Central Auditory System and Central Auditory Processing Disorders: Some Conceptual Issues
Bibliographic record
Abstract
The central auditory system has both parallel and hierarchical afferent architectures. In the frequency domain, it is tonotopically constrained, and in the spatial domain, it is dominated by a representation of the contralateral acoustic hemifield. The functions supported by the afferent pathways can be somewhat overlapping, and the connectivity among the pathways is to some degree plastic. Partial deafferentation (in the form of high-frequency hearing loss) and behavioral experience are capable of causing alterations in tonotopic maps in the more rostral auditory system, even in adult animals. Central auditory processing is often frequency-specific. The temporal processes needed for normal auditory function are diverse, which is to be expected given the heterogeneous ways in which auditory events are disposed in time and encoded neurally. Central auditory pathologies need not respect structural or functional boundaries in the brain, and so should be expected to have idiosyncratic presentations. Management strategies based on auditory training may exploit basic neuroplasticity, but more evidence is needed to substantiate any hypothesis of their differential efficacy in remediation of central auditory processing disorders or language and reading problems. KEYWORDS Auditory neuroscience - neural representation - neural plasticity - temporal processing - perceptual architecture - auditory training
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.040 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".