Asymmetric interference between the perception of shape and the perception of surface properties
Bibliographic record
Abstract
We previously showed that the processing of shape and the processing of surface properties linked to material properties engage different regions of the ventral stream (J. S. Cant & M. A. Goodale, 2007). Moreover, we recently used Garner's speeded-classification task to show that varying the surface (material) properties of objects does not interfere with shape judgments and vice versa (J. S. Cant, M. E. Large, L. McCall, & M. A. Goodale, 2008). In the present study, we looked at Garner interference when surface cues contributed to the perception of object shape and hypothesized that this would interfere with judgments about the width and the length of the objects. In contrast, we predicted that varying the width and the length of the objects would not interfere with surface-property judgments. This is precisely what we found. These results suggest that the shape and the surface properties of an object cannot be processed independently when both these sets of cues are linked to the perception of the object's overall shape. These observations, together with our previous findings, suggest that the surface cues that contribute to object shape are processed quite separately from the surface cues that are linked to an object's material properties.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".