New Research Perspectives on the Interplay Between Genes and Environment on Executive Function Development
Bibliographic record
Abstract
Executive functions (EFs) are a set of skills responsible for the cognitive control of emotional states and behavior as well as for information processing required for learning and memory. Impairments in these abilities, such as focused attention, working memory, cognitive flexibility, and self-regulation, are implicated in a variety of psychopathologies across the lifespan. EF development shows a protracted course that begins in early childhood and continues throughout adolescence and into early adulthood. Maturation of EFs is subject to environmental influences such that adversity during development can affect multiple EF-mediated processes and outcomes. In this review, we describe sensitive periods for the development of EFs and the effects of adverse environmental exposures, with consideration of the underlying neurobiological mechanisms. However, there is considerable interindividual variation in the impact of adversity, with some individuals more vulnerable and some more resilient to its effects. We explore the evidence for the genetic contribution to interindividual variation in EFs, providing an overview of classic studies, followed by the results of recent genome-wide association studies and innovative genomic methods. Finally, we review studies investigating the interdependence between early-life adversities and genetic factors on EFs. We discuss the importance of novel functional genomics approaches, multilevel analyses, and big data to elucidate the complexity of the relationships between genes, environment, and the development of EFs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".