Visual Cortical Representation of Whole Words and Hemifield-split Word Parts
Bibliographic record
Abstract
Reading requires the neural integration of visual word form information that is split between our retinal hemifields. We examined multiple visual cortical areas involved in this process by measuring fMRI responses while observers viewed words that changed or repeated in one or both hemifields. We were specifically interested in identifying brain areas that exhibit decreased fMRI responses as a result of repeated versus changing visual word form information in each visual hemifield. Our method yielded highly significant effects of word repetition in a previously reported visual word form area (VWFA) in occipitotemporal cortex, which represents hemifield-split words as whole units. We also identified a more posterior occipital word form area (OWFA), which represents word form information in the right and left hemifields independently and is thus both functionally and anatomically distinct from the VWFA. Both the VWFA and the OWFA were left-lateralized in our study and strikingly symmetric in anatomical location relative to known face-selective visual cortical areas in the right hemisphere. Our findings are consistent with the observation that category-selective visual areas come in pairs and support the view that neural mechanisms in left visual cortex--especially those that evolved to support the visual processing of faces--are developmentally malleable and become incorporated into a left-lateralized visual word form network that supports rapid word recognition and reading.
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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) | 0.002 | 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".