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Record W2129976542 · doi:10.1037/0278-7393.34.3.495

On the additive effects of stimulus quality and word frequency in lexical decision: Evidence for opposing interactive influences revealed by RT distributional analyses.

2008· article· en· W2129976542 on OpenAlexafffund
Melvin J. Yap, David A. Balota, Chi‐Shing Tse, Derek Besner

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2008
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Waterloo
FundersNational Institute on AgingNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyLexical decision taskStimulus (psychology)Word lists by frequencyCognitive psychologyWord recognitionCognitionLexical accessDimension (graph theory)LinguisticsCommunicationMathematics

Abstract

fetched live from OpenAlex

The joint effects of stimulus quality and word frequency in lexical decision were examined in 4 experiments as a function of nonword type (legal nonwords, e.g., BRONE, vs. pseudohomophones, e.g., BRANE). When familiarity was a viable dimension for word-nonword discrimination, as when legal nonwords were used, additive effects of stimulus quality and word frequency were observed in both means and distributional characteristics of the response-time distributions. In contrast, when the utility of familiarity was undermined by using pseudohomophones, additivity was observed in the means but not in distributional characteristics. Specifically, opposing interactive effects in the underlying distribution were observed, producing apparent additivity in means. These findings are consistent with the suggestion that, when familiarity is deemphasized in lexical decision, cascaded processing between letter and word levels is in play, whereas, when familiarity is a viable dimension for word-nonword discrimination, processing is discrete.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.390

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 studies0.0000.000
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 head0.125
GPT teacher head0.477
Teacher spread0.351 · 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

Citations88
Published2008
Admission routes2
Has abstractyes

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