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Record W2802250308 · doi:10.1002/hipo.22949

Greater caregiving risk, better infant memory performance?

2018· article· en· W2802250308 on OpenAlexaff
Anne Rifkin‐Graboi, Jeffry Quan, Jenny L. Richmond, Shaun Goh Kok Yew, Lit Wee Sim, Yap Seng Chong, Jean‐François Bureau, Helen Chen, Anqi Qiu

Bibliographic record

VenueHippocampus · 2018
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsUniversity of Ottawa
FundersNational Medical Research CouncilSingapore Institute for Clinical SciencesAgency for Science, Technology and Research
KeywordsPsychologyContext (archaeology)Developmental psychologyCognitionNeuroimagingAnxietyMaternal sensitivityClinical psychologyNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

Poor early life care often relates to cognitive difficulties. However, newer work suggests that in early-life, adversity may associate with enhanced or accelerated neurodevelopment. We examine associations between postnatal caregiving risks (i.e., higher self-reported postnatal-anxiety and lower observed maternal sensitivity) and infant relational memory (i.e., via deferred imitation and relational binding). Using subsamples of 67-181 infants (aged 433-477 post-conceptual days, or roughly five to seven months since birth) taking part in the GUSTO study, we found such postnatal caregiving risk significantly predictive of "better" performance on a relational binding task following a brief delay, after Bonferroni adjustments. Subsequent analyses suggest that the association between memory and these risks may specifically be apparent among infants spending at least 50% of their waking hours in the presence of their mothers. Our findings echo neuroimaging research concerning similar risk exposure and larger infant hippocampal volume, and likewise underscore the importance of considering developmental context in understanding early life experience. With this in mind, these findings caution against the use of cognitive outcomes as indices of experienced risk.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.004

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 head0.020
GPT teacher head0.285
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

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

Citations24
Published2018
Admission routes1
Has abstractyes

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