Bibliographic record
Abstract
In 1978, James Cutting published an algorithm to generate point-light displays that resemble the movements of the joints of a human walker. The method has since been used frequently to create stimuli for research on biological motion perception. More recently, Troje and Westhoff (2006) found that pattern of local movement of the feet was used to derive the direction in which a point-light walker is facing, even when structural information is removed. The results of previous studies that direction could not be determined using a scrambled version of Cutting's walker, may be explained by the significantly different motion of the feet between Cutting's walker and motion-captured humans. To compare the two stimuli, 14 participants performed a detection task and a direction task. Walkers consisted of 11 points presenting a sagittal view. In the detection task, walkers were embedded in a scrambled walker mask consisting of 50, 100, or 200 dots. Participants had to decide which of two successive intervals contained the walker. In the direction task, participants judged whether the walking figure was oriented towards the left or the right. The mask consisted of randomly appearing stationary dots (50, 200, or 750) with limited lifetime. Half of the walkers were spatially coherent and half of them were scrambled. Observers performed equally well for the two walkers in the detection task. However in the direction task, the error rate for Cutting's walker was significantly higher than for the motion-captured walker. Most of the difference came from the scrambled walker condition, where error rate increased from 39% to 48%. We conclude that Cutting's walker lacks critical features which signal direction in real walking motion, and suggest that studies which have presented the local motion of the Cutting walker as a stimulus need to be revisited.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.039 |
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".