Neural Correlates of Natural Human Echolocation in Early and Late Blind Echolocation Experts
Bibliographic record
Abstract
BACKGROUND: A small number of blind people are adept at echolocating silent objects simply by producing mouth clicks and listening to the returning echoes. Yet the neural architecture underlying this type of aid-free human echolocation has not been investigated. To tackle this question, we recruited echolocation experts, one early- and one late-blind, and measured functional brain activity in each of them while they listened to their own echolocation sounds. RESULTS: When we compared brain activity for sounds that contained both clicks and the returning echoes with brain activity for control sounds that did not contain the echoes, but were otherwise acoustically matched, we found activity in calcarine cortex in both individuals. Importantly, for the same comparison, we did not observe a difference in activity in auditory cortex. In the early-blind, but not the late-blind participant, we also found that the calcarine activity was greater for echoes reflected from surfaces located in contralateral space. Finally, in both individuals, we found activation in middle temporal and nearby cortical regions when they listened to echoes reflected from moving targets. CONCLUSIONS: These findings suggest that processing of click-echoes recruits brain regions typically devoted to vision rather than audition in both early and late blind echolocation experts.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".