The relative weight of shape and non-rigid motion cues in object perception: A model of the parameters underlying dynamic object discrimination
Bibliographic record
Abstract
Shape and motion are two dominant cues for object recognition, but it can be difficult to investigate their relative quantitative contribution to the recognition process. In the present study, we combined shape and non-rigid motion morphing to investigate the relative contributions of both types of cues to the discrimination of dynamic objects. In Experiment 1, we validated a novel parameter-based motion morphing technique using a single-part three-dimensional object. We then combined shape morphing with the novel motion morphing technique to pairs of multipart objects to create a joint shape and motion similarity space. In Experiment 2, participants were shown pairs of morphed objects from this space and responded "same" on the basis of motion-only, shape-only, or both cues. Both cue types influenced judgments: When responding to only one cue, the other cue could be ignored, although shape cues were more difficult to ignore. When responding on the basis of both cues, there was an overall bias to weight shape cues more than motion cues. Overall, our results suggest that shape influences discrimination more than motion even when both cue types have been made quantitatively equivalent in terms of their individual discriminability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".