Behavioral Tasks for Examining Identity Recognition In Mice
Bibliographic record
Abstract
Social animals, like rodents, are able to recognize and differentiate between the identity of familiar individuals. Recognizing the identity of familiar individuals is important for developing social structures such as hierarchy, kinship, and family. However, mechanisms underlying the recognition of social identity remain unclear. Most rodent studies of social recognition are based on the propensity of rodents to interact with a novel social target, a phenomenon known as social novelty. However, behavioral tasks for examining social novelty cannot reveal the recognition of familiar conspecifics based on their identities. Presented here are behavioral tasks allowing for the examination of identity recognition in C57BL/6 mice by associating two familiar mice with or without a valenced experience. Subjects had interactions with two mice either without (neutral) or with a valenced experience (negative or positive) and became familiar with these mice. The negatively valenced mouse was associated with shocks, while the positively valenced mouse was associated with a food reward. Following training, the recognition of the identity of these familiar mice can be revealed in a social discrimination test, which is represented as the preference for the positively valenced mouse and avoidance of the negatively valenced mouse compared to the neutral mouse. Behavioral tasks for identity recognition could be useful in probing social memory mechanisms and the pathophysiology of disorders with impaired social cognition, such as autism spectrum disorder or schizophrenia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".