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Record W2161108365 · doi:10.1037/0278-7393.34.2.422

Reading aloud: Spelling-sound translation uses central attention.

2008· article· en· W2161108365 on OpenAlexafffund
Shannon O’Malley, Michael Reynolds, Jennifer A. Stolz, Derek Besner

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2008
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsTrent UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyReading aloudStimulus onset asynchronyLexiconRepetition (rhetorical device)Cognitive psychologySpellingOrthographyLinguisticsCognitionReading (process)

Abstract

fetched live from OpenAlex

Contrary to the received view that reading aloud reflects processes that are "automatic," recent evidence suggests that some of these processes require a form of attention. This issue was investigated further by examining the effect of a prior presentation of exception words (words whose spelling-sound translation are atypical, such as pint as compared with mint, hint, or lint) and pseudohomophones (nonwords that sound identical to words, such as brane from brain) on reading aloud in the context of the psychological refractory period paradigm. For exception words, the joint effects of repetition and stimulus onset asynchrony (SOA) yielded an underadditive interaction on the time to read aloud, replicating previous work -- a short SOA between Task 1 and Task 2 increased reaction time (RT) and reduced the magnitude of the repetition effect relative to the long SOA. For pseudohomophones, in contrast, the joint effects of repetition and SOA were additive on RT. These results provide converging evidence for the conclusion that (a) processing up to and including the orthographic input lexicon does not require central attention when reading aloud, whereas (b) translating lexical and sublexical spelling to sound requires the use of central attention.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.056
GPT teacher head0.351
Teacher spread0.295 · 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

Citations27
Published2008
Admission routes2
Has abstractyes

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