A Functional Neuroimaging Study of Sound Localization: Visual Cortex Activity Predicts Performance in Early-Blind Individuals
Bibliographic record
Abstract
Blind individuals often demonstrate enhanced nonvisual perceptual abilities. However, the neural substrate that underlies this improved performance remains to be fully understood. An earlier behavioral study demonstrated that some early-blind people localize sounds more accurately than sighted controls using monaural cues. In order to investigate the neural basis of these behavioral differences in humans, we carried out functional imaging studies using positron emission tomography and a speaker array that permitted pseudo-free-field presentations within the scanner. During binaural sound localization, a sighted control group showed decreased cerebral blood flow in the occipital lobe, which was not seen in early-blind individuals. During monaural sound localization (one ear plugged), the subgroup of early-blind subjects who were behaviorally superior at sound localization displayed two activation foci in the occipital cortex. This effect was not seen in blind persons who did not have superior monaural sound localization abilities, nor in sighted individuals. The degree of activation of one of these foci was strongly correlated with sound localization accuracy across the entire group of blind subjects. The results show that those blind persons who perform better than sighted persons recruit occipital areas to carry out auditory localization under monaural conditions. We therefore conclude that computations carried out in the occipital cortex specifically underlie the enhanced capacity to use monaural cues. Our findings shed light not only on intermodal compensatory mechanisms, but also on individual differences in these mechanisms and on inhibitory patterns that differ between sighted individuals and those deprived of vision early in life.
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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 | 0.001 | 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) | 0.001 | 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".