Distributions of fixations on biological motion displays depend on the task: Direction discrimination vs. gender classification
Bibliographic record
Abstract
Even when a display of a person walking is presented only as dots following the motion of the major joints, human observers can readily determine both the facing direction of locomotion and higher-level properties, including the gender of the individual. We investigated the spatial concentration of direction and gender cues, by tracking the eye movements of 16 participants while they judged either property of a point-light display. The walkers had different levels of ambiguity of both their direction and their gender, which affected the difficulty of the tasks. Fixation locations were recorded throughout the 2 s presentation times. We analyzed the fixation data in two ways: first by creating fixation maps for the different conditions, and second by finding the average number of fixations that fell into three ROIs, representing the shoulders, pelvis and feet. In accordance with past literature emphasizing the role of lateral shoulder sway in gender identification, participants on average fixated more on the shoulders in the gender task than in the direction task. Analysis of individual differences showed that more fixations in the shoulder region predicted slightly better performance in the gender task. On the other hand, the number of fixations on the pelvis, an area also known to contain gender information, was not significantly different between tasks. In accordance with studies showing that the motion of the feet contains cues to direction, participants fixated significantly more often on the feet in the direction task. The feet were rarely fixated in the gender task. In general, task difficulty did not have an effect on fixation patterns, except in the case of walkers viewed from the side, which produced on average slightly fewer feet fixations in the direction task.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".