Modulation of Viewpoint Effects in Object Recognition by Shape and Motion Cues
Why this work is in the frame
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Bibliographic record
Abstract
In three experiments, we examined the role of structural similarity and different types of motion on the efficiency of performing same--different shape judgments across changes in viewpoints. In all experiments, participants judged whether two novel, multi-part objects were structurally identical, and they were to ignore any viewpoint or motion differences between the objects. In experiment 1, participants were affected by viewpoint differences more for structurally similar than structurally distinct objects, but this interaction was mitigated by rigid motion. In experiments 2 and 3, we used only structurally similar objects that moved only some of their parts, either in a similar way between objects within a pair or in distinctive ways. Participants' recognition performance was facilitated by this articulated motion relative to both static and scrambled controls. We conclude that coherent motion facilitates generalisation across different views of dynamic objects under some conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it