Adaptation to sensory loss
Bibliographic record
Abstract
The human brain has the remarkable ability to adapt to changes in its environment by benefiting from its 'plastic' properties. Following brain injury, the amputation of a limb, or the loss of a sensory input such as peripheral blindness, brain circuitry often seems to be able to reorganize itself in order to compensate for the handicap by being recruited to carry out tasks not associated with their prior 'default' functioning. The purpose of this review is to illustrate the brain's remarkable ability to adapt to changes in its environment, particularly when it is faced with a sensory loss. Two excellent models to study this phenomenon are provided by blind and deaf individuals. In both cases, studies have shown that they appear to compensate for the loss of sensory input with enhanced abilities in their remaining senses. These behavioral modifications are often coupled with changes in cerebral processing, generally in the form of crossmodal recruitment of deaffarented primary and secondary sensory areas. We will also discuss the possible mechanisms underlying these changes and whether the functional topography of these regions present in unimpaired individuals is preserved in blindness and deafness. The notion of a critical period for plastic changes will also be discussed and its importance will be shown to be twofold. On the one hand, the functional relevance of crossmodal processing appears to decrease as a function of the age of onset of the deficiency. On the other hand, the more cortical reorganization takes place, the less likely brain areas will be able to process input from its original sensory modality. This is especially important for deaf individuals as auditory input can now be restored thanks to cochlear implants. Copyright © 2010 John Wiley & Sons, Ltd. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".