Getting the right grasp on executive function
Bibliographic record
Abstract
Executive Function (EF) refers to important socio-emotional and cognitive skills that are known to be highly correlated with both academic and life success. EF is a blanket term that is considered to include self-regulation, working memory, and planning. Recent studies have shown a relationship between EF and motor control. The emergence of motor control coincides with that of EF, hence understanding the relationship between these two domains could have significant implications for early detection and remediation of later EF deficits. The purpose of the current study was to investigate this relationship in young children. This study incorporated the Behavioral Rating Inventory of Executive Function (BRIEF) and two motor assessments with a focus on precision grasping to test this hypothesis. The BRIEF is comprised of two indices of EF: (1) the Behavioral Regulation Index (BRI) containing three subscales: Inhibit, Shift, and Emotional Control; (2) the Metacognition Index (MI) containing five subscales: Initiate, Working Memory, Plan/Organize, Organization of Materials, and Monitor. A global executive composite (GEC) is derived from the two indices. In this study, right-handed children aged 5-6 and 9-10 were asked to: grasp-to-construct (Lego® models); and grasp-to-place (wooden blocks), while their parents completed the BRIEF questionnaire. Analysis of results indicated significant correlations between the strength of right hand preference for grasping and numerous elements of the BRIEF including the BRI, MI, and GEC. Specifically, the more the right hand was used for grasping the better the EF ratings. In addition, patterns of space-use correlated with the GEC in several subscales of the BRIEF. Finally and remarkably, the results also showed a reciprocal relationship between hand and space use for grasping and EF. These findings are discussed with respect to: (1) the developmental overlap of motor and executive functions; (2) detection of EF deficits through tasks that measure lateralization of hand and space use; and (3) the possibility of using motor interventions to remediate EF deficits.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".