Parental buffering of fear and stress neurobiology: Reviewing parallels across rodent, monkey, and human models
Bibliographic record
Abstract
It has been long recognized that parents exert profound influences on child development. Dating back to at least the seventeenth-century Enlightenment, the ability for parents to shape child behavior in an enduring way has been noted. Twentieth-century scholars developed theories to explain how parenting histories influence psychological development, and since that time, the number of scientific publications on parenting influences in both human and nonhuman animal fields has grown at an exponential rate, reaching numbers in the thousands by 2015. This special issue describes a symposium delivered by Megan Gunnar, Regina Sullivan, Mar Sanchez, and Nim Tottenham in the Fall of 2014 at the Society for Social Neuroscience. The goal of the symposium was to describe the emerging knowledge on neurobiological mechanisms that mediate parent-offspring interactions across three different species: rodent, monkey, and human. The talks were aimed at designing testable models of parenting effects on the development of emotional and stress regulation. Specifically, the symposium aimed at characterizing the special modulatory (buffering) effects of parental cues on fear- and stress-relevant neurobiology and behaviors of the offspring and to discuss examples of impaired buffering when the parent-infant relationship is disrupted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".