputer scienceComputer visionArtificial intelligencePerceptionBinocular disparityComputer graphics (images)Psychology

Abstract

fetched live from OpenAlex

Stereopsis and motion parallax provide depth information, capable of producing more realistic user experiences after being integrated into a flat screen (e.g. immersive virtual reality). Extensive research shows that stereoscopic screens increase realism, while few studies have investigated users’ responses to parallax screens without stereopsis. In this study, we examined users’ evaluations of screens with only parallax or stereopsis. We found that with only parallax, the mapping between observer motion and viewpoint change should be around 0.6 for a more realistic perceptual experience, and observers were less sensitive to stereoscopic distortions as a result of a different interpupillary distance scaling.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score1.000

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.142
GPT teacher head0.328
Teacher spread0.186 · 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