Bibliographic record
Abstract
Visual perception of biological motion is a complex process that involves several independent mechanisms. Particularly, two such mechanisms have to be distinguished. One responds to the local motion of the feet of a moving animal and signals both the presence and the facing direction of the animal. The second integrates the global configuration of a set of moving dots into the coherent, articulated shape of a human or animal body. We hypothesize that the first one is evolutionary old, not specific to human motion, and not sensitive to learning, while the second requires individual learning and is therefore specific to human motion. Here, we conducted two experiments. The first one required an observer to derive the direction in which a stationary walker was facing. The walker depicted either a human walker, a walking pigeon or a walking cat masked by a varying number of stationary flickering dots. Walkers were shown either spatially intact or scrambled. Five blocks of 60 trials each were run to probe for learning effects. The second experiment was a 2AFC detection experiment. In each trial, two displays were shown. One contained only a mask of scrambled walkers while the other one also contained a coherent walker. Walkers depicted a human, a pigeon, or a cat. Again, five blocks with 60 trials each were run to test for learning effects. Results confirmed our hypotheses: For the first task which focused on the local mechanism, we found effects of the number of masking dots, and an effect of scrambling, but neither an effect of the nature of the walker, nor an effect of learning. In contrast, for the second task (requiring global shape-from-motion processing) we found much better performance for the human walker as compared to the non-human walkers, and a strong effect of learning.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.051 |
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".