The impact of environmental and social factors on learning abilities: a meta‐analysis
Bibliographic record
Abstract
Since the 1950s, researchers have examined how differences in the social and asocial environment affect learning in rats, mice, and, more recently, a variety of other species. Despite this large body of research, little has been done to synthesize these findings and to examine if social and asocial environmental factors have consistent effects on cognitive abilities, and if so, what aspects of these factors have greater or lesser impact. Here, we conducted a systematic review and meta-analysis examining how different external environmental features, including the social environment, impact learning (both speed of acquisition and performance). Using 531 mean-differences from 176 published articles across 27 species (with studies on rats and mice being most prominent) we conducted phylogenetically corrected mixed-effects models that reveal: (i) an average absolute effect size |d| = 0.55 and directional effect size d = 0.34; (ii) interventions manipulating the asocial environment result in larger effects than social interventions alone; and (iii) the length of the intervention is a significant predictor of effect size, with longer interventions resulting in larger effects. Additionally, much of the variation in effect size remained unexplained, possibly suggesting that species differ widely in how they are affected by environmental interventions due to varying ecological and evolutionary histories. Overall our results suggest that social and asocial environmental factors do significantly affect learning, but these effects are highly variable and perhaps not always as predicted. Most notably, the type (social or asocial) and length of interventions are important in determining the strength of the effect.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.038 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".