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Record W3095015863 · doi:10.1075/ml.19011.par

Is inhibition involved in the processing of opaque compound words?

2020· article· en· W3095015863 on OpenAlexaff
Juana Park, Faria Sana, Christina L. Gagné, Thomas L. Spalding

Bibliographic record

VenueThe Mental Lexicon · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOpacityPerspective (graphical)ComprehensionCompoundStroop effectPsychologyNatural language processingTest (biology)Cognitive psychologyComputer scienceArtificial intelligenceCognitionBiologyBotanyPhysics

Abstract

fetched live from OpenAlex

Abstract We examined whether inhibition skills were recruited during the processing of compound words. Using an individual differences perspective, we analyzed whether participants’ scores on the Stroop test predicted performance on lexical decision tasks involving compound words varying in their level of semantic opacity. The results show that inhibition is involved in the comprehension of fully opaque (e.g., hogwash ) and fully transparent (e.g., blueberry ) compound words, but we found no evidence for such an effect in the comprehension of partially opaque compound words (e.g., strawberry , jailbird ).

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.194

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)0.0000.000

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 head0.054
GPT teacher head0.323
Teacher spread0.269 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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