Facial orientation biases in visual vs. pictorial space
Bibliographic record
Abstract
The orientation of a half-profile face presented on a screen or printed out on paper tends to get overestimated. 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 the phenomenon itself, 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. In our experiment, we asked participants to use keyboard presses to rotate a 3D rendering of a human head to orient it at 45 deg, that is, half way between frontal and profile view. A single block consisted of 80 trials. In each of them, the head was initially presented in a random initial orientation. Employing a repeated-measures design, participants completed two such blocks in counterbalanced order. Both viewing conditions were implemented in virtual reality (HTC Vive with Lighthouse tracking). In the first, participants saw a columnar pedestal with the head mounted on top of it in the visual space before them. In the second block, the same scene was recorded with a fixed camera and projected on a virtual computer screen. The results indicated that the mean estimates for angular orientation in visual space (M = 43.01, SD = 5.96) and pictorial space (M = 37.40, SD = 6.99) did differ significantly, t(15) = 5.13, p < .001 (two-tailed t-test). That fact that overestimation of slant angles observed in pictorial representations disappears in visual space is interpreted as evidence that the observed orientation bias is a result of depth compression due to the flatness of the picture itself which is perceived alongside with the depicted contents of the picture in a "twofold" way. Meeting abstract presented at VSS 2018
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".