An Analysis of Orientation Bias: A Comparison Between Visual and Pictorial Space in Virtual Reality
Bibliographic record
Abstract
If participants are asked to orient a face half way between frontal view and profile view, they typically choose an angle somewhere between 30 and 40 degrees. In this study, we demonstrate this phenomenon called orientation bias, and we test the hypothesis that it is directly related to presenting the face in the pictorial space of the flat screen rather than in the egocentric visual space of the observer.Participants were required to use a keyboard to rotate a 3D rendering of a human head to orient it at 45 degrees, that is, half way between frontal and profile view. Employing a repeated-measures design, participants completed two blocks in counterbalanced order. Both viewing conditions were implemented in virtual reality. In the first, participants saw a columnar pedestal with a head mounted on top of it in the visual space before them. In the second block, the very same scene was recorded with a fixed camera and projected on a virtual computer screen.The results indicated that the mean angle estimations in visual space (M = 43.01, SD = 5.96) and pictorial space (M = 37.40, SD = 6.99) differed significantly, t(15) = 5.13, p < .001.These differences could be a result of depth compression, which has been previously described in the context of distance perception. Given that interpretation, our results imply that depth compression might be a result of the flatness of the picture plane which is perceived in a “twofold” way alongside the depicted contents of the image.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".