The Directionality of the Relationship Between Executive Functions and Language Skills: A Literature Review
Bibliographic record
Abstract
It has been demonstrated that executive functions play a significant role in different aspects of the development of children. Development of language is also one of the most important accomplishments of the preschool years, and it has been linked to many outcomes in life. Despite substantial research demonstrating the association between executive function and language development in childhood, only a handful of studies have examined the direction of the developmental pathways between EF skills and language skills, therefore little is known about how these two constructs are connected. In this review paper, we discuss three possible directional relationships between EFs and language development throughout childhood. First, we discuss how EF might affect language functioning. Next, we discuss how language functioning might affect EF. Lastly, we consider other possible relationships between EF and language. Given that children with better EF and language skills are more likely to succeed in educational settings and demonstrate greater social-emotional competencies, investigating the relationship between EF and language in the preschool period provides insight into mechanisms that have not been extensively studied. Furthermore, it could create new opportunities for designing effective and efficient interventions aimed at addressing EF and language deficits during the preschool period which could in turn influence later development.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".