Self Recognition versus Recognition of others by Biological Motion: Viewpoint-Dependent Effects
Bibliographic record
Abstract
We investigated the influence of viewing angle on performance in recognising the identity of one's own person and familiar individuals such as friends or colleagues from walking patterns. Viewpoint-dependent recognition performance was tested in two groups of twelve persons who knew each other very well. Participants' motion data were acquired by recording their walking patterns in three-dimensional space with the use of a motion capture system. Size-normalised point-light displays of biological motion of these walking patterns, including one's own, were presented to the same group members on a computer screen in frontal view, half-profile view, and profile view. Observers were requested to assign the person's name to the individual gait pattern. No feedback was given. Whereas recognition performance of one's own walking patterns was viewpoint independent, recognition rate for other familiar individuals was better for frontal and half-profile view than for profile view. These findings are discussed in the context of the theory of common coding of motor and visual body representations.
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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 | 0.000 | 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".