Mug shots: Systematic biases in the perception of facial orientation
Bibliographic record
Abstract
The angular orientation of a face pictured in half-profile view is systematically overestimated by the human observer. For instance, a 35 deg view is estimated to be oriented around 45 deg. What is the cause for this perceptual orientation bias? Here, we address three related questions. (1) Is the phenomenon specific to pictorial projections or does it also occur in 3D space? (2) Can it be explained with the depth compression expected when the vantage point of the observer is closer to the picture than the point of projection? (3) Does the visual system use a shape prior that does not match the elliptical horizontal cross section of a typical head? Exp. 1 was conducted in virtual reality. We used a method of adjustment (“orient this face into a 45° position”). We found the orientation bias was smaller than expected and only marginally different between picture and 3D conditions. In Exp. 2 we presented static pictures and systematically varied the vantage point of the observer relative to the point of projection of the picture. We observed a pronounced bias which was not dependent on the vantage point. In Exp. 3, we replicated the orientation bias with a non-facial object – a coffee mug with a handle that defined its orientation. We systematically modified the shape of the mug between circular and elliptical horizontal cross sections. Mugs were then presented either as static images or as short movies with the mug rotating about its vertical axis. Participants estimated orientation almost veridically for circular shapes and displayed predictable errors for other shapes. The shape-dependent orientation biases were much smaller for the movies compared to the pictures. We conclude: The visual system adopts the heuristic of a cylindrical head shape unless explicit information about its shape is provided, e.g., through structure-from-motion.
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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 | 0.010 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".