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Record W4416070350 · doi:10.1073/pnas.2535704123

Reading ability in both deaf and hearing adults is linked to neural representations of abstract phonology derived from visual speech

2025· preprint· en· W4416070350 on OpenAlexaff
Samuel Evans, C.J. Price, Jörn Diedrichsen, Tae Twomey, Indie Beedie, Maggie R Fraser, Mairéad MacSweeney

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2025
Typepreprint
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsWestern University
FundersWellcome TrustWellcome
KeywordsSpeechreadingPhonologyReading (process)Spoken languageSign languagePhonological awarenessLearning to readSpeech perceptionCued speech

Abstract

fetched live from OpenAlex

Abstract Reading is central to academic and vocational success. Some deaf children face reading challenges due to limited access to spoken or signed language. Robust phonological representations are key to reading development in hearing children. Spoken language phonology may be one of many contributors to reading development in deaf children. Indeed, speechreading ability correlates with reading skill in both deaf and hearing individuals, suggesting it is linked to reading development regardless of hearing status. Further support for this hypothesis would be provided by evidence that similar neural representations of speech phonology are evoked by visual speech and other language forms (auditory speech and text), and that these neural representations are related to reading proficiency. We used fMRI and Representational Similarity Analysis (RSA) to identify shared neural representations of spoken language phonological structure. A group of deaf adult participants (N=22), with a mixture of sign language and spoken language backgrounds and reading abilities, were presented with single lexical items as visual speech and dynamic text (cursive text, revealed letter-by-letter to promote a phonological reading strategy). Adult hearing participants (N=25) were presented with the same words, but as visual speech and auditory speech. Shared neural representations of phonological structure of English words were found in each group in the superior and middle temporal cortex (STC/MTC) and these abstract representations were more similar across different language forms in better readers. Our data provide neurobiological evidence of the contribution of visual speech to abstract phonological representations of spoken language, that relate to reading proficiency, in both deaf and hearing adults. Significance Statement Reading is an essential skill, yet some deaf children face reading challenges due to reduced access to signed or spoken language. In hearing children, successful reading depends on abstract phonological representations, but whether spoken language phonology relates to reading in deaf individuals remains unclear. Using fMRI and RSA, we show that deaf and hearing adults recruit neural representations of phonology that are shared by visual speech and other language forms (visual/auditory speech in hearing; visual speech/dynamic text in deaf) in the superior and middle temporal cortex. Critically, greater cross-modal alignment of neural representations of phonological structure was associated with better reading in both groups. These findings provide neurobiological evidence that visual speech contributes to phonological representations that relate to reading, regardless of hearing status.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.083
GPT teacher head0.414
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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