Wayfinding in the blind: larger hippocampal volume and supranormal spatial navigation
Bibliographic record
Abstract
In the absence of visual input, the question arises as to how complex spatial abilities develop and how the brain adapts to the absence of this modality. We explored navigational skills in both early and late blind individuals and structural differences in the hippocampus, a brain region well known to be involved in spatial processing. Thirty-eight participants were divided into three groups: early blind individuals (n = 12; loss of vision before 5 years of age; mean age 33.8 years), late blind individuals (n = 7; loss of vision after 14 years of age; mean age 39.9 years) and 19 sighted, blindfolded matched controls. Subjects undertook route learning and pointing tasks in a maze and a spatial layout task. Anatomical data was collected by MRI. Remarkably, we not only show that blind individuals possess superior navigational skills than controls on the route learning task, but we also show for the first time a significant volume increase of the hippocampus in blind individuals [F(1,36) = 6.314; P < or = 0.01; blind: mean = 4237.00 mm(3), SE = 107.53; sighted: mean = 3905.74 mm(3), SE = 76.27], irrespective of whether their blindness was congenital or acquired. Overall, our results shed new light not only on the construction of spatial concepts and the non-necessity of vision for its proper development, but also on the hippocampal plasticity observed in adult blind individuals who have to navigate in this space.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".