Are facet-specific task trainings efficient in improving children’s executive functions and why (they might not be)? A multi-facet latent change score approach
Bibliographic record
Abstract
It currently remains unclear how facet-specific trainings of three core modules of executive function (EF; updating, switching, and inhibition) directly compare regarding efficacy, whether improvements on trained tasks transfer to nontrained EF tasks, and which factors predict children's improvements. The current study systematically investigated three separate EF trainings in 6- to 11-year-old children (N = 229) using EF-specific trainings that were similar in structure, design, and intensity. Children participated in pre- and posttest assessments of the three EFs and were randomly allocated to one of three EF trainings or to an active or passive control group. Multivariate latent change score models revealed that only the updating group showed training-specific improvements in task performance that were larger compared with active controls as well as passive controls. In contrast, there were no training-specific benefits of training switching or inhibition. Latent changes in the three EF tasks were largely independent, and there was no evidence of transfer effects to nontrained EF tasks. Lower baseline performance and older age predicted larger changes in EF performance. These seemingly opposing effects support compensation accounts as well as developmental theories of EF, and they highlight the importance of simultaneously accounting for multiple predictors within one model. In line with recent theoretical proposals of EF development, we provide new systematic evidence that questions whether modular task trainings represent an efficient approach to improve performance in narrow or in broader indicators of EF. Thereby, this evidence ultimately highlights the need for more comprehensive assessments of EF and, subsequently, the development of new training approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".