Bibliographic record
Abstract
Biological motion point-light walkers convey information about the sex of a walker. As has been shown earlier, retrieving this information depends on the viewpoint: Frontal views are easier to classify than profile views. However, what happens if a walker is shown from a varying viewpoint as is the case when we see a walker walking on a circle? Multiple viewpoints should facilitate activation of a three-dimensional representation which might help classification. On the other hand, the additional rotation might mask intrinsic (that is, relative) motion diagnostic for the sex of the walker and therefore hinder classification. In the current study, observers had to indicate perceived sex of point-light displays of individual walkers shown either in frontal view (0 deg), half profile view (30 deg), profile view (90 deg), or in a condition in which the viewpoint rotated from −50 to 50 deg over the display time of 2 sec. In addition, we manipulated the information provided. Walkers contained either only structural information, only kinematic information, or all information. The results replicated earlier findings showing that performance at frontal and half profile view is much better than at profile view and that kinematic information is required for sex classification whereas structural information has very little diagnostic value. In addition, we could show that rotating views of a walker are clearly resulting in worse classification than frontal or half-profile views, but were classified much better than profile view walkers. We conclude that three-dimensional representations do not facilitate sex classification from biological motion. Diagnostic information about sex is primarily contained in the kinematics within the fronto-parallel plane and the motion due to rotation of the walker aggravates retrieval of this information.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".