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Record W3083572728 · doi:10.1017/s0305000920000422

Cumulative semantic interference across unrelated responses in school-age children's picture naming

2020· article· en· W3083572728 on OpenAlexaff
Monique Charest, Tieghan Baird

Bibliographic record

VenueJournal of Child Language · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyPhenomenonSemantics (computer science)Developmental psychologyLinguisticsCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

Naming semantically related images results in progressively slower responses as more images are named. There is considerable documentation in adults of this phenomenon, known as cumulative semantic interference. Few studies have focused on this phenomenon in children. The present research investigated cumulative semantic interference effects in school-aged children. In Study 1, children named a series of contiguous, semantically related pictures. The results revealed no cumulative interference effects. Study 2 utilized an approach more closely aligned with adult methods, incorporating intervening, unrelated items intermixed with semantically related items within a continuous list. Study 2 showed a linear increase in reaction time as a function of ordinal position within semantic sets. These findings demonstrate cumulative semantic interference effects in young, school-aged children that are consistent with experience-driven changes in the connections that underlie lexical access. They invite further investigation of how children's lexical representation and processing are shaped by speaking experiences.

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)0.004
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 score0.132
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.312
Teacher spread0.288 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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