fMRI reveals greater within‐ than between‐hemifield integration in the human lateral occipital cortex
Bibliographic record
Abstract
Early visual areas within each hemisphere (V1, V2, V3/VP, V4v) contain distinct representations of the upper and lower quadrants of the contralateral hemifield. As receptive field size increases, the retinotopy in higher-tier visual areas becomes progressively less distinct. Using functional magnetic resonance imaging (fMRI) to map the visual fields, we found that an intermediate level visual area, the lateral occipital region (LO), contains retinotopic maps with a contralateral bias, but with a combined representation of the upper and lower visual field. Moreover, we used the technique of fMRI adaptation to determine whether neurons in LO code for both the upper and lower contralateral quadrants. We found that even when visual stimulus locations are equivalent across comparisons, the LO was more sensitive to location changes that crossed hemifields than location changes within a hemifield. These results suggested that within high-tier visual areas the increasing integration of visual field information is a two-stage process. The upper and lower visual representations are combined first, in LO, then the left and right representations. Furthermore, these results provided evidence for a neural mechanism to explain behavioral findings of greater integration within than between hemifields.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".