Language acquisition of early sequentially bilingual children is moderated by short‐term memory for order in developmental language disorder: Findings from the HelSLI study
Bibliographic record
Abstract
BACKGROUND: The role of domain-general short-term memory (STM) in language development remains controversial. A previous finding from the HelSLI study on children with developmental language disorder (DLD) suggested that not only verbal but also non-verbal STM for temporal order is related to language acquisition in monolingual children with DLD. AIMS: To investigate if a similar relationship could be replicated in a sample of sequentially bilingual children with DLD. In addition to the effect of age, the effect of cumulative second language (L2) exposure was studied. METHODS & PROCEDURES: Sixty-one 4-6-year-old bilingual children with DLD and 63 typically developing (TD) bilingual children participated in a cross-sectional study conducted in their L2. Children completed novel game-like tests of visual and auditory non-verbal serial STM, as well as tests of cognitive functioning and language. Interactions of STM for order with age and exposure to L2 (Finnish) were explored as explanatory variables. OUTCOMES & RESULTS: First, the improvement of non-verbal serial STM with age was faster in sequentially bilingual TD children than in bilingual children with DLD. A similar effect was observed for L2 exposure. However, when both age and exposure were considered simultaneously, only age was related to the differential growth of non-verbal STM for order in the groups. Second, only in children with DLD was better non-verbal serial STM capacity related to an improvement in language scores with age and exposure. CONCLUSIONS & IMPLICATIONS: The results suggest that, as previously found in Finnish monolingual children, domain-general serial STM processing is also compromised in bilingual children with DLD. Further, similar to the monolingual findings, better non-verbal serial STM was associated with greater language improvement with age and exposure, but only in children with DLD, in the age range studied here. Thus, in clinical settings, assessing non-verbal serial STM of bilingual children could improve the detection of DLD and understanding of its non-linguistic symptoms. WHAT THIS PAPER ADDS: What is already known on the subject Both phonological and non-verbal STM have been associated with DLD in monolingual and sequentially bilingual children. Monolingual children with DLD have also shown slower non-verbal serial STM development than TD children. What this study adds to existing knowledge Sequentially bilingual TD children's non-verbal serial STM improves more between ages 4 and 7 years than that of their peers with DLD, replicating a finding for monolingual children with DLD. Better non-verbal serial STM was especially associated with early receptive language development in sequentially bilingual children with DLD. L2 exposure showed largely comparable effects with age. These results support the hypothesis that a domain-general serial STM deficit is linked to DLD. What are the potential or actual clinical implications of this work? Non-verbal assessment of STM for serial order in sequentially bilingual children with DLD could benefit the development of better tailored therapeutic interventions.
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 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".