Occipital Cortical Thickness Predicts Performance on Pitch and Musical Tasks in Blind Individuals
Bibliographic record
Abstract
The behavioral and neurofunctional consequences of blindness often include performance enhancements and recruitment of occipital regions for nonvisual tasks. How the neuroanatomical changes resulting from this sensory loss relate to these functional changes is, however, less clear. Previous studies using cortical thickness (CT) measures have shown thicker occipital cortex in early-blind (EB) individuals compared with sighted controls. We hypothesized that this finding reflects the crossmodal plasticity often observed in blind individuals and thus could reflect behavioral adaptations. To address this issue, CT measures in blind (early and late) and sighted subjects were obtained along with several auditory behavioral measures in an attempt to relate behavioral and neuroanatomical changes. Group contrasts confirmed previous results in showing thicker occipital cortex in the EB. Regression analyses between CT measures across the whole brain of all blind individuals with the behavioral scores from 2 tasks in which EB subjects were superior (pitch and melody discrimination) showed that CT of occipital areas was directly related to behavioral enhancements. These findings constitute a compelling demonstration that anatomical changes in occipital areas are directly related to heightened behavioral abilities in the blind and hence support the idea that these anatomical features reflect adaptive compensatory plasticity.
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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 | 0.001 | 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) | 0.003 | 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".