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Record W7154602533 · doi:10.48448/pmxq-6917

Iconic Meanings Are Learned Earlier: Homophones Provide Insight on Iconicity's Role in the Acquisition of Words

2025· other· W7154602533 on OpenAlexaff
Cognitive Science Society 2025, Laura Aguanno, David Sidhu

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsCarleton University
Fundersnot available
KeywordsIconicityHomophoneWord (group theory)Sound symbolismSign (mathematics)Semantics (computer science)OnomatopoeiaFactor (programming language)

Abstract

fetched live from OpenAlex

Iconic words are those whose sounds share properties in common with their referents, such as “clatter” or “hiccup.” Research shows that children learn iconic words earlier than arbitrary words and that iconicity may help children form these connections. However, another factor to consider is that iconic words have forms that are easier to produce. To gain further insight into the link between iconicity and acquisition we studied homophones. This allowed us to hold the form of each word constant and examine whether iconic meanings are acquired earlier. Participants provided iconicity ratings on 1668 total meanings for 390 word forms. We ran a mixed effects linear regression and found an effect of iconicity on test-based age-of-acquisition, controlling for word form, length, frequency, phonological neighbourhood, and meaning-specific familiarity. These findings suggest that children learn iconic meanings earlier than arbitrary ones and support iconicity as an important factor in word-learning.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0070.014
Science and technology studies0.0010.006
Scholarly communication0.0010.001
Open science0.0070.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.003

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 head0.280
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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