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Record W2129162148 · doi:10.1037/xlm0000090

The locus of serial processing in reading aloud: Orthography–to–phonology computation or speech planning?

2014· article· en· W2129162148 on OpenAlexaff
Petroula Mousikou, Kathleen Rastle, Derek Besner, Max Coltheart

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Waterloo
FundersEconomic and Social Research Council
KeywordsPhonologyOrthographyConnectionismComputer scienceDual (grammatical number)Reading (process)Priming (agriculture)Phonological ruleReading aloudLinguisticsPsychologySpeech recognitionCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Dual-route theories of reading posit that a sublexical reading mechanism that operates serially and from left to right is involved in the orthography-to-phonology computation. These theories attribute the masked onset priming effect (MOPE) and the phonological Stroop effect (PSE) to the serial left-to-right operation of this mechanism. However, both effects may arise during speech planning, in the phonological encoding process, which also occurs serially and from left to right. In the present paper, we sought to determine the locus of serial processing in reading aloud by testing the contrasting predictions that the dual-route and speech planning accounts make in relation to the MOPE and the PSE. The results from three experiments that used the MOPE and the PSE paradigms in English are inconsistent with the idea that these effects arise during speech planning, and consistent with the claim that a sublexical serially operating reading mechanism is involved in the print-to-sound translation. Simulations of the empirical data on the MOPE with the dual route cascaded (DRC) and connectionist dual process (CDP++) models, which are computational implementations of the dual-route theory of reading, provide further support for the dual-route account.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.026
GPT teacher head0.371
Teacher spread0.345 · 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 designOther design
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

Citations8
Published2014
Admission routes1
Has abstractyes

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