Annual Research Review: All mothers are not created equal: neural and psychobiological perspectives on mothering and the importance of individual differences
Bibliographic record
Abstract
Quality of mothering relies on the integrity of multiple physiological and behavioral systems and on two maternal factors, one proximal and one distal, that have a great impact on how a mother mothers: postpartum depression and early experiences. To mother appropriately requires the action of systems that regulate sensation, perception, affect, reward, executive function, motor output and learning. When a mother is at risk to engage in less than optimal mothering, such as when she is depressed or has experienced adversity in childhood, the function of many or all of maternal and related systems may be affected. In this paper, we will review what is currently known about the biological basis of mothering, with attention to literature on hormones but with a particular focus on recent advances in the fields of functional neuroimaging. Instead of discussing strictly 'maternal' brain imaging studies, we instead use a systems approach to survey important findings relevant to brain systems integral to and/or strongly related to the mothering experience: (a) social behavior; (b) reward and affect; (c) executive function; and (d) maternal behavior. We find that there are many commonalities in terms of the brain regions identified across these systems and, as we would expect, all are sensitive to the influence of, or function differently in the context of, depression and adverse early experience. It is likely that the similarity and cross-talk between maternal, affect and stress systems, observed behaviorally, hormonally and in the context of brain function, allows for mood disturbance and early adverse experiences to have a significant impact on the quality of mothering and the motivation to mother.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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".