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Record W2607583961 · doi:10.1037/xlm0000411

Surviving blind decomposition: A distributional analysis of the time-course of complex word recognition.

2017· article· en· W2607583961 on OpenAlexafffund
Daniel Schmidtke, Kazunaga Matsuki, Victor Kuperman

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcMaster University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentMcMaster UniversityNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research and InnovationSocial Sciences and Humanities Research Council of CanadaNational Institutes of Health
KeywordsLexical decision taskWord recognitionMorphemeLinguisticsPsychologyMeaning (existential)Computer scienceArtificial intelligenceCognitive psychologyNatural language processingReading (process)Cognition

Abstract

fetched live from OpenAlex

). Furthermore, for derived word processing across lexical decision and eye-tracking paradigms (Experiments 1-2; 5-7), semantic effects emerge early in the time-course of word recognition, and their effects either precede or emerge simultaneously with morphological effects. These results are not consistent with the premises of the form-then-meaning view of complex word recognition, but are convergent with a form-and-meaning account of complex word recognition. (PsycINFO Database Record

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.359

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 studies0.0000.001
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 head0.071
GPT teacher head0.394
Teacher spread0.323 · 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 designBench or experimental
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

Citations54
Published2017
Admission routes2
Has abstractyes

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