Early maternal care predicts reliance on social learning about food in adult rats
Bibliographic record
Abstract
Many vertebrates rely extensively on social information, but the value of information produced by other individuals will vary across contexts and habitats. Social learning may thus be optimized by the use of developmental or current cues to determine its likely value. Here, we show that a developmental cue, early maternal care, correlates with social learning propensities in adult rodents. The maternal behavior of rats Rattus norvegicus with their litters was scored over the first 6 days postpartum. Rat dams show consistent individual differences in the rate they lick and groom (LG) pups, allowing them to be categorized as high, low, or mid-LG mothers. The 100-day old male offspring of high and low-LG mothers were given the opportunity to learn food preferences for novel diets from conspecifics that had previously eaten these diets ("demonstrators"). Offspring of high-LG mothers socially learned food preferences, but offspring of low-LG mothers did not. We administered oxytocin to subjects to address the hypothesis that it would increase the propensity for social learning, but there were no detectable effects. Our data raise the possibility that social learning propensities may be both relatively stable throughout life and part of a suite of traits "adaptively programmed" by early developmental experiences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".