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Electrophysiological Indices of Phonological Impairments in Dyslexia

2012· article· en· W1967760860 on OpenAlexafffund
Amy S. Desroches, Randy Lynn Newman, Erin K. Robertson, Marc F. Joanisse

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2012
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsCape Breton UniversityAcadia UniversityUniversity of WinnipegWestern University
FundersCanadian Institutes of Health ResearchScottish Rite Charitable Foundation of Canada
KeywordsDyslexiaPsychologyRhymeN400Cognitive psychologyPhoneticsPerceptionPhonologyEvent-related potentialAudiologyElectroencephalographyReading (process)LinguisticsNeuroscience

Abstract

fetched live from OpenAlex

PURPOSE: A range of studies have shown difficulties in perceiving acoustic and phonetic information in dyslexia; however, much less is known about how such difficulties relate to the perception of individual words. The authors present data from event-related potentials (ERPs) examining the hypothesis that children with dyslexia have difficulties with processing phonemic information within spoken words compared to age-matched readers with typical development. METHOD: The authors monitored ERPs to auditory words during a simple picture-word matching task. The key manipulation was the inclusion of both matching stimuli and three types of mismatches (cohort, CONE-comb; rhyme, CONE-bone; and unrelated, CONE-fox). RESULTS: Children with dyslexia showed atypical N400 ERP waveforms to both types of phonological mismatches, but not to phonologically unrelated mismatches, reflecting a relative insensitivity to phonological overlap among auditory words. CONCLUSION: The data suggest that children with dyslexia have impairments in integrating phonological information into word-level representations. The results suggest that speech perception difficulties in dyslexia might have consequences for processing auditory words.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.079
GPT teacher head0.437
Teacher spread0.358 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2012
Admission routes2
Has abstractyes

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