7. Shape and Motion Integration in People Perception Depends on the Action of the Performer
Bibliographic record
Abstract
Perception of human action depends on both the body shape and motion of a performer. We can indirectly perceive the properties of an object being acted upon even when visual information is limited and the object itself is not visible; we accomplish this using internal models of a body’s dynamics and an action’s kinematics (Runeson & Frykholm, 1981). We are also sensitive to correlations between a performer’s shape and motion, known as internal consistency (Runeson & Frykholm, 1983). To investigate how decorrelating shape and motion affects indirect object perception, we ran an experiment where participants watched realistic avatars of performers manipulating invisible objects. Unbeknownst to participants, half of the stimuli were internally inconsistent: the shape of one performer was combined with the motion of a performer with a dissimilar body shape. Participants saw sled pushes, beanbag throws, and box lifts, and estimated the sled weight, throw distance, or box weight. For sled pushes, there was a shape-motion interaction such that heavy bodies were perceived as pushing heavier weights when animated with motion from light performers, and light bodies were perceived as pushing lighter weights when animated with motion from heavy performers. In contrast, participants estimated beanbag throw distance primarily from performer motion. Interpretation of the box lift data is more complex. In conclusion, the way in which our visual system combines shape and motion information depends on the role of body shape and centre of mass on the outcome of an action.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".