The Right Hand Draws the Trees, But the Left Draws the Forest?
Bibliographic record
Abstract
Spatial processing is lateralized: the right hemisphere is optimized for perceiving global aspects of space (“seeing the forest”), while the left hemisphere specializes in perceiving local aspects of space (“seeing the trees”). However, less is known about how the information is shared across the hemispheres and which areas within the corpus callosum are required for transferring and integrating visuospatial information. Here, we report a 60 year old woman with a mass lesion in the splenium of the corpus callosum who demonstrated visuospatial processing deficits that were out-of-proportion to the rest of her neurological examination. Remarkably, in the Rey-Osterrieth Complex figure task, she copied with her left hand the outlines of the figure (global aspects), whereas with her right hand she drew the details of that figure (local aspects). While hemispheric lesions have demonstrated single dissociations of spatial processing, these results indicate that a lesion in the corpus callosum can produce a double dissociation for high-level spatial tasks, as local and global spatial perception are further dissociated with handedness. Interestingly, as little as the posterior third of the corpus callosum is required for proper visuospatial information transfer and integration, which provides important insight into the interhemispheric functional anatomy that underlies visuospatial perception.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".