Cognition varies across the calendar year in multiple large-scale datasets
Bibliographic record
Abstract
Children’s cognitive abilities vary across short and long timescales, from circadian fluctuations to year-by-year developmental changes. “Summer slide” refers to seasonal variation in academic performance, characterized by decreased performance following an extended school vacation. However, it remains unclear whether this effect generalizes across varied assessments and the extent of this effect across sociodemographic groups has been debated. Though seasonal variation in cognitive performance is often attributed to “forgetting” of learned material, no large-scale studies have systematically investigated seasonal variation in general cognitive skills (e.g., executive functioning) on standardized laboratory-based assessments in school-age youth compared to adults. Across four datasets with geographic, demographic, mental health, and measurement variation (total n = 23,251), we quantify cyclical, seasonal variation in children’s cognitive performance using generalized additive models with cyclic cubic splines. In school-age children but not young adults, we found consistent cognitive performance minima following school vacation (July–September in the United States; November–January in Singapore) across nearly all cognitive domains investigated. These results demonstrate a generalizable small-magnitude effect of lower cognitive performance aligning with school vacation even after adjusting for socioeconomic status or ADHD diagnosis. We contextualize summer cognitive minima relative to environmental and developmental effects, noting that the largest effect size is still seven times smaller than effects of the socioeconomic environment on cognition, and provide recommendations for future data collection and analysis of behavior across the calendar year.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".