Gaze patterns during perception of direction and gender from biological motion
Bibliographic record
Abstract
Humans can perceive many properties of a creature in motion from the movement of the major joints alone. However it is likely that some regions of the body are more informative than others, dependent on the task. We recorded eye movements while participants performed two tasks with point-light walkers: determining the direction of walking, or determining the walker's gender. To vary task difficulty, walkers were displayed from different view angles and with different degrees of expressed gender. The effects on eye movement were evaluated by generating fixation maps, and by analyzing the number of fixations in regions of interest representing the shoulders, pelvis, and feet. In both tasks participants frequently fixated the pelvis region, but there were relatively more fixations at the shoulders in the gender task, and more fixations at the feet in the direction task. Increasing direction task difficulty increased the focus on the foot region. An individual's task performance could not be predicted by their distribution of fixations. However by showing where observers seek information, the study supports previous findings that the feet play an important part in the perception of walking direction, and that the shoulders and hips are particularly important for the perception of gender.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".