Characterizing global and local mechanisms in biological motion perception
Bibliographic record
Abstract
The perception of biological motion is subserved by both a global process that retrieves structural information and a local process that is sensitive to individual limb motions. Here, we present an experiment aimed to characterize these two mechanisms psychophysically. Naive observers were tested on one of two tasks. In a walker detection task designed to address global processing, observers were asked to discriminate coherent from scrambled walkers presented in separate intervals. In an alternate direction discrimination task designed to address primarily local processing, observers were asked to discriminate walking direction from both coherent and spatially scrambled displays. In both tasks, we investigated performance-specificity to human (versus non-human) motion and the effects of mask density and learning on task performance. Performance in the walker detection task was best for the human walker, was susceptible to learning, and was heavily hindered by increasing mask densities. In contrast, performance on the direction discrimination task, in particular for the scrambled walkers, was unaffected by walker type, did not show a learning trend, and was relatively robust to masking noise. These findings suggest that the visual system processes global and local information contained in biological motion via distinct neural mechanisms that have very different properties.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".