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Record W4409071198 · doi:10.1075/ml.24035.loo

Long-lag morphological priming and inflectional paradigm size effectsin Estonian and Finnish text reading

2024· article· en· W4409071198 on OpenAlexaff
Kaidi Lõo, Raymond Bertram, Victor Kuperman

Bibliographic record

VenueThe Mental Lexicon · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcMaster UniversityConcordia UniversityUniversity of Windsor
Fundersnot available
KeywordsEstonianReading (process)LagComputer sciencePriming (agriculture)LinguisticsNatural language processingInflectionPsychologyArtificial intelligenceBiologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract Morphological priming and paradigm size effects have been established in single word reading studies, however, morphological priming effects in longer texts have not been observed, and, to the best of our knowledge, paradigmatic effects in text reading have not yet been examined. The current study utilized the Multilingual Eye-Tracking Corpus MECO ( Siegelman et al., 2022 ) to explore paradigmatic and morphological priming effects during text reading in Estonian and Finnish, two morphologically rich Finno-Ugric languages. The results showed clear inflectional paradigm size effects for Estonian during text reading in several eye movement measures, but not for Finnish. This may be linked to the support from the inflectional paradigm being semantically more beneficial to the reader in Estonian than in Finnish. The current study also showed clear long-lag inflectional priming effects in text reading, unlike what was observed in prior studies in Dutch, English, and Spanish. This study is thus the first to show that inflectional priming can extend beyond word or sentence level and suggest that inflectional variants of a particular word in Estonian and Finnish get and remain activated even when text context is present.

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 score0.043
Threshold uncertainty score0.340

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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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