Surface but not volumetric part structure mediates three-dimensional shape representation: Evidence from part–whole priming
Bibliographic record
Abstract
The decomposition of three-dimensional (3-D) objects into shape primitives consisting of geometric volumes is a key proposal of some theories of object recognition. It implicitly assumes that recognition involves volumetric completion--the derivation of a three-dimensional structure that comprises inferred shape properties, such as surfaces, that are not directly visible due to self-occlusion. The goal of this study was to test this claim. In Experiment 1 participants memorized novel objects and then discriminated these from previously unseen objects. Targets were preceded by primes containing a subset of object surfaces that either matched those visible in the whole objects or that could only be inferred through volumetric completion. The results showed performance benefits through priming from visible surfaces but not from inferred surfaces. In Experiment 2, we found equivalent priming for part-primes containing two visible surfaces from the same volumetric part and for primes containing one surface from each of two volumes. These results challenge the view that 3-D object recognition is mediated by shape primitives comprising geometric volumes. Instead, the results support an alternative model that proposes that 3-D shapes are represented as a non-volumetric surface-based structural description.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".