Dynamic relationships between children’s higher‐order regulation and lower‐order reactivity predict development of attention problems
Bibliographic record
Abstract
Abstract Dual‐process theories contend that interplay between higher‐order (i.e., regulatory) and lower‐order (i.e., reactive) systems influences the development of attention in early childhood. We therefore investigated interactions between an aspect of children's top‐down self‐regulation (i.e., effortful control; EC) and positive reactivity (indexed by observed positive affectivity; PA) and negative reactivity (indicated by cortisol stress reactivity and observed fear) in predicting children's early attention problems. We found that observed EC at the age of three predicted lower attention problems 2 years later, controlling for attention problems at baseline. Importantly, the predictive effect of EC was more pronounced for children higher in cortisol stress reactivity at the age of three; this pattern was not found for observed PA or fear. Findings align with dual‐process developmental theories that emphasize the dynamics between regulatory and reactive processes in shaping child development. Our study provides the first evidence supporting dual‐process interactions in the domain of attention problems and has implications for identifying early risk markers and informing early prevention programs for children at greater risk for attention problems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".