Interhemispheric connectivity during lateralized lexical decision
Bibliographic record
Abstract
The well-established right visual field (RVF-lh) advantage in word recognition is commonly attributed to the typical left hemisphere dominance in language; words presented to the LVF-rh are processed less efficiently due to the need for transcallosal transfer from the right to left hemisphere. The exact stage for this hemispheric transfer is currently unsettled. Some studies suggest that transfer occurs at very early stages between primary visual regions, whereas other studies suggest that transfer occurs between the left visual word form area and its right hemisphere homolog. This study explores these conflicting accounts and finds evidence for both. Participants conducted a lateralized lexical decision task with both unilateral and bilateral display conditions. Connectivity analyses were conducted from magnetoencephalography signals that were localized to the left middle occipital gyrus (LMOG), right middle occipital gyrus (RMOG), left visual word form area (LVWFA), and right visual word form area (RVWA). Results from unilateral trials showed asymmetrical interhemispheric connectivity from the RMOG to LMOG and symmetrical interhemispheric connectivity between the LVWFA and RVWFA. Furthermore, bilateral presentations led to reduced interhemispheric connectivity between both homologous region of interest pairs. Together, these results suggest that lateralized word recognition involves multiple stages of interhemispheric interactions and that these interactions are reduced with bilateral displays.
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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 | 0.000 | 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".