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Attachment insecurity and the biological embedding of reproductive strategies: Investigating the role of cellular aging

2022· article· en· W4306804084 on OpenAlexaff
Emma Bolhuis, Jay Belsky, Willem E. Frankenhuis, Idan Shalev, Waylon J. Hastings, Marieke S. Tollenaar, Kieran J. O’Donnell, Megan McGill, Irina Pokhvisneva, David Lin, Julia L. MacIsaac, Michael S. Kobor, Carolina de Weerth, Roseriet Beijers

Bibliographic record

VenueBiological Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaMcGill UniversityDouglas Mental Health University Institute
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsDevelopmental psychologyPsychologyAggressionStructural equation modelingLife history theoryLife historyBiologyEcology

Abstract

fetched live from OpenAlex

Evolutionary-developmental psychologists have posited that individuals who grow up in stressful rearing circumstances follow faster life history strategies, thereby increasing their chances of reproduction. This preregistered study tested this stress-acceleration hypothesis in a low-risk longitudinal sample of 193 Dutch mother-child dyads, by investigating whether infant-mother attachment insecurity at 12 months of age predicted earlier pubertal onset and more callous-unemotional traits, aggression and risk-taking about a decade later. Also evaluated were the possible mediating roles of two biomarkers of accelerated aging (i.e., telomere length, epigenetic aging) at age 6. Structural equation modelling revealed no effects of attachment insecurity on biomarkers, pubertal timing or behavior. These null findings suggest that the explanatory value of evolutionary-developmental thinking might be restricted to high-risk samples, though unexplored variation in susceptibility to environmental influences might also explain the null 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.084
GPT teacher head0.384
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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