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Record W3197790657 · doi:10.1037/pag0000633

Age differences in intuitive moral decision-making: Associations with inter-network neural connectivity.

2021· article· en· W3197790657 on OpenAlexafffund
Shenyang Huang, Leonard Faul, Günes Sevinc, Laetitia Mwilambwe-Tshilobo, Roni Setton, Amber W. Lockrow, Natalie C. Ebner, Gary R. Turner, R. Nathan Spreng, Felipe De Brigard

Bibliographic record

VenuePsychology and Aging · 2021
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsYork UniversityMcGill University
FundersNational Center for Research ResourcesCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of Health
KeywordsPsychologyMoralityFunctional magnetic resonance imagingPsycINFOSocial psychologyMoral reasoningContrast (vision)Default mode networkNeural correlates of consciousnessCognitive psychologyCognitionNeuroscienceMEDLINEEpistemology

Abstract

fetched live from OpenAlex

. By contrast, there was no difference between YAs and OAs in utilitarian decisions (i.e., decisions aimed at maximizing collective well-being) when the utilitarian choice was intuitive. Enhanced connections between the posterior medial core of the default network (pmDN) and the dorsal attention network, and overall reduced segregation of pmDN from the rest of the brain, were associated with this increased deontological-intuitive moral decision-making style in OAs. The present study contributes to our understanding of age differences in decision-making styles by taking into account the intuitiveness of the moral choice, and it offers further insights as to how age differences in intrinsic brain connectivity relate to these distinct moral decision-making styles in YAs and OAs. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.094
GPT teacher head0.334
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2021
Admission routes2
Has abstractyes

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