Language and Executive Functioning: Children’s Benefit from Induced Verbal Strategies in Different Tasks
Bibliographic record
Abstract
The interplay of language and cognition in children’s development has been subject to research for a long time.The present study followed up on recently reported deleterious effects of articulatory suppression on children’sexecutive functioning (Fatzer & Roebers, 2012), aiming to provide more empirical evidence on the differentialinfluence of language on executive functioning. In the present study, verbal strategies were induced in threeexecutive functioning tasks. The tasks were linked to the three central executive functioning dimensions ofupdating (Complex Span task), shifting (Cognitive Flexibility task) and inhibition (Flanker task). It was expectedthat the effects of the verbal strategy instruction would counter the results of articulatory suppression and thus bestrong in the Complex Span task, weak but present in the Cognitive Flexibility task and small or nonexistent inthe Flanker task. N = 117 children participated in the study, with n = 39 four-year-olds, n = 38 six-year-olds, andn = 40 nine-year-olds. As expected, results revealed a benefit from induced verbal strategies in the ComplexSpan and the Cognitive Flexibility task, but not in the Flanker task. The positive effect of strategy instructiondeclined with increasing age, pointing to more frequent spontaneous and self-initiated use of verbal strategiesover the course of development. The effect of strategy instruction in the Cognitive Flexibility task wasunexpectedly strong in the light of the only small detrimental effect of articulatory suppression in the precedingstudy. Implications for language’s involvement in the different executive functioning dimensions and for practiceare discussed.
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 | 0.003 |
| 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 | 0.001 |
| 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".