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Record W4307386081 · doi:10.1075/ml.21010.cru

Is meaning construction attempted during the processing of pseudo-compounds?

2022· article· en· W4307386081 on OpenAlexaff
Karen Pérez Cruz, Chelsa Patel, Jazlynn Steinbach, Mohamed Barre, Holly Kibbins, Dixie Wong, Alexander Taikh, Christina L. Gagné, Thomas L. Spalding

Bibliographic record

VenueThe Mental Lexicon · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMorphemeCompoundMeaning (existential)Natural language processingPrime (order theory)LinguisticsComputer scienceSet (abstract data type)Artificial intelligencePsychologyMathematicsPhilosophyCombinatorics

Abstract

fetched live from OpenAlex

Abstract Psycholinguists have yet to reach a consensus on what role constituent morphemes play in the processing of compound words, although some recent work suggests that morphemes are activated obligatorily during processing. In the current study, we investigate whether people use morphemes to attempt meaning construction even for pseudo-compounds which are words that appear to have a compound structure, but in fact do not (e.g., carpet is not car + pet ). We obtained relational entropies (a measure of potential relational competition) for a set of pseudo-compound words based on responses from a possible relations task. The relational entropy values as well as frequency of the prime (e.g., carpet ) and target (e.g., car ) were then used to predict the processing of the pseudo-first constituents after exposure to the pseudo-compound masked primes. We observed a significant three-way interaction between entropy, target frequency, and prime frequency. Our results suggest that meaning construction is attempted for pseudo-compound words.

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 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 score0.550
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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.

Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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