Multisensory deficits in dyslexia may result from a locus coeruleus attentional network dysfunction
Bibliographic record
Abstract
A fundamental educational requirement of beginning reading is to learn, access, and rapidly process associations between novel visuospatial symbols and their phonological representations in speech. Children with difficulties in such cross-modal integration are often divided into dyslexia subtypes, based on whether their primary problem is with the written or spoken component of decoding. The present review suggests that starting in infancy, perceptions of audiovisual speech are integrated by mutual oscillatory phase-resetting between sensory cortices, and throughout development visual and auditory experiences are coupled into unified perceptions. Entirely separate subtypes are incompatible with this view. Visual or auditory deficits will invariably affect processing to some degree in both domains. It is suggested that poor auditory/visual integration may be diagnostic for both forms of dyslexia, stemming from an encoding weakness in the early cross-sensory binding of audiovisual speech. The review presents a model of dyslexia as a dysfunction of the large-scale ventral and dorsal attention networks controlling such binding. Excessive glutamatergic neuronal excitability of the attention networks by the Locus coeruleus-norepinephrine system may interfere with multisensory integration, with deleterious effects on the acquisition of reading by degrading graphene/phoneme conversion.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".