The contribution of nonrigid motion and shape information to object perception in pigeons and humans
Bibliographic record
Abstract
The ability to perceive and recognize objects is essential to many animals, including humans. Until recently, models of object recognition have primarily focused on static cues, such as shape, but more recent research is beginning to show that motion plays an important role in object perception. Most studies have focused on rigid motion, a type of motion most often associated with inanimate objects. In contrast, nonrigid motion is often associated with biological motion and is therefore ecologically important to visually dependent animals. In this study, we examined the relative contribution of nonrigid motion and shape to object perception in humans and pigeons, two species that rely extensively on vision. Using a parametric morphing technique to systematically vary nonrigid motion and three-dimensional shape information, we found that both humans and pigeons were able to rely solely on either shape or nonrigid motion information to identify complex objects when one of the two cues was degraded. Humans and pigeons also showed similar 80% accuracy thresholds when the information from both shape and motion cues were degraded. We argue that the use of nonrigid motion for object perception is evolutionarily important and should be considered in general theories of vision at least with respect to visually sophisticated animals.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".