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Record W4387311041 · doi:10.1075/ml.21014.cos

Long-lag repetition priming in natural text reading

2023· article· en· W4387311041 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Mental Lexicon · 2023
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPriming (agriculture)Reading (process)Context (archaeology)Identity (music)Repetition primingGeneralizability theoryLinguisticsRepetition (rhetorical device)PsychologyComputer scienceCognitive psychologyEye movementCommunicationArtificial intelligenceLexical decision taskBiologyNeuroscienceCognition

Abstract

fetched live from OpenAlex

Abstract Most of the empirical evidence that lays the ground for research on recognition of printed morphologically complex words comes from experimental paradigms employing morphological priming, e.g., exposure to morphologically related forms. Furthermore, most of these paradigms rely on context-less presentation of isolated words. We examined whether well-established morphological priming effects (i.e., faster recognition of a word preceded by a morphologically related word) are observable under more natural conditions of fluent text reading. Using the GECO database of eye-movements recorded during the reading of a novel, we examined the long-lag morphological and identity priming in one’s first language (L1, English and Dutch) or second language (L2, English). While the effects of identity priming were ubiquitous, no evidence of morphological priming was observed in the L1 or L2 eye-movement record. We discuss implications of these findings for ecological validity and generalizability of select current theories of morphological processing.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.002

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 head0.025
GPT teacher head0.322
Teacher spread0.298 · 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