Bibliographic record
Abstract
Bats and dolphins are known for their ability to use echolocation. They emit bursts of sounds and listen to the echoes that bounce back to detect the objects in their environment. What is not as well-known is that some blind people have learned to do the same thing, making mouth clicks, for example, and using the returning echoes from those clicks to sense obstacles and objects of interest in their surroundings. The current review explores some of the research that has examined human echolocation and the changes that have been observed in the brains of echolocation experts. We also discuss potential applications and assistive technology based on echolocation. Blind echolocation experts can sense small differences in the location of objects, differentiate between objects of various sizes and shapes, and even between objects made of different materials, just by listening to the reflected echoes from mouth clicks. It is clear that echolocation may enable some blind people to do things that are otherwise thought to be impossible without vision, potentially providing them with a high degree of independence in their daily lives and demonstrating that echolocation can serve as an effective mobility strategy in the blind. Neuroimaging has shown that the processing of echoes activates brain regions in blind echolocators that would normally support vision in the sighted brain, and that the patterns of these activations are modulated by the information carried by the echoes. This work is shedding new light on just how plastic the human brain is. WIREs Cogn Sci 2016, 7:382-393. doi: 10.1002/wcs.1408 For further resources related to this article, please visit the WIREs website.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".