Is that a <i>pibu</i> or a <i>pibo</i>? Children with reading and language deficits show difficulties in learning and overnight consolidation of phonologically similar pseudowords
Bibliographic record
Abstract
Word learning is critical for the development of reading and language comprehension skills. Although previous studies have indicated that word learning is compromised in children with reading disability (RD) or developmental language disorder (DLD), it is less clear how word learning difficulties manifest in children with comorbid RD and DLD. Furthermore, it is unclear whether word learning deficits in RD or DLD include difficulties with offline consolidation of newly learned words. In the current study, we employed an artificial lexicon learning paradigm with an overnight design to investigate how typically developing (TD) children (N = 25), children with only RD (N = 93), and children with both RD and DLD (N = 34) learned and remembered a set of phonologically similar pseudowords. Results showed that compared to TD children, children with RD exhibited: (i) slower growth in discrimination accuracy for cohort item pairs sharing an onset (e.g. pibu-pibo), but not for rhyming item pairs (e.g. pibu-dibu); and (ii) lower discrimination accuracy for both cohort and rhyme item pairs on Day 2, even when accounting for differences in Day 1 learning. Moreover, children with comorbid RD and DLD showed learning and retention deficits that extended to unrelated item pairs that were phonologically dissimilar (e.g. pibu-tupa), suggestive of broader impairments compared to children with only RD. These findings provide insights into the specific learning deficits underlying RD and DLD and motivate future research concerning how children use phonological similarity to guide the organization of new word knowledge.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".