MétaCan
Menu
Back to cohort
Record W2799273115 · doi:10.1080/23273798.2018.1470250

Pseudo-morphemic structure inhibits, but morphemic structure facilitates, processing of a repeated free morpheme

2018· article· en· W2799273115 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLanguage Cognition and Neuroscience · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMorphemeFacilitationComputer sciencePrime (order theory)Syllabic verseWord (group theory)Natural language processingArtificial intelligenceSpeech recognitionCommunicationLinguisticsMathematicsBiologyPsychologyCombinatoricsNeuroscience

Abstract

fetched live from OpenAlex

Five experiments examined whether words with embedded morphemes are automatically morphologically parsed, even when doing so does not reflect the actual morphological structure. We found that the presence of an embedded morpheme in a word affects the subsequent processing of those embedded morphemes and that the effect depends on a mixture of facilitation due to the orthographic overlap and inhibition that depends on whether the target functions morphologically in the prime. Exposure to a word in which the target (e.g. car) does not function as a morpheme (e.g. carpet) made it more difficult (relative to an unrelated prime) to identify that target as a word, whereas exposure to a word in which the target was a productive morpheme (e.g. hogwash) made it easier to process the target (e.g. hog), and that these effects cannot be reduced to semantic, orthographic, phonological, or syllabic overlap.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.271
Teacher spread0.250 · 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