Modularity and saccade influence in the cortical vision network: an fMRI / graph theory approach
Bibliographic record
Abstract
Considerable regional evidence has accumulated for dorsal-ventral modularity in the visual system, but it is not clear how these modules function at the whole brain network level, or how these networks are influenced by naturally occurring saccades. We addressed these questions using graph theory analysis of fMRI data collected during a task where participants had to remember, then discriminate between two different object features. Seventeen participants judged whether a remembered object changed shape or orientation with or without an intervening saccade. BOLD activation from 50 cortical nodes was used to identify local and global network properties, which indicated greater interconnectivity and efficiency of information transfer during saccades. A network modularity analysis revealed three sub-networks during fixation: a bilateral dorsal sub-network linking areas involved in visuospatial processing and two lateralized ventral sub-networks linking areas involved in object feature processing. Importantly, when horizontal saccades across the remembered object required visual comparisons between hemifields, the two lateralized ventral sub-networks became functionally integrated into a single bilateral sub-network. Comparisons of betweenness centrality between conditions identified several significant hub regions in occipital, parietal, and frontal cortex involved in linking distant network nodes during saccades. These results provide support of a ventral and dorsal stream distinction in human perception and show how hemispheric sub-networks are modified to functionally integrate information across saccades.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".