Exploring rodent prosociality: A conceptual framework
Bibliographic record
Abstract
Prosociality is a behavior characterized by actions performed for the benefit or well-being of others. Recent studies have corroborated parallels in brain activation patterns between rodents and humans during prosocial behaviors. These findings have the potential to advance our understanding of social impairments observed in neurodevelopmental disorders, brain injuries, neurological conditions, and mental health disorders. However, a consensus regarding prosocial paradigms in rodents remains scattered. This conceptual framework aims to (1) reframe prosociality as a set of complex behaviors emerging in response to environmental determinants that cannot be reduced to a single set of data; (2) highlight important methodological considerations, mediating variables, and behavioral analyses that influence prosocial behaviors; and (3) present a decision tree as a dynamic element within this conceptual framework to offer guidance to researchers. The conceptual framework and decision tree are concise and straightforward, providing a robust foundation for the ongoing utilization of current models and the creation of novel paradigms. The integration of this conceptual framework into research practices will contribute to the advancement of knowledge in the field of rodent prosociality and foster greater confidence in the validity and reproducibility of study findings.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".