Keeping it together: The maintenance of figure-ground segregation in the lateral occipital sulcus
Bibliographic record
Abstract
An important aspect of visual processing involves binding the elementary features of an object and segregating them from background features. Previous research demonstrated that fragmented line-drawings of objects could be discriminated from a background of randomly orientated lines on the basis of differences in motion. Furthermore, the percept of the object persists for a second or so after motion has stopped. Functional imaging showed that this persistence was reflected in brain activation in area LO but not in MT+ (Ferber et al, 2003). In the present study a comparison was made between the persistence of forms constructed from motion and those constructed from colour/brightness similarity. In the colour/brightness condition the background was a different colour/brightness from the object, but they moved together. Both motion and colour/brightness percepts produced persistence after the motion or colour/brightness cue was removed. Functional imaging showed a gradual increase in the persistence of brain activity in the early visual areas (V1, V2, VP), which reached significance in V4v and peaked in LO. These results suggest that the binding or grouping of visual elements is accomplished early on in the visual pathway, before V4v, and that the maintenance of grouped visual elements in V4v and LO is independent of cue type.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".