Global shape processing involves feature-selective and feature-agnostic coding mechanisms
Bibliographic record
Abstract
Recent research and modeling proposes that a closed shape is accurately described by both the curvature and angular location of its parts relative to the shape center, implying that the shape is coded along with its overall orientation. We tested this proposition. Radial frequency (RF) patterns were employed as stimuli as they can represent a range of familiar closed shapes and are processed globally. We measured a RF amplitude aftereffect (RFAAE) as a function of the shape orientation difference between adapt and test patterns of the same RF. For RF3 and RF4, RFAAEs were largest when adapt and test patterns were the same orientation, and then linearly decreased as the adaptor was rotated away from the test. RFAAEs did not, however, reach zero, instead plateauing significantly above zero. On the other hand, when adapt and test were of opposite luminance polarity, RFAAEs, although lower than same luminance-polarity RFAAEs, were invariant to differences between adapt and test orientations. Our findings provide evidence for two global shape mechanisms: one that is selective for shape orientation and luminance polarity, and one that is agnostic to these characteristics.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".