Independent discrimination of left/right and up/down head orientations
Bibliographic record
Abstract
The great majority of head and face discrimination studies have utilized either frontal or left/right rotated views of the head, while virtually none have examined the up/down direction or interactions between these two dimensions. A principal component (PC) analysis of head shapes defined relative to the bridge of the nose suggests that just three components can encode head shape across ±40° horizontally and ±20° vertically. Furthermore, these PCs suggest that horizontal and vertical head orientations may be represented orthogonally. To test these hypotheses, discrimination thresholds for head orientation were measured for left/right discrimination among head shapes that were either oriented up, frontal, or down in the vertical dimension. Thresholds were found to be independent of vertical orientation. A control experiment randomized the vertical orientation from trial to trial and showed that left/right orientation discrimination was unaffected. An analogous result was obtained for discrimination of head orientation in the up/down direction with randomization across left/right orientations. Thus, discrimination of vertical head orientations is independent of horizontal head orientation, a result consistent with the PC analysis. In further studies we are using head orientation aftereffects following adaptation (Fang & He, Neuron, 45, 7930800, 2005) to characterize receptive fields responsible for the representation of left/right and up/down head orientations. These results imply that the visual system can estimate head orientation independently in two dimensions, and this may greatly simplify the process of individual face encoding and recognition.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".