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Record W2583879242 · doi:10.1017/s0142716416000461

Cumulative semantic interference in young children's picture naming

2017· article· en· W2583879242 on OpenAlexaff
Monique Charest

Bibliographic record

VenueApplied Psycholinguistics · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyHomogeneousRepetition (rhetorical device)Context (archaeology)Interference (communication)Developmental psychologyCognitive psychologyLatency (audio)LinguisticsComputer scienceMathematics

Abstract

fetched live from OpenAlex

ABSTRACT In children and adults, naming an item sometimes interferes with later attempts to name other items. Adult speakers experience cumulative semantic interference, interpreted as the result of incremental learning. Studies to date have not examined whether incremental learning can also account for interference in children. This study examined context effects on picture naming in 3-year-old children, and investigated whether children, like adults, show interference that is semantically based and cumulative. Children named pictures from semantically homogeneous and mixed sets. Response latency, accuracy, and repetition errors were recorded. The results demonstrated a progressive slowing of responses in the semantically homogeneous condition that was greater than that observed for the mixed condition. There were no significant effects for accuracy. Repetition errors, although infrequent, patterned similarly to previous reports for adults. The results indicate that preschool-aged children experience cumulative semantic interference in naming, and suggest that incremental learning may account for interference effects across development.

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)0.002
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 score0.279
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.331
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

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

Citations5
Published2017
Admission routes1
Has abstractyes

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