Neurobiology of secure infant attachment and attachment despite adversity: a mouse model
Bibliographic record
Abstract
Attachment to an abusive caregiver has wide phylogenetic representation, suggesting that animal models are useful in understanding the neural basis underlying this phenomenon and subsequent behavioral outcomes. We previously developed a rat model, in which we use classical conditioning to parallel learning processes evoked during secure attachment (odor-stroke, with stroke mimicking tactile stimulation from the caregiver) or attachment despite adversity (odor-shock, with shock mimicking maltreatment). Here we extend this model to mice. We conditioned infant mice (postnatal day (PN) 7-9 or 13-14) with presentations of peppermint odor and either stroking or shock. We used (14) C 2-deoxyglucose (2-DG) to assess olfactory bulb and amygdala metabolic changes following learning. PN7-9 mice learned to prefer an odor following either odor-stroke or shock conditioning, whereas odor-shock conditioning at PN13-14 resulted in aversion/fear learning. 2-DG data indicated enhanced bulbar activity in PN7-9 preference learning, whereas significant amygdala activity was present following aversion learning at PN13-14. Overall, the mouse results parallel behavioral and neural results in the rat model of attachment, and provide the foundation for the use of transgenic and knockout models to assess the impact of both genetic (biological vulnerabilities) and environmental factors (abusive) on attachment-related behaviors and behavioral development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".