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Record W6958819629 · doi:10.6084/m9.figshare.c.6017062

Supplementary material from "Evaluating the neuropeptide-social cognition link in ageing: the mediating role of basic cognitive skills"

2022· other· en· W6958819629 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCognitionSocial cognitionAssociation (psychology)Identification (biology)OxytocinEmotion recognition

Abstract

fetched live from OpenAlex

The roles of oxytocin (OT) and arginine-vasopressin (AVP) as crucial modulators of social cognition and related behaviours have been extensively addressed in the literature. The involvement of these neuropeptides in social cognition in ageing, however, and a potential mediating effect of basic cognitive capacities on this link, are not well understood. To fill these research gaps, this study assessed associations of plasma OT and AVP levels with dynamic emotion identification accuracy in generally healthy older men (aged 55–95 years) and probed the underlying roles of crystallized and fluid cognition in these associations. Higher plasma OT levels were associated with lower accuracy in dynamic emotion identification, with this negative relationship fully mediated by cognition. For plasma AVP levels, in contrast, there was no association with dynamic emotion identification accuracy. Integrated within existing theoretical accounts, results from this study advance understanding of the neuropeptide-social cognition link in ageing and support basic cognitive capacities as mediators in this association.This article is part of the theme issue ‘Interplays between oxytocin and other neuromodulators in shaping complex social behaviours’.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.8890.002

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 head0.042
GPT teacher head0.309
Teacher spread0.266 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2022
Admission routes1
Has abstractyes

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