Siblings matter: Family heterogeneity improves associative learning later in life
Bibliographic record
Abstract
Abstract Despite the strong interest in connecting social complexity and cognitive ability, there remains considerable debate about how to best quantify both cognitive performance and social complexity. Measuring group and brain size are clearly not sufficient and recent attention has been placed on the use of rigorous, increasingly challenging cognitive tasks and studying the quality, not merely the number of social interactions. Here we used two cichlid fishes from Lake Tanganyika, one cooperative breeder and one biparental species, in a cross‐fostering experiment, to investigate the links between social complexity and cognition. While controlling for parental cues, individual fish grew up either in a socially homogenous group with only conspecifics or in a mixed and diverse social group with hetero‐ as well as conspecifics and then were tested for learning abilities as subadults. To quantify differences in learning, we first employed a discrimination learning task followed by a reversal learning task that requires behavioral flexibility, as previous associations are forgotten and new associations forged. We found that individuals growing up in a more diverse social environment learned faster and made fewer mistakes in the discrimination learning task, but this ability did not transfer to the reversal learning task. Irrespective of the early social experiences, the cooperatively breeding, and thus the more social of the two cichlid species, learnt the color discrimination more quickly and made significantly fewer errors. These results provide a first demonstration of a possible association between cognitive performance and social complexity in cichlid fishes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".