Acceleration carries the local inversion effect in biological motion perception
Bibliographic record
Abstract
The ability to derive the facing direction of a spatially scrambled point-light walker relies on the motions of the feet and is impaired if they are inverted. We exploited this local inversion effect in three experiments that employed novel stimuli derived from only fragments of full foot trajectories. In Experiment 1, observers were presented with stimuli derived from a single fragment or a pair of counterphase fragments of the foot trajectory of a human walker in a direction discrimination task. We show that direction can be retrieved for displays as short as 100 ms and is retrieved in an orientation-dependent manner only for stimuli derived from the paired fragments. In Experiment 2, we investigated direction retrieval from stimuli derived from paired fragments of other foot motions. We show that the inversion effect is correlated with the difference in vertical acceleration between the constituent fragments of each stimulus. In Experiment 3, we compared direction retrieval from the veridical human walker stimuli with stimuli that were identical but had accelerations removed. We show that the inversion effect disappears for the stimuli containing no accelerations. The results suggest that the local inversion effect is carried by accelerations contained in the foot motions.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".