What and How the Deaf Brain Sees
Bibliographic record
Abstract
Over the past decade, there has been an unprecedented level of interest and progress into understanding visual processing in the brain of the deaf. Specifically, when the brain is deprived of input from one sensory modality (such as hearing), it often compensates with supranormal performance in one or more of the intact sensory systems (such as vision). Recent psychophysical, functional imaging, and reversible deactivation studies have converged to define the specific visual abilities that are enhanced in the deaf, as well as the cortical loci that undergo crossmodal plasticity in the deaf and are responsible for mediating these superior visual functions. Examination of these investigations reveals that central visual functions, such as object and facial discrimination, and peripheral visual functions, such as motion detection, visual localization, visuomotor synchronization, and Vernier acuity (measured in the periphery), are specifically enhanced in the deaf, compared with hearing participants. Furthermore, the cortical loci identified to mediate these functions reside in deaf auditory cortex: BA 41, BA 42, and BA 22, in addition to the rostral area, planum temporale, Te3, and temporal voice area in humans; primary auditory cortex, anterior auditory field, dorsal zone of auditory cortex, auditory field of the anterior ectosylvian sulcus, and posterior auditory field in cats; and primary auditory cortex and anterior auditory field in both ferrets and mice. Overall, the findings from these studies show that crossmodal reorganization in auditory cortex of the deaf is responsible for the superior visual abilities of the deaf.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".