Structural Similarity and Spatiotemporal Noise Effects on Learning Dynamic Novel Objects
Bibliographic record
Abstract
The spatiotemporal pattern projected by a moving object is specific to that object, as it depends on both the shape and the dynamics of the object. Previous research has shown that observers learn to make use of this spatiotemporal signature to recognize dynamic faces and objects. In two experiments, we assessed the extent to which the structural similarity of the objects and the presence of spatiotemporal noise affect how these signatures are learned and subsequently used in recognition. Observers first learned to identify novel, structurally distinctive or structurally similar objects that rotated with a particular motion. At test, each learned object moved with its studied motion or with a non-studied motion. In the non-studied motion condition we manipulated either dynamic information alone (experiment 1) or both static and dynamic information (experiment 2). Across both experiments we found that changing the learned motion of an object impaired recognition performance when 3-D shape was similar or when the visual input was noisy during learning. These results are consistent with the hypothesis that observers use learned spatiotemporal signatures and that such information becomes progressively more important as shape information becomes less reliable.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".