Non‐linguistic cognitive measures as predictors of functionally defined developmental language disorder in monolingual and bilingual children
Bibliographic record
Abstract
BACKGROUND & AIMS: Given that standardized language measures alone are inadequate for identifying functionally defined developmental language disorder (fDLD), this study investigated whether non-linguistic cognitive abilities (procedural learning, motor functions, executive attention, processing speed) can increase the prediction accuracy of fDLD in children in linguistically diverse settings. METHODS & PROCEDURES: We examined non-linguistic cognitive abilities in mono- and bilingual school-aged children (ages 8-12) with and without fDLD. Typically developing (TD) children (14 monolinguals, 12 bilinguals) and children with fDLD (28 monolinguals, 12 bilinguals) completed tasks measuring motor functions, procedural learning, executive attention and processing speed. Children were assigned as fDLD based on parental or professional concerns regarding children's daily language functioning. If no concerns were present, children were assigned as TD. Standardized English scores, non-verbal IQ scores and years of maternal education were also obtained. Likelihood ratios were used to examine how well each measure separated the fDLD versus TD groups. A binary logistic regression was used to test whether combined measures enhanced the prediction of identifying fDLD status. OUTCOMES & RESULTS: A combination of linguistic and non-linguistic measures provided the best distinction between fDLD and TD for both mono- and bilingual groups. For monolingual children, the combined measures include English language scores, functional motor abilities and processing speed, whereas for bilinguals, the combined measures include English language scores and procedural learning. CONCLUSIONS & IMPLICATIONS: A combination of non-linguistic and linguistic measures significantly improved the distinction between fDLD and TD for both mono- and bilingual groups. This study supports the possibility of using non-linguistic cognitive measures to identify fDLD in linguistically diverse settings. WHAT THIS PAPER ADDS: What is already known on the subject Given that standardized English language measures may fail to identify functional language disorder, we examined whether supplementing English language measures with non-linguistic cognitive tasks could resolve the problem. Our study is based on the hypothesis that non-linguistic cognitive abilities contribute to language processing and learning. This is further supported by previous findings that children with language disorder exhibit non-linguistic cognitive deficits. What this paper adds to existing knowledge The results indicated that a combination of linguistic and non-linguistic cognitive abilities increased the prediction of functional language disorder in both mono- and bilingual children. What are the potential or actual clinical implications of this work? This study supports the possibility of using non-linguistic cognitive measures to identify the risk of language disorder in linguistically diverse settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".