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Record W4408096624 · doi:10.1007/s11145-025-10640-0

Relationship between implicit learning and early English reading skills is mediated by morphological awareness

2025· article· en· W4408096624 on OpenAlexaff
Fun Lau, Xin Ru Toh, Jia Hoong Ong, Gigi Luk, Francis C. K. Wong, Alice H. D. Chan

Bibliographic record

VenueReading and Writing · 2025
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcGill University
FundersMinistry of Education - Singapore
KeywordsPsycholinguisticsReading (process)PsychologyMetacognitionLiteracyCognitive psychologyDevelopmental psychologyPhonological awarenessLinguisticsPedagogyCognitionNeuroscience

Abstract

fetched live from OpenAlex

This study examines the role of implicit learning – the ability to detect regularities in the environment without conscious effort – in English reading during the early stages of reading development. Previous investigations into the relationship between implicit learning and reading have yielded mixed findings, and some have raised the notion that previous implicit learning tasks are unidimensional, and/or that the relationship between implicit learning and reading may be mediated by an intermediary variable, such as a metalinguistic skill. To evaluate the predictions of these theories, an implicit learning task comprising four subtests (old-new, cooccurrence, position, generalisation) was administered to 82 first-grade children along with a battery of language and cognitive measures. Significant correlations were observed among the accuracies in the generalisation subtest in the implicit learning task, English word reading, and English morpheme discrimination. Mediation analysis revealed that the relationship between generalisation and English word reading was fully mediated by morpheme discrimination. These findings underscore the multifaceted nature of implicit learning, and provide insights into the potential mechanism through which implicit learning contributes to reading development.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.330
Teacher spread0.308 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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