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Multisensory deficits in dyslexia may result from a locus coeruleus attentional network dysfunction

2021· review· en· W3199858803 on OpenAlexaff
John R. Kershner

Bibliographic record

VenueNeuropsychologia · 2021
Typereview
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyDyslexiaMultisensory integrationNeuroscienceSensory systemCognitive psychologyPerceptionSpeech perceptionVisual perceptionReading (process)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.010

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.

Opus teacher head0.155
GPT teacher head0.417
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

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