Home Language Experience Shapes Which Skills Are Used during Unfamiliar Speech Processing
Bibliographic record
Abstract
Speech mixed with noise and speech that is of an unfamiliar variety both make the task of understanding more difficult. Children are often more negatively affected by these situations than adults. Numerous studies have examined the cognitive and linguistic skills that support spoken language processing. In the current study, we examine the contribution of linguistic exposure and various cognitive and linguistic skills for spoken word recognition of an unfamiliar variety of speech (German-accented English). The Ease of Language Understanding model predicts that working memory skills are needed in the most difficult listening situations. Two groups of school-age children were drawn from a larger sample: those with exposure to multiple languages in the home and those exposed to only English in the home. As predicted, working memory skills predicted performance for children with less varied linguistic experience (those only exposed to English in the home), but not for children with varied linguistic exposure. In contrast, linguistic skills predicted performance for children with more varied linguistic experience, even though the two groups did not differ overall in any of the assessed skills. These findings support the Ease of Language Understanding model of language processing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".