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Record W7154593195 · doi:10.48448/rms7-jd77

When the Learning Gets Tough: Children's Accent-Based Learning Choices are Influenced by Processing Difficulty

2025· other· W7154593195 on OpenAlexaff
Cognitive Science Society 2025, Ashley Avarino, Katherine White

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVariety (cybernetics)Stress (linguistics)Information processingWord (group theory)Speech processingWord learningVariation (astronomy)

Abstract

fetched live from OpenAlex

Children use a variety of cues to decide who they can trust to be a credible source of information. One such cue is accent. Previous research has attributed accent-based preferences to a bias for in-group members. In the present study, we examine another potential contributor to these preferences: processing difficulty. Four- to seven-year-old children completed a selective word-learning task, in which they were presented with pairs of speakers and needed to choose one to learn a new word from. The speakers differed in accent type – native or non-native – and non-native speakers differed in how difficult their speech was to process. Children were more likely to choose to learn from the speaker whose speech was easier to process, and the magnitude of this effect was linearly related to the processing difficulty disparity between the two speakers: the greater the disparity, the stronger the effect. These findings are the first to demonstrate the role of processing difficulty in children’s accent-based selective 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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.007
Science and technology studies0.0120.010
Scholarly communication0.0070.003
Open science0.0090.002
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0030.002

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 head0.013
GPT teacher head0.286
Teacher spread0.273 · 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 designSimulation or modeling
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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