Bibliographic record
Abstract
Introduction: Learning algorithms based on natural image statistics have been capable of generating oriented receptive fields akin to the properties of V1 neurons. Here we ask whether an analogous approach can be taken to study the basis of face viewpoint representations. This extends our previous work showing that deviations from head symmetry can be used to discriminate among head orientations near the frontal view. Methods: Male and female faces were digitized at 16 points around the perimeter of the head. Each head was digitized in 9 different horizontal rotations from −40° to +40° in 10° steps. For each of these rotations front, 24° up, and 24° down vertical rotations were digitized, making a total of 27 views of each head. The 27 views of all heads were then submitted to a principal component (PC) analysis. Psychophysical experiments were conducted to determine whether observers could correctly discriminate head orientation from the head outline alone. Results: PC analysis showed that three components accounted for 97% of the variance. PC1 (56%) was positively weighted for rightward rotations and negatively weighted for leftward rotations, with no weighting on front or up/down views. PC2 (29%) was heavily weighted for front views and did not discriminate between left and right rotations, so it functions as an estimator of bilateral symmetry. PC3 (12%) was positively weighted on upward views and negatively weighted on downward views with insignificant weighting on horizontal rotation. Psychophysical results showed that observers could accurately estimate head orientation from head outlines alone. Conclusions: Principal Components can be learned readily by Hebbian neural networks. Thus, we hypothesize that neural representations in face selective areas will reflect the small number of PCs that are theoretically necessary for representations of head rotation. Comparisons with neurophysiology appear to support this hypothesis.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.127 | 0.072 |
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".