Measuring executive function in Indian mothers and their 4‐year‐old daughters
Bibliographic record
Abstract
Executive function (EF), including cognitive flexibility, attention shifting, and inhibitory control, has been linked to a range of outcomes across the lifespan, such as school readiness and academic functioning, job performance, health, and social-emotional well-being. Yet, research investigating links between parent EF and child EF is still limited. This is partly due to challenges in measuring the same EF abilities in parents and their children. The current study investigated the applicability of a computer-based battery of various EF tasks for use with both mothers and children. The battery included the following EF tasks: Dimensional Change Card Sort, Hearts and Flowers, and Fish Flanker. Participants were 80 Indian mothers and their 4-year-old daughters. EF was measured with regard to accuracy scores, response time, and inverse efficiency (IE) scores of the most complex blocks of each task. Scoring patterns indicated that children's task performance appeared to be determined by their ability to recognize the cue indicating which task to perform at any given trial and to inhibit an incorrect response. In contrast, mothers' performance appeared to be determined by response time, that is, their ability to be quick in giving the correct response. However, for both children and mothers, IE scores best captured individual differences in EF performance between participants. Furthermore, confirmatory factor analyses found that, for both children and mothers, all EF measures loaded on a latent factor, suggesting that the measures shared common variance in EF. There appeared to be no significant association between mothers' and children's EF scores, controlling for several background variables. Directions for further research include examining the applicability of the EF task battery to reliably describe developmental trajectories of EF abilities over time, and further examining variability in the parent-child EF association across the lifespan.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".