It takes a village: A multi-brain approach to studying multigenerational family communication
Bibliographic record
Abstract
Grandparents play a critical role in child rearing across the globe. Yet, there is a shortage of neurobiological research examining the relationship between grandparents and their grandchildren. We employ multi-brain neurocomputational models to simulate how changes in neurophysiological processes in both development and healthy aging affect multigenerational inter-brain coupling - a neural marker that has been linked to a range of socio-emotional and cognitive outcomes. The simulations suggest that grandparent-child interactions may be paired with higher inter-brain coupling than parent-child interactions, raising the possibility that the former may be more advantageous under certain conditions. Critically, this enhancement of inter-brain coupling for grandparent-child interactions is more pronounced in tri-generational interactions that also include a parent, which may speak to findings that grandparent involvement in childrearing is most beneficial if the parent is also an active household member. Together, these findings underscore that a better understanding of the neurobiological basis of cross-generational interactions is vital, and that such knowledge can be helpful in guiding interventions that consider the whole family. We advocate for a community neuroscience approach in developmental social neuroscience to capture the diversity of child-caregiver relationships in real-world settings.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 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".