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Record W4386438876 · doi:10.1037/xlm0001289

An examination of models of reading multi-morphemic and pseudo multi-morphemic words using sandwich priming.

2023· article· en· W4386438876 on OpenAlexafffund
Stephen J. Lupker, Giacomo Spinelli

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPriming (agriculture)MorphemeLexical decision taskRepetition primingAffixNatural language processingDissociation (chemistry)PsychologyComputer scienceArtificial intelligenceMathematicsLinguisticsCognitive psychologyChemistryCognition

Abstract

fetched live from OpenAlex

Rastle et al. (2004) reported that true (e.g., walker) and pseudo (e.g., corner) multi-morphemic words prime their stem words more than form controls do (e.g., brothel priming BROTH) in a masked priming lexical decision task. This data pattern has led a number of models to propose that both of the former word types are "decomposed" into their stem (e.g., walk, corn) and affix (e.g., -er) early in the reading process. The present experiments were designed to examine the models proposed to explain Rastle et al.'s effect, including models not assuming a decomposition process, using a more sensitive priming technique, sandwich priming (Lupker & Davis, 2009). Experiment 1, using the conventional masked priming procedure, replicated Rastle et al.'s results. Experiments 2 and 3, involving sandwich priming procedures, showed a clear dissociation between priming effects for true versus pseudo multi-morphemic words, results that are not easily explained by any of the current models. Nonetheless, the overall data pattern does appear to be most consistent with there being a decomposition process when reading real and pseudo multi-morphemic words, a process that involves activating (and inhibiting) lexical-level representations including a representation for the affix (e.g., -er), with the ultimate lexical decision being based on the process of resolving the pattern created by the activated representational units. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.000
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 score0.011
Threshold uncertainty score0.551

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)0.0000.000

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 head0.106
GPT teacher head0.389
Teacher spread0.283 · 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
Published2023
Admission routes2
Has abstractyes

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