When the Learning Gets Tough: Children's Accent-Based Learning Choices are Influenced by Processing Difficulty
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".